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RNNs were initially developed to aid in predicting sequences; for example, the Long Short-Term Memory (LSTM) algorithm is well-known for its versatility. These networks operate exclusively on data sequences of varying lengths.

The RNN uses the previous state’s knowledge as an input value for the current prediction. As a result, it can aid in establishing short-term memory in a network, enabling the effective administration of stock price movements or other time-based data systems.

As previously stated, there are two broad categories of RNN designs that aid in issue analysis. They are as follows:

LSTMs: Effective for predicting data in temporal sequences by utilizing memory. It contains three gates: one for input, one for output, and one for forget.

Effective in the following situations:

One to One: A single input is coupled to a single output, as with image categorization.

One to many: A single input is connected to several output sequences, such as picture captioning, which incorporates many words from a single image.

Many to One: Sentiment Analysis is an example of a series of inputs producing a single outcome.

Many to many: As in video classification, a sequence of inputs results in outputs.

Additionally, it is widely used in language translation, dialogue modelling, and other applications.

4. Generative Adversarial Networks

It combines a Generator and a Discriminator, two techniques for deep learning neural networks. The Discriminator aids in differentiating fictional data from real data generated by the Generator Network.

Even if the Generator continues to produce bogus data that is identical in every way, the Discriminator continues to discern real from fake. An image library might be created using simulated data generated by the Generator network in place of the original photographs. In the next step, a deconvolutional neural network would be created.

Following that, an Image Detector network would be used to determine the difference between actual and fraudulent pictures. Starting with a 50% possibility of correctness, the detector must improve its categorization quality as the generator improves its false picture synthesis. This rivalry would ultimately benefit the network’s efficacy and speed.

Effective in the following situations:

Image and Text Generation

Image Enhancement

New Drug Discovery processes

5. Self-Organizing Maps

SOMs, or Self-Organizing Maps, minimize the number of random variables in a model by using unsupervised data. The output dimension is set as a two-dimensional model in this deep learning approach since each synapse links to its input and output nodes.

As each data point vies for model representation, the SOM adjusts the weights of the nearest nodes or Best Matching Units (BMUs). The weights’ values alter in response to the vicinity of a BMU. Because weights are regarded as a node feature in and of itself, the value signifies the node’s placement in the network.


Effective in the following situations:

When the datasets do not include Y-axis values.

Explorations for the dataset framework as part of the project.

AI-assisted creative initiatives in music, video, and text.

6. Boltzmann Machines

Because this network architecture lacks a fixed direction, its nodes are connected circularly. Due to the peculiarity of this approach, it is utilized to generate model parameters.

Unlike all preceding deterministic network models, the Boltzmann Machines model is stochastic in nature.


Effective in the following situations:

Monitoring of the system

Establishment of a platform for binary recommendation

Analyzing certain datasets

7. Deep Reinforcement Learning

Before diving into the Deep Reinforcement Learning approach, it’s important to grasp the concept of reinforcement learning. To assist a network in achieving its goal, the agent can observe the situation and take action accordingly.

This network architecture has an input layer, an output layer, and numerous hidden multiple layers – the input layer containing the state of the environment. The model is based on repeated efforts to forecast the future reward associated with each action made in a given state of the circumstance.


Effective in the following situations:

Board Games like Chess, Poker

Self-Drive Cars


Inventory Management

Financial tasks such as asset valuation

8. Autoencoders

One of the most often used deep learning approaches, this model functions autonomously depending on its inputs before requiring an activation function and decoding the final output. Such a bottleneck creation results in fewer categories of data and the utilization of the majority of the inherent data structures.


Effective in the following situations:

Feature recognition

Creating an enticing recommendation model

Enhance huge datasets using characteristics

9. Backpropagation

Backpropagation, or back-prop, is the basic process through which neural networks learn from data prediction mistakes in deep learning. By contrast, propagation refers to data transfer in a specific direction over a defined channel. The complete system can operate in the forward direction at the time of decision and feeds back any data indicating network deficiencies in reverse.


To begin, the network examines the parameters and decides about the data.

Second, a loss function is used to weigh it.

Thirdly, the detected fault is propagated backwards to self-correct any inaccurate parameters.

Effective in the following situations:

Data Debugging

10 Gradient Descent

Gradient refers to a slop with a quantifiable angle and may be expressed mathematically as a relationship between variables. The link between the error produced by the neural network and the data parameters may be represented as “x” and “y” in this deep learning approach. Due to the dynamic nature of the variables in a neural network, the error can be increased or lowered with modest adjustments.

