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Accented characters, such as the letter “e with accent marks”, are essential for effective communication in many languages. 

These diacritical marks modify vowels, significantly influencing pronunciation and conveying meaning. 

In this comprehensive guide, we will explore different methods, shortcuts, and keyboard options available on Android and iPhone devices to help you master typing these accented characters.

Related: How to Type E with Accents on a Computer Keyboard

Whether you’re communicating in French, Spanish, or any other language that utilizes accents on “e,” this guide will equip you with the knowledge and skills to effortlessly incorporate e with accent marks into your written text (on both iPhone and Android mobile phones.)

Before diving into the practical aspects of typing e with accents on phone, it’s important to understand their function. 

Accented “e” characters, such as e with acute (é), e with grave (è), e with circumflex (ê), or e with diaeresis/umlaut (ë), alter the pronunciation of the letter “e” in various languages. 

These accents often indicate a different sound or meaning. By grasping the functionality of these special characters, you can accurately convey your intended message in written communication.

Both Android and iPhone devices offer a range of default keyboard options, each with its own approach to accessing “e with accent” characters.

Understanding these options will help you type these accents effortlessly. Let’s explore the best methods.

One convenient method to access accented “e” characters on Android devices is by utilizing long-press gestures. 

Simply press and hold on the “e” key on your Android keyboard, and a popup menu will appear, presenting you with various accented versions of the letter “e,” including é, è, ê, and ë. 

Select the desired accented “e” character from the menu to insert it into your text. 

This intuitive method allows for quick access to any “e with an accent” while typing on Android devices.

Similar to Android devices, iPhones offer various default keyboard options to facilitate typing “e with accent” letters. 

To access these “e with accent marks” on an iPhone, long-press the “e” key on your iPhone keyboard. A popup menu will appear, displaying accented versions of the letter “e,” including é, è, ê, and ë. Slide your finger to select the desired accented “e” letter, and it will be inserted into your text. 

This method ensures quick and intuitive typing of accented “e” characters on iPhone devices.

To expedite your typing of accented “e” characters, both Android and iPhone devices offer shortcuts and special characters. 

These shortcuts allow you to quickly insert the accented “e” characters without the need for additional menus or gestures. 

Let’s take a look at some examples:

On Android devices, you can utilize the following shortcuts:

To type “é”: Long-press the “e” key and select the acute accent option.

To type “è”: Long-press the “e” key and select the grave accent option.

To type “ê”: Long-press the “e” key and select the circumflex accent option.

To type “ë”: Long-press the “e” key and select the diaeresis accent option.

On iPhone devices, you can use the following shortcuts:

To type “é”: Long-press the “e” key and swipe upwards on the popup menu to select the acute accent option.

To type “è”: Long-press the “e” key and swipe upwards on the popup menu to select the grave accent option.

To type “ê”: Long-press the “e” key and swipe upwards on the popup menu to select the circumflex accent option.

To type “ë”: Long-press the “e” key and swipe upwards on the popup menu to select the diaeresis accent option.

These shortcuts can significantly speed up your typing process once you become familiar with them.

Text replacements, also known as keyboard shortcuts, allow you to create custom abbreviations that automatically expand into other characters, phrases, or sentences when typed. 

These shortcuts can save you time and effort, especially when frequently using special characters like “e with accent marks”. 

Here’s how to set up text replacements on your iPhone:

Open the Settings app on your device.

Scroll down and tap on General.

Select Keyboard and then choose Text Replacement.

Tap on the “+” button to create a new text replacement.

In the Phrase field, enter the accented “e” character you want to assign to the shortcut. You can copy and paste the character from a reliable source or website.

In the Shortcut field, input the abbreviation or shortcut that triggers the expansion. For example, you can use “ea” for é, “ee” for è, “ei” for ê, and “ee” for ë.

Tap Save to confirm

Now, whenever you type the designated shortcut on your iPhone keyboard, it will automatically expand into the corresponding accented “e” character.

If you on Android, you can also add a keyboard text shortcut on your Android device to help you type e with accent marks. 

Obey the following instructions for guidance:

Step 1 – Open Keyboard Settings: Unlock your Android device and go to the home screen. Locate the “Settings” app, which is typically represented by a gear icon, and tap on it to open the Settings menu.

Step 2 – Access Language & Input Settings: In the Settings menu, scroll down until you find the “System” section. Tap on “System” to expand the options, and then locate and tap on “Language & input.” This will open the Language & Input settings page.

Step 3 – Select Your Keyboard: Under the Language & Input settings, you’ll see a list of keyboards installed on your device. Tap on the keyboard you are currently using to access its settings. For example, if you’re using the Gboard keyboard, tap on “Gboard” in the list, and if you are using a Samsung keyboard, tap Samsung in the list.

Step 4 – Open Text Correction Settings: Inside the keyboard settings, you’ll find various options related to keyboard behavior and input. Look for an option called “Text correction” or “Text correction settings” or “Text Shortcuts” and tap on it to proceed.

Step 5 – Locate Personal Dictionary: In the Text Correction settings, you’ll find different options for customizing your keyboard’s behavior. Look for an option called “Personal dictionary” or “Personal dictionary settings” and tap on it to access the personal dictionary settings.

Step 7 – Enter the Shortcut: In the “Shortcut” field, enter the shortcut or abbreviation you want to use to trigger the accented “e” character. For example, you can use “ea” for the acute accent é, “eg” for the grave accent è, “ec” for the circumflex accent ê, or “ed” for the diaeresis accent ë.

Step 8 – Enter the Phrase: In the “Phrase” field, enter the accented “e” character corresponding to the shortcut you’ve entered. You can copy the desired accented “e” character from a reliable source or website and paste it into this field.

Step 9 – Save the Shortcut: Once you’ve entered the shortcut and its corresponding accented “e” character, tap on the “Save” or “OK” button to save the new text shortcut.

Step 10 – Test the Shortcut: To ensure that your new keyboard text shortcut works correctly, open any app that allows text input, such as a messaging app or a text editor. Tap on a text field to bring up the keyboard, and then type the shortcut you created for the accented “e” character. The corresponding character should appear in the text field.

By following these step-by-step instructions, you can easily add a keyboard text shortcut on your Android device for typing accented “e” characters.

Mastering the art of typing accented “e” characters on Android and iPhone devices enhances your ability to communicate effectively in multiple languages. 

By understanding the functionality of accented “e” characters and familiarizing yourself with the different methods, shortcuts, and keyboard options available, you can effortlessly incorporate these accents into your written text. 

Throughout this comprehensive guide, we have explored the importance of accurately typing accented “e” characters and the impact they have on pronunciation and conveying meaning. 

With this knowledge and practice, you’ll be able to communicate with ease and confidence. 

Thank you for reading this guide.