The goal of this method is to arrive at the best possible outcome. There are ways to prevent data from being caught in a neural network’s local minimum solutions, causing compilations to run slower and be less accurate.


As with the mountain’s terrain, certain functions in the neural network called Convex Functions ensure that data flows at predicted rates and reaches its smallest possible value. Due to variance in the function’s beginning values, there may be differences in the techniques through which data enters the end destination.

Effective in the following situations:

Updating parameters in a certain model


There are several techniques for deep learning approaches, each with its own set of capabilities and strategies. Once these models are found and applied to the appropriate circumstances, they can help developers achieve high-end solutions dependent on the framework they utilize. Best of luck!

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Top 10 Python Deep Learning Libraries For Programmers In 2023

Python has progressively risen to the top of the programming language rankings. This is due to a variety of factors, including its extraordinarily high efficiency in comparison to other widely used languages. Artificial neural networks, which are algorithms modeled after the architecture of the human brain, are used in the machine learning discipline known as deep learning.

Python Deep Learning libraries for programmers in 2023 have emerged as a fantastic technology in recent years. The code is short and easy to comprehend, which makes it ideal for deep learning applications. In comparison to other programming languages, its straightforward syntax also makes it possible to create programs more quickly. Python may be interconnected with other systems written in other programming languages, which is another important benefit of utilizing Python deep learning libraries. As a result, integrating it with AI projects created in other languages is made simpler. In this article, we have mentioned the top 10 Python Deep Learning libraries for programmers in 2023.

1. TensorFlow

One of the top Python libraries for deep learning applications is commonly regarded as TensorFlow. It offers a wide range of adaptable tools, libraries, and community resources and was created by the Google Brain Team. TensorFlow may be used to build deep learning models and neural networks by both novices and experts.

2. Pytorch

Pytorch is another of the most well-known open-source Python libraries for deep learning. It was developed by Facebook’s AI research team in 2023. Torch, a deep learning framework created in the Lua programming language, is where the name of the library originates. You may do a variety of tasks using PyTorch, which is particularly beneficial for deep learning applications like NLP and computer vision.

3. NumPy

NumPy, one of the other well-known Python modules, may be used with ease for processing massive multidimensional arrays and matrices. It is notably helpful for effective basic scientific calculations in deep learning since it is dependent on a sizable collection of high-level mathematical functions.

4. Scikit-Learn

Initially a third-party addition to the SciPy library, Scikit-Learn is now a stand-alone Python package available on GitHub. Scikit-Learn’s classification, regression, and clustering techniques include DBSCAN, gradient boosting, support vector machines, and random forests.

5. Scipy

Now let’s talk about Scipy, a free and open-source library built on NumPy. SciPy is one of the greatest Python libraries available since it can run technical and scientific computations on enormous datasets. It is supplemented with embedded modules for linear algebra and array optimization.

6. Pandas

Pandas is one of the open-source Python libraries that is mostly utilized in data science and deep learning fields. For analyzing data, the library offers capabilities for data manipulation and analysis. The library uses its robust data structures for time series analysis and manipulating numerical tables.

7. Microsoft CNTK

Microsoft CNTK (Cognitive Toolkit), originally known as Computational Network ToolKit, is another Python package for deep learning applications. Distributed deep learning and machine learning tasks are implemented using the open-source deep learning library.

8. Keras

Keras is another well-known deep learning open-source Python module that enables quick testing of deep neural networks. You are given the resources you need to build models, view graphs, and examine datasets using Keras. Additionally, it contains directly importable and loadable prelabeled datasets.

9. Theano

Theano, a Python library for numerical computing that was created primarily for machine learning and deep libraries, is at the bottom of our ranking. You may effectively define, optimize, and assess mathematical expressions and matrix computations with this tool. All of this makes it possible to utilize Theano for the construction of deep-learning models using dimensional arrays.

10. MXNet

Top 3 Task Mining Benefits You Must Know In ’23

Over the last five years, task mining has received a gradual interest from the business circles since it provides a deeper view of the business operations. However, late-adopters of the technology might miss out on the benefits that task mining can offer them.

Therefore, in this article we explain the top 3 benefits of task mining that can help business leaders improve their operations and achieve efficiency. 

1. Understand how you handle business tasks 

Business leaders need to gain a full picture of their business operations and the way they handle their tasks to ensure digital transformation and continuous process improvement. 

Task mining is a useful tool that mines user interactions data, such as screenshots, and recordings. This feature allows businesses to discover unrecognized actions that employees do while performing their tasks, and they might want to take them into account during process planning. 

For example, a process analyst can determine implications for each method used in customer enquiry responding tasks. The analyst can categorize and compare the ways the customer reps answer these enquiries and assess the most efficient method with highest customer satisfaction. 

2. Improved task efficiency

There can be various reasons why a process or task is inefficient such as variations from standard ways of executing tasks, manual tasks, repetitive tasks or tasks with unnecessary steps. Task mining helps identify the reasons where inefficiency originates so that analysts can start implementing the required change.

Some of the ways business leaders can leverage task mining to detect these task efficiency include: 

Improving user experience for employees:

Employees might have a hard time while performing their tasks due to poor user interface, such as distracting background, unaligned elements or lack of text hierarchy. Task mining can detect lower performance related to the interface to avoid such situations. 

Identifying and eliminating unnecessary actions:

Some actions might be unnecessary while doing tasks, or employees might take unnecessary steps that don’t affect the process. Task mining can identify those actions and save the time of employees.

Discovering automation opportunities:

Businesses can observe that some tasks are too straightforward and don’t require high-level skills. Automating those tasks would accelerate processes and reduce human-made errors.

Reducing process deviations:

Task mining can identify the reasons behind the deviations in processes. Businesses can change the way they handle those processes to reduce these deviations.

For instance, in a purchase order process, employees check invoices by matching units and invoice amount information against the order. Task mining enables users to analyze this task and identify bottlenecks and inefficiencies. As a result, the end user can make improvements in the software or process.

3. Compliance benefits

Process design and planning defines tasks and substeps that are necessary to complete a process or comply with rules and regulations. Yet, employees might end up following different paths while executing the tasks because of circumstance. For instance, hospitals suffer from such lack of consistency due to the high number of emergency management cases. 

Even under such conditions, it is possible to comply with rules, regulations and procedures with the help of task mining. As task mining monitors user actions, it identifies such non-compliant issues. Based on the insights, these processes can be modified to reduce non-compliance. 

For a bank, non-compliance might refer to fraudulent cases which can be identified and prevented once the employee allows task mining to monitor tasks executed in an external transaction. 

Combine task mining with process mining

Process mining benefits and task mining benefits seem to share similarities. However, they differ in several aspects as we discussed before. Therefore, we recommend using these two technologies together so that they can harness the full power of them. 

Using both software together allows users to  leverage more comprehensive data (e.g, user task data, system logs and business data). These two complementary techniques include more capabilities that can help reduce variations, enhance customer satisfaction and accelerate process transformation. 

Further reading

Discover more on other process intelligence tools by reading:

If you have more questions on process mining, our whitepaper could help:

If you believe task mining and process mining can benefit your business, feel free to check our data-driven comprehensive vendor lists.

Assess different vendors with a transparent methodology yourself by downloading our checklist: 

And, if you still have some question, we are happy to help:

Hazal Şimşek

Hazal is an industry analyst in AIMultiple. She is experienced in market research, quantitative research and data analytics. She received her master’s degree in Social Sciences from the University of Carlos III of Madrid and her bachelor’s degree in International Relations from Bilkent University.





Top 10 Applications Of Deep Learning In Cybersecurity In 2023

Deep learning tools have a major role to play in the field of cybersecurity in 2023.

Deep learning

which is also known as Deep Neural Network includes machine learning techniques that enable the network to learn from unsupervised data and solve complex problems. It can be extensively used for


to protect companies from threats like


, spear-phishing, drive-by attack, a

password attack

, denial of service, etc. Learn about the top 10 applications of

deep learning

in cybersecurity. 


Detecting Trace of Intrusion

Deep learning

, convolutional neural networks, and Recurrent Neural Networks (RNNs) can be applied to create smarter ID/IP systems by analyzing the traffic with better accuracy, reducing the number of false alerts, and helping security teams differentiate bad and good network activities. Notable solutions include Next-Generation Firewall (NGFW), Web Application Firewall (WAF), and User Entity and Behavior Analytics (UEBA).


Battle against Malware


Spam and Social Engineering Detection

Natural Language Processing (NLP), a deep learning technique, can help you to easily detect and deal with spam and other forms of social engineering. NLP learns normal forms of communication and language patterns and uses various statistical models to detect and block spam. You can read this post to learn how Google used TensorFlow to enhance the spam detection capabilities of Gmail.  