You're reading Typing E With Accent Marks On Iphone And Android: A Comprehensive Guide

A Comprehensive Guide To Reinforcement Learning

Everyone heard when DeepMind announced its milestone project AlphaGo –

AlphaGo is the first computer program to defeat a professional human Go player, the first to defeat a Go world champion, and is arguably the strongest Go player in history.

This alone says a lot about how powerful the program itself is but how did they achieve it? They did it through novel approaches in Reinforcement learning!

And it’s not just fixated on games, the applications range from –

In this guide, I’ll walk you through the theory behind reinforcement learning, ideas based on theory, various algorithms with basic concepts, and implementation in Python!

Table of Contents

Fundamentals of Reinforcement learning

Creating an environment using OpenAI Gym

Algorithms (Concepts and Implementation)

RL – Libraries in Python

Challenges in Reinforcement Learning


Fundamentals of Reinforcement Learning

Let’s dig into the fundamentals of RL and review them step by step.

Key elements fundamental to RL

There are basically 4 elements – Agent, Environment, State-Action, Reward


An agent is a program that learns to make decisions. We can say that an agent is a learner in the RL setting. For instance, a badminton player can be considered an agent since the player learns to make the finest shots with timing to win the game. Similarly, a player in FPS games is an agent as he takes the best actions to improve his score on the leaderboard.


For instance, we discussed badminton players, here the court is the environment in which the player moves and takes appropriate shots. Same in the case of the FPS game, we have a map with all the essentials (guns, other players, ground, buildings) which is our environment to act for an agent.

State – Action

A state is a moment or instance in the environment at any point. Let’s understand it with the help of chess. There are 64 places with 2 sides and different pieces to move. Now this chessboard will be our environment and player, our agent. At some point after the start of the game, pieces will occupy different places in the board, and with every move, the board will differ from its previous situation. This instance of the board is called a state(denoted by s). Any move will change the state to a different one and the act of moving pieces is called action (denoted by a).


We have seen how taking actions change the state of the environment. For each action ‘a’ the agent takes, it receives a reward (feedback). The reward is simply a numerical value assigned which could be negative or positive with different magnitude.

Let’s take badminton example if the agent takes the shot which results in a positive score we can assign a reward as +10. But if it gets the shuttle inside his court then it will get a negative reward -10. We can further break rewards by giving small positive rewards(+2) for increasing the chances of a positive score and vice versa.

Rough Idea to relate Reinforcement Learning problems

Before we move on to the Math essentials, I’d like to give a bird-eye view of the reinforcement learning problem. Let’s take the analogy of training a pet to do few tricks. For every successful completion of the trick, we give our pet a treat. If the pet fails to do the same trick we don’t give him a treat. So, our pet will figure out what action caused it to receive a cookie and repeat that action. Thus, our pet will understand that completing a trick caused it to receive a treat and will attempt to repeat doing the tricks. Thus, in this way, our pet will learn a trick successfully while aiming to maximize the treats it can receive.

Here the pet was Agent, groundfloor our environment which includes our pet. Treats given were rewards and every action pet took landed him in a different state than the previous.

Markov Decision Process (MDP)

The Markov Decision Process (MDP) provides a mathematical framework for solving RL problems. Almost all RL problems can be modeled as an MDP. MDPs are widely used for solving various optimization problems. But to understand what MDP is, we’d have to understand Markov property and Markov Chain.

The Markov property and Markov chain

Markov Property is simply put – says that future states will not depend on the past and will solely depend on the present state. The sequence of these states (obey Markov property) is called Markov Chain.

Change from one state to another is called transition and the probability of it is transition probability. In simpler words, it means in every state we can have different choices(actions) to choose from. Each choice(action) will result in a different state and the probability of reaching the next state(s’) will be stored in our sequence.

Now, if we add rewards in Markov Chains we get a sequence with the state, transition probability, and rewards (The Markov Reward Process). If we further extend this to include actions it will become The Markov Decision Process. So, MDP is just a sequence of . We will learn more concepts on the go as we move further.

OpenAI Gym for Training Reinforcement Learning Agents

OpenAI is an AI research and deployment company whose goal is to ensure that artificial general intelligence benefits all of humanity. OpenAI provides a toolkit for training RL agents called Gym.

As we have learned that, to create an RL model we need to create an environment first. The gym comes into play here and helps us to create abstract environments to train our agents on it.

Installing Gym

Overview of Gym

Creating an episode in the Gym environment

Cart-Pole balancing with a random agent

Installing Gym

Its installation is simple using Pip. Though the latest version of Gym was just updated a few days ago after years, we can still use the 0.17 version.

pip install gym

You can also clone it from the repository.

Creating our first environment using Gym

We will use pre-built (in Gym) examples. One can get explore all the agents from OpenAI gym documentation. Let’s start with Mountain Car.

First, we import Gym

import gym

To create an environment we use the ‘make’ function which required one parameter ID (pre-built ones can be found in the documentation)

env = gym.make('CartPole-v0')

To can see how our environment actually looks like using render function.


The goal here is to balance the pole as long as possible by moving the cart left or right.

To close rendered environment, simply use

env.close() Cartpole-Balancing using Random Agent import gym env = gym.make('CartPole-v0') env.reset() for _ in range(1000): env.render() env.step(env.action_space.sample()) # take a random action env.close()

We created an environment, the first thing we do is to reset our environment to its default values. Then we ran it for 1000 timesteps by taking random actions. The ‘step’ function is basically transitioning our current state to the next state by taking the action our agent gives (in this case it was random).


If we want to do better than just taking random actions, we’d have to understand what our actions are doing to the environment.

The environment’s step function returns what we need in the form of 4 values :

observation (object): an environment-specific object representing the observation of our environment. For example, state of the board in a chess game, pixels as data from cameras or joints torque in robotic arms.

reward (float): the amount of reward achieved by each action taken. It varies from env to env but the end goal is always to maximize our total reward.

done (boolean): if it’s time to reset our environment again. Most of the tasks are divided into a defined episode (completion) and if done is true it means the env has completed the episode. For example, a player wins in chess or we lose all lives in the Mario game.

info (dict): It is simply diagnostic information that is useful for debugging. The agent does not use this for learning, although it can be used for other purposes. If we want to extract some info from each timestep or episode it can be done through this.

This is an implementation of the classic “agent-environment loop”. With each timestep, the agent chooses an action, and the environment returns an observation and a reward with info(not used for training).

The whole process starts by calling the reset() function, which returns an initial observation.

import gym env = gym.make('CartPole-v0') for i_episode in range(20): observation = chúng tôi for t in range(100): env.render() #renders our cartpole env print(observation) action = env.action_space.sample() #takes random action from action space observation, reward, done, info = env.step(action) if done: #prints number of timesteps it took to finish the episode print("Episode finished after {} timesteps".format(t+1)) break env.close()

Now, what we see here is observation at each timestep, in Cartpole env observation is a list of 4 continuous values. While our actions are just 0 or 1. To check what is observation space we can simply call this function –

import gym env = gym.make('CartPole-v0') print(env.action_space) #type and size of action space print(env.observation_space) #type and size of observation space

Discrete and box are the most common type of spaces in Gym env. Discrete as the name suggests has defined values while box consists of continuous values. Action values are as follows –

Value Action 0 Push cart towards the left 1 Push cart towards the right

Meanwhile, the observation space is a Box(4,) with 4 continuous values denoting –

0.02002610 -0.0227738 0.01257453 0.04411007 Position of Cart Velocity of Cart Angle of Pole The velocity of Pole at the tip

Gym environments are not just restricted to text or cart poles, its wide range is as follows –

Atari games Box2D MuJoCo

And many more… We can also create our own custom environment in the gym suiting to our needs.

Popular Algorithms in Reinforcement Learning

In this section, I will cover popular algorithms commonly used in Reinforcement Learning. Right after some basic concepts, it will be followed with implementation in python.

Deep Q Network

The objective of reinforcement learning is to find the optimal policy, that is, the policy that gives us the maximum return (the sum of total rewards of the episode). To compute policy we need to first compute the Q function. Once we have the Q function, then we can create a policy that selects the best action based on the maximum Q value. For instance, let’s assume we have two states A and B, we are in state A which has 4 choices, and corresponding to each choice(action) we have a Q value. In order to maximize returns, we follow the policy that has argmax (Q) for that state.

State Action Value A left 25 A Right 35 A up 12 A down 6

We are using a neural network to approximate the Q value hence that network is called the Q network, and if we use a deep neural network to approximate the Q value, then it is called a deep Q network or (DQN).

Basic elements we need for understanding DQN is –

Replay Buffer

Loss Function

Target Network

Replay Buffer –

We know that the agent makes a transition from a state s to the next state 𝑠′ by performing some action a, and then receives a reward r. We can save this transition information in a buffer called a replay buffer or experience replay. Later we sample random batches from buffer to train our agent.

We learned that in DQN, our goal is to predict the Q value, which is just a continuous value. Thus, in DQN we basically perform a regression task. We generally use the mean squared error (MSE) as the loss function for the regression task. We can also use different functions to compute the error.

Target Network –

There is one issue with our loss function, we need a target value to compute the losses but when the target is in motion we can no longer get stable values of y_i to compute loss, so here we use the concept of soft update. We create another network that updates slowly as compared to our original network and computes losses since now we have frozen values of y_i. It will be better understood with the code below.

Let’s start coding our DQN algorithm!

import random import gym import numpy as np from collections import deque from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Flatten, Conv2D, MaxPooling2D , Dense, Activation from tensorflow.keras.optimizers import Adam env = gym.make("MsPacman-v0") state_size = (88, 80, 1) #defining state size as image input pixels action_size = env.action_space.n #number of actions to be taken

Pre-processing to feed image in our CNN

color = np.array([210, 164, 74]).mean() def preprocess_state(state): #creating a function to pre-process raw image from game #cropping the image and resizing it image = state[1:176:2, ::2] #converting the image to greyscale image = image.mean(axis=2) #improving contrast image[image==color] = 0 #normalize image = (image - 128) / 128 - 1 #reshape and returning the image in format of state space image = np.expand_dims(image.reshape(88, 80, 1), axis=0) return image

We need to pre-process the raw image from the game, like removing color, cropping to the desired area, resizing it to state space as we defined previously.

Building DQN class

#epsilon of 0.8 denotes we get 20% random decision self.epsilon = 0.8 #define the update rate at which we update the target network self.update_rate = 1000 #building our main Neural Network self.main_network = self.build_network() #building our target network (same as our main network) self.target_network = self.build_network() #copying weights to target network self.target_network.set_weights(self.main_network.get_weights()) def build_network(self): #creating a neural net model = Sequential() model.add(Conv2D(32, (8, 8), strides=4, padding='same', input_shape=self.state_size)) model.add(Activation('relu')) #adding hidden layer 1 model.add(Conv2D(64, (4, 4), strides=2, padding='same')) model.add(Activation('relu')) #adding hidden layer 2 model.add(Conv2D(64, (3, 3), strides=1, padding='same')) model.add(Activation('relu')) model.add(Flatten()) #feeding flattened map into our fully connected layer model.add(Dense(512, activation='relu')) model.add(Dense(self.action_size, activation='linear')) #compiling model using MSE loss with adam optimizer return model #we sample random batches of data, to store whole transition in buffer def store_transistion( self, state, action, reward, next_state, done ): self.replay_buffer.append(( state, action, reward, next_state, done)) # defining epsilon greedy function so our agent can tackle exploration vs exploitation issue def epsilon_greedy(self, state): #whenever a random value < epsilon we take random action if random.uniform(0,1) < self.epsilon: return np.random.randint(self.action_size) #then we calculate the Q value Q_values = self.main_network.predict(state) return np.argmax(Q_values[0]) #this is our main training function def train(self, batch_size): #we sample a random batch from our replay buffer to train the agent on past actions minibatch = random.sample(self.replay_buffer, batch_size) #compute Q value using target network for state, action, reward, next_state, done in minibatch: #we calculate total expected rewards from this policy if episode is not terminated if not done: target_Q = (reward + self.gamma * np.amax(self.target_network.predict(next_state))) else: target_Q = reward #we compute the values from our main network and store it in Q_value Q_values = self.main_network.predict(state) #update the target Q value for losses Q_values[0][action] = target_Q #training main network, Q_values, epochs=1, verbose=0) #update the target network weights by copying from the main network def update_target_network(self): self.target_network.set_weights(self.main_network.get_weights()

Now we train our network after defining the values of hyper-params

num_episodes = 500 #number of episodes to train agent on num_timesteps = 20000 #number of timesteps to be taken in each episode (until done) batch_size = 8 #taking batch size as 8 num_screens = 4 #number of past game screens we want to use dqn = DQN(state_size, action_size) #initiating the DQN class done = False #setting done to false (start of episode) time_step = 0 #begining of timestep for i in range(num_episodes): #reset total returns to 0 before starting each episode Return = 0 #preprocess the raw image from game state = preprocess_state(env.reset()) for t in range(num_timesteps): env.render() #render the env time_step += 1 #increase timestep with each loop #updating target network if time_step % dqn.update_rate == 0: dqn.update_target_network() #selection of action based on epsilon-greedy strategy action = dqn.epsilon_greedy(state) #saving the output of env after taking 'action' next_state, reward, done, _ = env.step(action) #Pre-process next state next_state = preprocess_state(next_state) #storing transition to be used later via replay buffer dqn.store_transistion(state, action, reward, next_state, done) #updating current state to next state state = next_state #calculating total reward Return += reward if done: print('Episode: ',i, ',' 'Return', Return) #if episode is completed terminate the loop break #we train if the data in replay buffer is greater than batch_size #for first 1-batch_size we take random actions dqn.train(batch_size)

Results – Agent learned to play the game successfully.