Network Traffic Analysis

Deep learning

ANNs are showing promising results in analyzing HTTPS network traffic to look for malicious activities. This is very useful to deal with many cyber threats such as SQL injections and DOS attacks.


User Behavior Analytics

Tracking and analyzing user activities and behaviors is an important deep learning-based security practice for any organization. It is much more challenging than recognizing traditional malicious activities against the networks since it bypasses security measures and often doesn’t raise any flags and alerts. User and Entity Behavior Analytics (UEBA) is a great tool against such attacks. After a learning period, it can pick up normal employee behavioral patterns and recognize suspicious activities, such as accessing the system in unusual hours, that possibly indicate an insider attack and raise alerts.  

Monitoring Emails

It is vital to keep an eye on the official Email accounts of the employees to prevent any kind of cyberattacks. For instance, phishing attacks are commonly caused through emails to employees and asking them for sensitive data. Cybersecurity software along with deep learning can be used to avoid these kinds of attacks. Natural language processing can also be used to scan emails for any suspicious behavior.


Analyzing Mobile Endpoints

Deep learning

is already going mainstream on mobile devices and is also driving voice-based experiences through mobile assistants. So using deep learning, one can identify and analyze threats against mobile endpoints when the enterprise wants to prevent the growing number of malware on mobile devices.


Enhancing Human Analysis

Deep learning

in cybersecurity can help humans to detect malicious attacks, endpoint protection, analyze the network, and do vulnerability assessments. Through this, humans can decide on things better by bringing out ways and means to find the solutions to the problems.


Task Automation

The main benefit of deep learning is to automate repetitive tasks that can enable staff to focus on more important work. There are a few cybersecurity tasks that can be automated with the help of machine learning. By incorporating deep learning into the tasks, organizations can accomplish tasks faster and better.  


WebShell is a piece of code that is maliciously loaded into a website to provide access to make modifications on the Webroot of the server. This allows attackers to gain access to the database. Deep learning can help in detecting the normal shopping cart behavior and the model can be trained to differentiate between normal and malicious behavior.  

Network Risk Scoring

Top 10 Important Business Analysis Techniques

Business analysis is a process to analyze an organization’s business needs and identify opportunities to improve or exploit. Business analysis is the

Business Analysis Disciplines

Business analysis is a broad term that includes a number of different disciplines. There are three main types of business analysis: functional, process and organizational. Functional business analysis looks at the current system to see how it works and what the customer needs. Process business analysis looks at how the process is executed by examining its steps and workflow. Organizational business analysis examines the corporate culture and how it performs in relation to customer needs, market conditions, competition, etc. A great way to increase your chances of success in any type of business analysis is by bringing together people with

Below we will list down the important business analysis techniques: SWOT Analysis

A SWOT analysis is a quick and simple way to identify the strengths, weaknesses, opportunities and threats of a business. A SWOT analysis is an instrument that is used to compile information about the company, its strengths, weaknesses, opportunities and threats. It is a very practical organizational tool that helps in analyzing performance and potential of the business. This technique identifies significant aspects of a business or organization so it can take steps in the right direction with clear strategies for success. SWOT analysis is commonly used in smaller businesses and startups.  

MOST Analysis

MOST analysis is a common form of qualitative research that helps to determine which purchasing motivations are most important for individual consumers. MOST analysis is a process where the researchers ask the consumers what they think motivates them to purchase a certain product and how much they value each motivation. The survey consists of five motivations – money, other people, status, image and fear of missing out. The survey asks respondents which two they consider most important or how happy they are with each aspect among participants.  

Business Process modelling

Business process modelling is the process of analyzing your business processes and then providing a diagram that identifies where efficiencies can be made. Business Process Modelling is important for any company looking to improve its operational efficiency. It can help you identify what processes are most time consuming, which ones are redundant and what could be done differently to make your business more productive. Business Process Modelling also provides a blueprint for future growth opportunities, by measuring the potential impact of new technologies on company operations.  

Use Case Modeling

The use case model is a representation of the system being developed. The process involves identifying stakeholders, actors, and use cases. The use case model is a representation of the system being developed. The process involves identifying stakeholders, actors, and use cases. This method can be used by business analysts to determine the requirements of a system from an end user’s perspective. It will also help them identify gaps that need to be filled in by software development teams. Use Case modelling is an integral part of agile software development because it helps engineers understand how the product will be used and what it must accomplish during each stage of its lifecycle.  