DDPG (Deep Deterministic Policy Gradient)

DQN works only for discrete action space but it’s not always the case that we need discrete values. What if we want continuous action output? to overcome this situation, we start with DDPG (Timothy P. Lillicrap 2024) to deal with when both state and action space is continuous. The idea of replay buffer, target functions, loss functions will be taken from DQN but with novel techniques which I will explain in this section.

Now, we move on to the core Actor-critic method. The original paper explains this concept quite well, but here is a rough idea. The actor takes a decision based on a policy, critic evaluates state-action pair, and gives it a Q value which is assigned to each pair. If the state-action pair is good enough according to critics, it will have a higher Q value (more preferable) and vice versa.

Critic Network

#creating class for critic network class CriticNetwork(nn.Module): def __init__(self, beta): super(CriticNetwork, self).__init__() #fb, insta as state of 2 dim self.input_dims = 2 #hidden layers with 256 N self.fc1_dims = 256 #hidden layers with 256 N self.fc2_dims = 256 #fb, insta spends as 2 actions to be taken self.n_actions = 2 # state + action as fully connected layer chúng tôi = nn.Linear( 2 + 2, self.fc1_dims ) #adding hidden layers chúng tôi = nn.Linear(self.fc1_dims, self.fc2_dims) #final Q value from network self.q1 = nn.Linear(self.fc2_dims, 1) #using adam optimizer with beta as learning rate self.optimizer = optim.Adam(self.parameters(), lr=beta) #device available to train on CPU/GPU self.device = T.device('cuda' if T.cuda.is_available() else 'cpu') #assigning device #Creating Critic Network with state and action as input def CriticNetwork(self, state, action): #concatinating state and action before feeding to Neural Net q1_action_value = self.fc1([state, action], dim=1 )) q1_action_value = F.relu(q1_action_value) #adding hidden layer q1_action_value = self.fc2(q1_action_value) q1_action_value = F.relu(q1_action_value) #getting final Q value q1 = self.q1(q1_action_value) return q1

Now we move to actor-network, we created a similar network but here are some key points which you must remember while making the actor.

Weight initialization is not necessary but generally, if we provide initialization it tends to learn faster.

Choosing an optimizer is very very important and results can vary from the optimizer to optimizer.

Now, how to choose the last activation function solely depends on what kind of action-space, you are using, for example, if it is small and all values are like [-1,-2,-3] to [1,2,3] you can go ahead and tanh (squashing) function, but if you have [-2,-40,-230] to [2,60,560] you might want to change the activation function or create a wrapper.


class ActorNetwork(nn.Module): #creating actor Network def __init__(self, alpha): super(ActorNetwork, self).__init__() #fb and insta as 2 input state dim self.input_dims = 2 #first hidden layer dimension self.fc1_dims = fc1_dims #second fully connected layer dimension self.fc2_dims = fc2_dims #total number of actions self.n_actions = 2 #connecting fully connected layers chúng tôi = nn.Linear(self.input_dims, self.fc1_dims) chúng tôi = nn.Linear(self.fc1_dims, self.fc2_dims) #final output as number of action values we need (2) chúng tôi = nn.Linear(self.fc2_dims, self.n_actions) #using adam as optimizer self.optimizer = optim.Adam(self.parameters(), lr=alpha) #setting up device (CPU or GPU) to be used for computation self.device = T.device('cuda' if T.cuda.is_available() else "cpu") #connecting the device def forward(self, state): #taking state as input to our fully connected layer prob = self.fc1(state) #adding activation layer prob = F.relu(prob) #adding second layer prob = self.fc2(prob) prob = F.relu(prob) #fixing each output between 0 and 1 mu = T.sigmoid( return mu

Note: We used 2 hidden layers since our action space was small and our environment was not very complex. Authors of DDPG used 400 and 300 neurons for 2 hidden layers but we can increase at the cost of computation power.

Just like gym env, agent has some conditions too. We initialized our target networks with same weights as our original (A-C) networks. Since we are chasing a moving target, target networks create stability and helps original networks to train.

We initialize all the basic requirements, as you might have noticed we have a loss function parameter too. We can use different loss functions and choose whichever works best (can be L1 smooth loss), paper used mse loss, so we will go ahead and use it as default.

Here we include the ‘choose action’ function, you can create an evaluation function as well to cross-check values that outputs action space without noise.

‘Update parameter’ function, now this is where we do soft (target networks) and hard updates (original networks, complete copy). Here it takes only one parameter Tau, this is similar to how we think of learning rate.

It is used to soft update our target networks and in the paper, they found the best tau to be 0.001 and it usually is the best across different papers.

class Agent(object): #binding everything we did till now def __init__( self, alpha , beta, input_dims= 2, tau, env, gamma=0.99, n_actions=2, max_size=1000000, batch_size=64): #fixing discount rate gamma self.gamma = gamma #for soft updating target network, fix tau chúng tôi = tau #Replay buffer with max number of transitions to store self.memory = ReplayBuffer(max_size) #batch size to take from replay buffer self.batch_size = batch_size #creating actor network using learning rate alpha = ActorNetwork(alpha) #creating target network with same learning rate self.target_actor = ActorNetwork(alpha) #creating critic network with beta as learning rate self.target_critic = CriticNetwork(beta) #adjusting scale as std for adding noise self.scale = 1.0 self.noise = np.random.normal(scale=self.scale,size=(n_actions)) #hard updating target network weights to be same self.update_network_parameters(tau=1) #this function helps to retrieve actions by adding noise to output network def choose_action(self, observation): #get actor in eval mode #convert observation state to tensor for calcualtion observation = T.tensor(observation, dtype=T.float).to( #get the output from actor network mu = #add noise to our output from actor network mu_prime = mu + T.tensor(self.noise(),dtype=T.float).to( #set back to training mode #get the final results as array return mu_prime.cpu().detach().numpy() #training our actor and critic network from memory (Replay buffer) def learn(self): #if batch size is not filled then do not train if self.memory.mem_cntr < self.batch_size: return #otherwise take a batch from replay buffer state, action, reward, new_state, done= self.memory.sample_buffer(self.batch_size) #convert all values to tensors reward = T.tensor(reward, dtype=T.float).to(self.critic.device) done = T.tensor(done).to(self.critic.device) new_state = T.tensor(new_state, dtype=T.float).to(self.critic.device) action = T.tensor(action, dtype=T.float).to(self.critic.device) state = T.tensor(state, dtype=T.float).to(self.critic.device) #set netowrks to eval mode self.target_actor.eval() self.target_critic.eval() self.critic.eval() #fetch the output from the target network target_actions = self.target_actor.forward(new_state) #get the critic value from both networks critic_value_ = self.target_critic.forward(new_state, target_actions) critic_value = self.critic.forward(state, action) #now we will calculate total expected reward from this policy target = [] for j in range(self.batch_size): target.append(reward[j] + self.gamma*critic_value_[j]*done[j]) #convert it to tensor on respective device(cpu or gpu) target = T.tensor(target).to(self.critic.device) target = target.view(self.batch_size, 1) #to train critic value set it to train mode back self.critic.train() self.critic.optimizer.zero_grad() #calculate losses from expected value vs critic value critic_loss = F.mse_loss(target, critic_value) #backpropogate the values critic_loss.backward() #update the weights self.critic.optimizer.step() self.critic.eval() #fetch the output of actor network mu = #using formula from DDPG network to calculate actor loss actor_loss = -self.critic.forward(state, mu) #calculating losses actor_loss = T.mean(actor_loss) #back propogation actor_loss.backward() #update the weights #soft update the target network self.update_network_parameters() #since our target is continuously moving we need to soft update target network def update_network_parameters(self, tau=None): #if tau is not given then use default from class if tau is None: tau = self.tau #fetch the parameters actor_params = critic_params = self.critic.named_parameters() #fetch target parameters target_actor_params = self.target_actor.named_parameters() target_critic_params = self.target_critic.named_parameters() #create dictionary of params critic_state_dict = dict(critic_params) actor_state_dict = dict(actor_params) target_critic_dict = dict(target_critic_params) target_actor_dict = dict(target_actor_params) #update critic network with tau as learning rate (tau =1 means hard update) for name in critic_state_dict: critic_state_dict[name] = tau*critic_state_dict[name].clone() + (1-tau)*target_critic_dict[name].clone() self.target_critic.load_state_dict(critic_state_dict) #updating actor network with tau as learning rate for name in actor_state_dict: actor_state_dict[name] = tau*actor_state_dict[name].clone() + (1-tau)*target_actor_dict[name].clone() self.target_actor.load_state_dict(actor_state_dict)

The most crucial part is the learning function. First, we feed the network with samples until it fills up to the batch size and then start sampling from batches to update our networks. Calculate critic and actor losses and then just soft update all the parameters.

env = OurCustomEnv(sales_function, obs_range, act_range) agent = Agent(alpha= 0.000025, beta =0.00025, tau=0.001, env=env, batch_size=64, n_actions=2) score_history = [] for i in range(10000): obs = env.reset() done = False score = 0 while not done: act = agent.choose_action(obs) new_state, reward, done, info = env.step(act) agent.remember(obs, act, reward, new_state, int(done)) agent.learn() score += reward obs = new_state score_history.append(score)

Just after some training, our agent performs very well and exhausts almost complete budget.

Reinforcement Learning Libraries in Python

There are plenty of libraries offering implemented RL algorithms like –

Stable Baselines

TF Agents




We will explore a bit on Stable Baselines and how to use them through an example.


pip install stable-baselines[mpi] import gym from stable_baselines import DQN env = gym.make('MountainCar-v0') agent = DQN('MlpPolicy', env, learning_rate=1e-3) agent.learn(total_timesteps=25000)

Now we need an evaluation policy

mean_reward, n_steps = evaluate_policy(agent, agent.get_env(), n_eval_episodes=10)"DQN_mountain_car_agent") #we can save our agent in the disk agent = DQN.load("DQN_mountain_car_agent") # or load it

Training the Agent

state = env.reset() for t in range(5000): action, _ = agent.predict(state) next_state, reward, done, info = env.step(action) state = next_state env.render()

This gives us a rough idea, how to use create agents to train in our environment. Since RL is still a heavily research-oriented field, libraries updates fast. Stable baselines has the largest collection of algorithms implemented with additional features. It is suggestive to start with baselines before moving to other libraries.

Challenges in Reinforcement Learning

Reinforcement Learning is very easily prone to errors, local maxima/minima, and debugging it is hard as compared to other machine learning paradigms, it is because RL works on feedback loops and small errors propagate in the whole model. But that’s not it, we have the most crucial part which is assigning the reward function. Agent heavily depends upon the reward as it is the only thing by which it gets feedback. One of the classical problems in RL is exploration vs exploitation. Various novel methods are used to suppress this, for example, DDPG is prone to this issue so authors of TD3 and SAC (both are improvements over DDPG) used two additional networks (TD3) and temperature parameter(SAC) to deal with the exploration vs exploitation problem and many more novel approaches are being worked upon. Even from all the challenges, Deep RL has lots of applications in real life.


We learned what is reinforcement learning, how we model problems into RL. Created environments using OpenAI Gym, wrote agents from scratch, and also learned how to use already build RL libraries like stable baselines. Although it has some challenges, it still helps in major fields like Robotics, Healthcare, etc. I hope you gained some knowledge or refreshed some concepts from this guide. Thanks to Phil, Andrej Karpathy, Sudarshan for their marvelous work through books and blogs.

Reach out to me via LinkedIn (Nihal Singh)

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How To Record A Call On Iphone And Android

Being able to record a call may come in handy in a number of situations. Perhaps you’re interviewing someone over the phone or on a work call and want to ensure that you can remember everything that is being discussed. Depending on whether you’re using an iOS or Android device, you have several options available to you. This post details how to start recording your phone calls whether you’re using an iPhone or Android.

Tip: is your iPhone not ringing when you’re getting a call? We show you what to do to fix this annoying issue.

Is It Legal to Record Phone Calls?

Before we dive into the nitty-gritty of recording phone calls, we need to first talk about the legal aspect. In the U.S., different states have varying laws when it comes to recording phone calls. The same applies to European countries and beyond. You can visit this Wiki page to verify a country’s policy.

In the U.S., for example, most states require at least one party consent to the recording for it to be considered legal. However, states like California, Florida, and Washington require that all participating parties be informed of the recording.

In Europe, things are a bit more liberal. For instance, Italy considers recorded conversations legal and even allows them to be used as evidence in court (even if the other party was unaware they were being recorded), provided that the recording party is part of the conversation.

Image source:


In the U.K., on the other hand, a recording made by one party without notifying the other is not prohibited if the recording is for personal use. Recording without notification is prohibited, however, if the conversation is then made available to a third party.

Beyond what these laws actually state in your region, you should be mindful that a person’s right to privacy can be severely impacted if their calls are recorded without their express permission. As a result, we encourage you to ask for permission every time you want to record a call. If nothing else, this etiquette will help you avoid any unwanted legal issues. Let’s take a look at how you can get started recording phone calls that are made through a carrier.