Brainstorming in business analysis is a way of generating new ideas and solutions for problems. It’s a collaborative process that involves many people. Brainstorming is important to businesses because it helps increase productivity, creativity, and problem solving skills. This process also gives workers a chance to think about their own ideas without the pressure of having to come up with an answer immediately. It can be challenging to get people from all levels in an organization involved in brainstorming sessions. But it’s worth the effort because the more diverse viewpoints that are included, the better solutions can be found.  

Non-functional Requirement Analysis

Non-functional requirements are often overlooked, but they are the most important part of a software. These requirements include security, reliability, scalability, usability and accessibility among others. They are more difficult to test and assess than functional requirements because they are not code-based and their effects are not immediately visible.  

PESTLE Analysis

There are several factors that need to be taken into consideration when performing a PESTLE analysis. These include: – Political landscape – Economic stability – Social conditions – Technological environment – Legal and regulatory framework. PESTLE analysis is a tool that can be used to assess the external and internal environment in which a business operates. It provides a snapshot of the political, economic, social, technological, legal, environmental and competitive factors that shape an organization’s operating environment. PESTLE analysis is useful because it helps business people to see both the opportunities and challenges present in their sectors.  

Requirement Analysis

Requirement analysis is a critical stage of a project because it is the stage where we know what are the requirements that need to be fulfilled. A project can be failed if its requirements are not met. Requirement analysis is a systematic and research-oriented process to identify, analyze, and document the needs or requirements of stakeholders in all aspects of a proposed product or service. It involves identifying stakeholder needs, understanding stakeholder priorities, and synthesizing this information into detailed requirements for how to satisfy these needs.  

User Stories

User stories are a great format for documenting the requirements of a new system. They are also often used by teams to coordinate their work. User stories help us to understand the motivations and priorities of the users in different ways. The user stories represent an atomic unit of system functionality. The team then needs to break these user stories into tasks and estimate how long they will take.  


CATWOE stands for context, audience, task, work environment, organization, and equipment. It is a mnemonic device to help analysts to remember the essential aspects of the context in which they are performing analysis.

Top 10 Learning Tips For Self

In this article, we gave the top 10 learning tips for self-taught data scientists in 2023

Data science is an essential part of many industries today, in the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data scientists examine which questions need answering and where to find the related data. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In this modern age of information technology, an enormous chances are available to learn data science for self-study to become data scientists, can master the fundamentals of data science. Here we gave the learning tips for self-taught data scientists.

Learn The Tools: There are many tools that data scientists can use to process, analyze, and visualize data. SAS, Apache Spark or simply Spark, BigML, Github, Jupyter notebooks, TensorFlow, chúng tôi MATLAB, Excel, ggplot2, Tableau, Jupyter, Matplotlib, Natural Language Processing, Scikit-learn, TensorFlow these are the some of the tools can be overwhelming to learn to become a data scientist.

Level Up Your Soft Skills: Making a career in data science is just as much about people skills as it is technical. In the process of product development, improving customer retention, or mining through data to find new business opportunities, organizations are increasingly relying on data scientist skills to sustain, grow, and stay one step ahead of the competition.

Signing Up for Hackathons: Hackathons are events where you work on a project with other people. It helps to learn how to put all new data science knowledge into practice as well as meet like-minded individuals who were also interested in learning more about data science or had already learned quite a bit.

Learning from Textbooks: Learning from textbooks provides a more refined and in-depth knowledge beyond what you get from online courses. These books provide a great introduction to data science and machine learning, with code including Python Machine Learning.

Practice The Fundamentals: The data science method looks similar to the scientific method, but with the heaviest emphasis on ensuring that all the data used is of the highest quality. Data wrangling comprises the bulk of data science because, without quality data, your insights are meaningless, or worse, incorrect.

Online courses: These online courses really help to learn the fundamentals of data science and how to apply them. It was able to see what other people were working on, and how they approached problems.

Dive into the Technical: One area where traditional learning can be beneficial is in the technical aspects of data science. The field has underlying mathematical concepts that separate data scientists from data hobbyists. Regression analysis, Probability, Statistics, Linear algebra, etc are some of the essential concepts for budding data scientists.

Doing personal projects: Doing personal projects to help build up a portfolio. This also helps to learn more about real-world problems as well as how people approach which is something don’t get from online courses.

Pick Up a Programming Language: Without learning the programming language, one cannot become a data scientist. Data scientists build algorithms and environments to run those algorithms. Python, R-programming, and Context-specific language are some of the popular programming languages for data science.

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