How to Record a Phone Call on iPhone

Apple takes your privacy very seriously; hence, it won’t allow you to record phone calls using any native features. The company blocks recording through an iPhone’s built-in microphone if the handset is actively in a call using its own software. This means that despite your iPhone having a native recording app (Voice memos app) you won’t be able to press “Record” while you’re speaking to someone over the phone. Instead, you’ll need to resort to a few workarounds, which are detailed below.

Tip: just downloaded a file on your iPhone or iPad and can’t find it? Here’s where to look.

1. Use Another Phone to Record the Call

The easiest solution if you have another phone (even an older phone would do) laying around the house, is to use it to record the call. It doesn’t matter if it’s an iPhone or Android device. The important thing is that it has a recording app on board. Most phones, including Android devices, come with one preinstalled. If yours doesn’t have one, for some reason, you can easily download one from the App Store or Google Play Store. A few examples of these apps include:

Recorder Plus (iOS)

Rev Voice Recorder & Memos (iOS)

Hi-Q MP3 Voice Recorder (Android)

Smart Voice Recorder (Android)

Begin the Recording

Open the Voice Memos app on your recorder phone.

Start or receive a call on your other iOS device. Put the phone on “speaker.”

Once the other person starts speaking about the subject you’re interested in, make sure you press the red “Record” button in the Voice Memos app to start your recording – but not before informing the other person that you’re going to be recording them.

Make sure the phone that acts as the recorder is placed in the near vicinity of the phone involved in the call. Also, the phone you’re using for the call should have the volume turned up to the maximum (if that’s possible). This will ensure you get a better quality recording.

Once you’re done recording, press the red button once again. The file will be saved to your iPhone, and you can access it from the app. Tap on it to have it start the playback. You can also share it from there or edit it.

2. Use Google Voice

Google Voice is another alternative when it comes to recording phone calls from your iPhone. Unfortunately, the service is only officially available in the United States and Canada or for people who already have a U.S. phone number. If you meet the requirements, download the iOS app and sign up for an account.

Open the Google Voice app on your iOS device.

At the top, tap the hamburger menu and select “Settings.”

Under Calls, you will see “Incoming calls options.” Make sure the toggle next to it is on.

If it is, you’ll notice some information underneath. You’ll be instructed to press “4” to start recording calls.

Wait for the call to come in and answer it.

Once you press “4,” all participants in the call will hear an announcement alerting them that the call is being recorded.

To end the recording, press “4” once again. Participants will once again hear an announcement. By the way, hanging up the call also cancels the recording.

The resulting recordings can be found in the Voicemail tab.

Good to know: need an email solely dedicated to signing up for apps that you’re not sure you’ll be using again? Check out these disposable email services.

3. Try a Third-Party iOS App

Apple is quite restrictive when it comes to call recording apps, not allowing many in the App Store. Even so, you can still find quite a few of them. The trouble is, most of them are paid apps. Fortunately, Rev Call Recorder is an app that works for free and is fully functional.

Download the app on your phone.

Open it and verify your current phone number.

Once you’re all set, press the “Call” button at the bottom of the display.

Enter the number of the person you wish to call.

Press the “Call [number]” button, and the recording should start automatically.

Both parties involved in the call will hear an announcement saying the call is being recorded.

To end the recording, simply hang up as you would normally do.

4. Use a Dedicated Call Recorder

An interesting alternative for mobile users is using a call recorder device, such as the RecorderGear PR200. This device can wirelessly record both sides of the conversation on Bluetooth-compatible Android or iOS devices.

Tip: don’t want to be bothered by certain callers? Learn how to hide calls from specific contacts on Android.

How to Record a Phone Call on Android

Recording a phone call on Android has the potential to be easier, but only in some regions, as Google offers a native feature in this respect. For the rest of us, most of the options detailed in the iPhone section also work on Android, meaning recording the call using another phone (iOS or Android), Google Voice, and a dedicated call recorder device. Below we look at the Android-specific methods.

1. Use Google’s Own Phone App

On some Android phones, this app might already be preinstalled, but if not, you can download it from the Play Store. However, keep in mind that this feature is only available in certain regions of the world, and your carrier must also support the feature for you to use it. Also, your device needs to be on Android 9 or higher.

You can check whether you have this feature by opening the Google Phone app and tapping on the three-dot menu in the upper-right corner.

Select “Settings.”

Go to “Call Recording.” If you don’t see it, it probably means your phone does not support this feature.

You’ll now have to answer a series of questions regarding which calls will be recorded. Follow the prompts that appear on the screen and select “Always record.”

Once you’ve done this, go back to the Phone app and try calling someone.

Look at the display during the call. You should see a “Record” button among the options. Tap on it to start recording.

Once you’re done, tap the “Stop Recording” button again to stop the recording.

2. With a Third-Party Android App

Like Apple, Google doesn’t like call recording apps, but even so, some have still made it to the Play Store. Call Recorder – Cube ACR, for instance, is a free app that can help you record calls without much fuss.

Open the app on your phone and give the necessary permissions as required.

Make or receive a call.

The app will automatically start recording. You’ll see a small notification at the top of the screen that the app is active and recording.

Once you’re done, just hang up, and the recording will be terminated.

The file will be available in the app where you can play it back. You can also easily share it via social or mail apps.

Tip: unclutter your Android agenda and learn how to organize your contacts.

Frequently Asked Questions How do I know my call is being recorded?

Some of the apps and services included on this list will notify both parties that the call is being recorded automatically. That might not always be the case, though. If you hear a constantly beeping sound during a call or a loud beep at the start of the call, it could be a sign that your call is being recorded. Remember that in some countries or states, recording without informing the other party is considered illegal, so it may be a good idea to always ask for permission regardless of the method you are using to record the call.

How can I stop my calls from being recorded?

Unfortunately, there’s no technical solution that could prevent your calls from being recorded. The best you can do is to pay attention to the signs, such as strange beeps during the calls or other noises. If you feel there’s something off about the conversation, hang up immediately and try to limit interactions with the person in question going forward.

Can I use my phone’s screen recording feature to record a call?

While on an iPhone, you can screen record with the sound on, but the moment a call comes through, screen recording will automatically turn off. On the latest version of Android, while it’s possible to screen record with microphone and device sound enabled, you will only be able to record your side of the conversation. If you never recorded your screen on an Android, check out our dedicated guide on the topic.

Image credit: Freepik. All screenshots by Alexandra Arici

Alexandra Arici

Alexandra is passionate about mobile tech and can be often found fiddling with a smartphone from some obscure company. She kick-started her career in tech journalism in 2013, after working a few years as a middle-school teacher. Constantly driven by curiosity, Alexandra likes to know how things work and to share that knowledge with everyone.

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Voicemail Not Working On Android: A Troubleshooting Guide

While it might seem impossible to disconnect yourself from the real world, you shouldn’t be afraid to switch your cell phone off every once in a while. After all, messages you don’t see will be waiting for you, and any missed calls will go straight to your voicemail for you to pick up and deal with later.

That is, of course, if your voicemail service is working correctly. If your voicemail is not working on your Android device, you may need to set up or tweak your settings to get them working again. To help you, this guide covers a few ways you can fix your voicemails on Android so you don’t miss any important messages.

Table of Contents

Check Your Voicemail Settings

First, you should make sure that your voicemail settings are correct. You can check these settings for yourself using the Phone app on your device.

This app (and its settings menu) will look slightly different, depending on your device model and Android version. These settings have been written with a Samsung Galaxy S20 running Android 10 in mind, but should be similar for most other Android devices.

Open the Phone app on your phone to begin. Tap the three-dots menu icon in the top-right.

From the drop-down menu, tap the Settings option.

From here, you can double-check how your voicemail is configured. For instance, make sure that the correct network carrier is selected under the Service provider section. 

Your carrier should have a voicemail number. This is the number your device will call to hear your voicemail. Check that this is correct under the Voicemail number section.

If you’re not being notified when you receive new voicemails, check your voicemail notifications are set correctly under the Notifications section.

The voicemail settings shown here should be applied automatically when you insert a SIM card into your cell phone, but they could have become corrupted or outdated. If you’re unsure whether these settings are correct, you may need to look at one of the additional fixes below.

Request New Voicemail Settings From Your Carrier

You may believe that your voicemail settings are correct, but there can sometimes be conflicting settings on your cell phone that can cause issues with voicemail not working on your Android device. To help overcome these issues, you can request new settings from your carrier to set up your voicemail.

How you do this will depend on your carrier and location. You may also be using a visual voicemail service that allows you to view voicemail messages in a list to listen, save, or delete using a voicemail app.

This service can often cost you extra, so if you’ve been downgraded (or upgraded), you may need new settings sent to your device for it to work. The best way to manually update these settings is to visit the website for your network carrier for additional information or to contact your carrier directly.

In many cases, you may be able to receive your settings in an SMS message from your network. Many networks allow you to request these settings with a message sent from your own cell phone. Your carrier will then respond with a message containing the new settings which can then be applied to your device.

Update Your Carrier’s Voicemail App

Depending on the voicemail service you have, you may have a carrier-issued voicemail app installed on your device for you to use your voicemail service. This is especially true if you’re using a next-gen visual voicemail service.

If a carrier-issued app isn’t working, you may need to check for updates. App updates often come with new features or bug fixes that can help resolve common issues. If your voicemail app breaks, this may be the result of an issue that your carrier has since resolved.

You can check for updates to this app using the Google Play Store. If your carrier has updated the app, you may need to manually update it, as some apps with sensitive permissions require this.

Call Your Carrier Voicemail Inbox

All cell networks have a voicemail number that you can manually call to access your voicemail inbox. Calling your voicemail number manually can help you determine if your inbox is active and working correctly.

This number is always available for you to use, so if you’re struggling with a voicemail app or notifications not working on your Android, you can call your carrier’s voicemail number to check your messages manually.

For instance, you may need to follow some additional steps to switch on your voicemail. You may need to confirm a message for your voicemail before calls are accepted, or your inbox may be full, preventing any extra messages from being saved.

If you can call your voicemail number, listen to messages, and configure your settings, this would suggest that your voicemail is working and any issue with it is located on your device.

Use A Third-Party Voicemail App

While this may not work for all network carriers, it may be possible to install a third-party voicemail app. This could help you bypass any issues you have with calling your voicemail manually or with a bug-ridden carrier app.

Several third-party voicemail apps are available for you to try in the Google Play Store. Note that some of these apps may not work in your locale or with your particular voicemail service, so you’ll need to try them out first.

If you have a visual voicemail service, apps like My Visual Voicemail and Voxist can be used to set up and use it. Apps like these come with additional features, such as support for voicemail transcription, allowing you to quickly view your voice messages as text messages instead.

Contact Your Carrier For Support

If your voicemail settings still aren’t working, it could indicate an issue with the service provided by your network carrier. At this point, the best thing you can do is speak to your carrier to ensure that there isn’t a fault or issue that needs to be resolved with further technical support.

If there is a fault, your carrier can investigate and resolve the issue. They may also be able to offer additional support to set up and configure the voicemail on your Android device, if required.

Staying In Contact Using Android

If your voicemails aren’t working on Android, then the fixes above should help you solve the problem. In many cases, an update to your carrier’s voicemail app or settings can resolve the issue, but don’t forget to call your voicemail number to check if it is set up correctly.

Once you’ve set up your voicemail, you’re free to switch off when you need to. There are other ways you can stay in contact, however. There are a number of free messaging apps for Android you could use or, if you’re looking to chat face-to-face, you could set up a Zoom meeting on your smartphone instead.

Comprehensive Guide To Devops Principles

Introduction to DevOps Principles

Hadoop, Data Science, Statistics & others

It has some core key aspects and three effective ways in which they can be framed in incremental ways:

Flow-Flow of work should be from left to right and understandable as well.

Feedback- Continuous Improvement should occur with every release or a DevOps lifecycle. This can be achieved using feedback loops.

Foster- FosterDevelop an environment and try to adapt it. Generate Experimentation and Risk-taking ability. Repetition of the same activity and practice to attain the goal with grace.

Let’s walk through some in-depth DevOps Principles and Practices with real lie examples and scenarios. DevOps is not only a framework or methodology. It possesses many more facts and processes, such as agile, lean, and ITSM.

Compared with Agile, DevOps has made a tremendous change that has helped reduce the chaos between IT and development teams by breaking them into small teams, more frequent software releases, frequent deployments, and continuous incremental improvements. DevOps also includes Lean principles such as increasing flow and reducing the IT Value stream. It also requires an Agile method for all service and project management processes to help remove bottlenecks and achieve faster lead and cycle time.

Principles of DevOps How First Principle and Practice Work in Real Life?

Continuous Integration – Every day, developers commit codes in a shared repository which is a good development practice.

Continuous Delivery – Any software should be releasable throughout its lifecycle.

Continuous Deployment – Every change in each development phase should pass all automated tests during production.

Value Stream Mapping – A lean tool that helps depict the entire flow of information, material, and works across functional silos, including quality and time.

Theory of Constraints – A methodology for identifying the most limiting factor to achieve a milestone and then systematically improving the constraint until it is no longer the limiting factor.

How Feedback as a Second Principle and Practice Works?

Production Logs: Logs are saviors or rescues to escape everyday errors.

Automated Testing: Manual testing sometimes does not produce much of what we expect at the End phase.

Dashboards: Dashboards such as JIRA and KANBAN for entire project management or to keep track of each team developer’s development work.

Monitoring or Event Management: Ansible tools to monitor the overall system configuration and health check of the builds.

Process Measurements: How to measure the flow of the entire process from development to deployment.

How does Foster help in Attaining DevOps Principles and Practices?

Practices and self-feedback include continuous learning and experimentation.

Experimentation and learning

The Deming Cycle[feedback loop]

Using failure to improve resiliency

A collaborative effort for learning

Adopting the Environment is the most important factor to foster with DevOps as it never stops.

DevOps Tools Capability

DevOps tools deliver the following things which can be listed as follows:

Self Service Projects via project configuration portals.

Dependency analysis and impact analysis.

We have automated builds, testing, and deployment. Quality code and its enhancement across environments and servers.

Optimization of Resources

Another essential aspect and principle of DevOps is the Optimization of Resources. How can it be done?

By Proper scaling of the entire infrastructure.

Re-designing of the entire global services from stacked resources instead of using and wasting new ones.

Also, to transform a solution, it is required to apply agendas across vendors to operate the overall cost for application per user or transaction. Foundation or base is also one of the critical aspects of some reasonable values of DevOps; we can put time and effort into creating an excellent new application environment, redeploying the application, and promoting the application to a new lifecycle phase.

One notion of getting it answered is it includes some difficult aspects to follow, such as

Get the right people together.

Get everyone on the same page with sync.

Build capabilities that lead to lasting change.

Focus on critical behaviors.

Experiment and Learn.

Ultimately, DevOps enables companies to deliver better software faster by improving flow, shortening and amplifying feedback loops, and fostering a culture of continuous improvement and development.

Conclusion – DevOps Principles

Lastly, a conclusion can be made saying that the focus to be kept should be DevOps only. Creating a complex application will help shape an organization with a transformation based on the time-space trade-off required for integrating business, process, and event processors.

Recommended Articles

This has been a guide to the DevOps Principles. Here we discuss its principles, tools capability, and optimization of DevOps. You may also have a look at the following articles to learn more –

Definition of Agile DevOps

DevOps Tools

ITIL vs DevOps

AngularJS Unit Testing

How To Type O With Circumflex Accent On Keyboard

In this post, you’ll learn how to use some keyboard shortcuts to type the letter O with Circumflex Accent. (Ô for uppercase, ô for lowercase)

Using the methods I’m about to teach you, you’ll be able to insert or type both the lowercase and uppercase Accent O with Circumflex.

Before we begin, I’ll like to tell you that you may also use the button below to copy and paste this symbol into your work for free.

However, if you just want to type this symbol on your keyboard, the actionable steps below will show you the way.

Related: How to type letter O with Accent marks

To type the O with Circumflex Accent symbol, press down the Alt key and type 0212 or 0244 (i.e., O Circumflex Accent Alt Codes) using the numeric keypad, then let go of the Alt key. For Mac Users, press [OPTION] + [i] then O, on your keyboard.

These shortcuts can work on any software such as in your browser, MS Word, Excel, and PowerPoint, on both Windows and Mac.

The below table contains all the information you need to type the O with Circumflex Accent Symbol on the keyboard for both Mac and Windows.

The quick guide above provides some useful shortcuts and alt codes on how to type these symbols on both Windows and Mac.

For more details, below are some other methods you can also use to insert this symbol into your work, such as Word or Excel documents.

Microsoft Office provides several methods for typing O with Circumflex Accent Symbol or inserting symbols that do not have dedicated keys on the keyboard.

In this section, I will make available for you several methods you can use to type or insert the O Circumflex Accent Sign on your PC, like in MS Office (i.e., Word, Excel, or PowerPoint) for both Mac and Windows users.

Without any further ado, let’s get started.

Related Posts:

The O with Circumflex Accent alt code is 0212 or 0244 for uppercase and lowercase, respectively.

Even though this Symbol has no dedicated key on the keyboard, you can still type it on the keyboard with the Alt code method. To do this, press and hold down the Alt key whilst pressing the O Circumflex Alt code (i.e., 0212 for Uppercase or 0244 for lowercase) using the numeric keypad.

This method works on Windows only. And your keyboard must also have a numeric keypad.

Below is a break-down of the steps you can use to type the O Circumflex Accent Sign on your Windows PC:

Place your insertion pointer where you need the symbol.

Press and hold one of the Alt keys on your keyboard.

Whilst holding on to the Alt key, press the O with Circumflex Accent alt code (0212 or 0244). You must use the numeric keypad to type the alt code. If you are using a laptop without the numeric keypad, this method may not work for you. On some laptops, there’s a hidden numeric keypad which you can enable by pressing Fn+NmLk on the keyboard.

Release the Alt key after typing the Alt code to insert the Symbol into your document.

This is how you may type this symbol in Word using the Alt Code method.

For Mac users, the keyboard shortcut for the O with Circumflex Accent Symbol is [OPTION] + [i], then a.

For Windows users, use the Alt Code method by pressing down the [Alt] key whilst typing the O Circumflex alt code which is 0212 or 0244. You must use the numeric keypad to type the alt code. Also, ensure that your Num Lock key is turned on.

Below is a breakdown of the O with Circumflex Accent shortcut for Mac:

First of all, place the insertion pointer where you need to type the symbol (Ô ô).

Now, on your keyboard, press [OPTION] + [i] simultaneously then press once on the ‘a’ letter key to insert the symbol.

Below is a breakdown of the O with Circumflex Accent Symbol shortcut for Windows:

Place the insertion pointer at the desired location.

Press and hold down the Alt key

While pressing down the Alt key, type 0212 or 0244 using the numeric keypad to insert the symbol.

These are the steps you may use to type these letters with shortcuts.

Another easy way to get the O with Circumflex Accent Symbol on any PC is to use my favorite method: copy and paste.

All you have to do is to copy the symbol from somewhere like a web page or the character map for windows users, and head over to where you need the symbol (say in Word or Excel), then hit Ctrl+V to paste.

Below is the symbol for you to copy and paste into your Word document. Just select it and press Ctrl+C to copy, switch over to Microsoft Word, place your insertion pointer at the desired location, and press Ctrl+V to paste.



Alternatively, use the copy button at the beginning of this post.

For windows users, obey the following instructions to copy and paste these letters using the character map dialog box.

This is how you may use the Character Map dialog to copy and paste any symbol on Windows PC.

Obey the following steps to insert the O with Circumflex Accent symbol in Word or Excel using the insert symbol dialog box.

Close the dialog.

The symbol will then be inserted where you placed the insertion pointer.

These are the steps you may use to perform this task in Microsoft Word.

As you can see, there are several different methods you can use to type the O with Circumflex Accent in Microsoft Word and other documents.

Using the shortcuts for both Windows and Mac makes the fastest option for this task. Shortcuts are always fast.

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