Trending December 2023 # 5 Bestplugins You Must Have # Suggested January 2024 # Top 18 Popular

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5 best chúng tôi plugins you must have

There are many image editors on the market, but one of the easiest to use is Paint.NET.

The software has support for plugins, and in today’s article, we’re going to show you the best chúng tôi plugins that you can get.

Want to learn more about chúng tôi Be sure to check this dedicated chúng tôi article for more information.

Looking for more guides like this one? You can find them in our Download Hub.

INSTALL BY CLICKING THE DOWNLOAD FILE

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When it comes to image editors, chúng tôi is a pretty popular choice for many users. The software is simple to use, and it offers great features.

Paint.NET also supports various plugins, and in this article, we’re going to show you the best chúng tôi plugins and how to install them.

How to install chúng tôi plugins? Extract the plugin to the required directory

Download the plugin archive.

Open the archive.

C:Program Filespaint.net

Now extract the plugin to the required directory. In most cases, the plugin will come with instructions, so you’ll know where to extract it.

For the Windows Store version do the following:

C:Usersyour_usernameDocuments

Create a new folder called paint.net App Files.

Navigate to the paint.net App Files directory that you just created.

Now create three different folders inside it named: Effects, FileTypes, Shapes.

To install a plugin, just download it and move it to the directory that’s mentioned in the instructions file.

This plugin is created by a user called midora, and it requires two additional plugins in order to work. To use this plugin, you need to create layers individually.

After doing that, you need to rename them but adding

For example, you can assign the number of loops for the animation, or the duration for each individual frame.

Overall, the GIF Animation Creator FileType plugin is a solid chúng tôi GIF plugin, so you should really consider it.

PSD Plugin for chúng tôi is a chúng tôi Photoshop plugin, and it lets you work with Photoshop files. The plugin will allow you to open and save PSD files with ease in Paint.NET.

The plugin will save RGB images, color depth of 8 bits per channel, raster images, layers along with their blend modes, and RLE compression from PSD files without any problems.

By using this plugin, you can easily edit your files in both chúng tôi and Photoshop, and if you’re using both applications, be sure to try this plugin.

Expert tip:

To create the perspective effect, you just need to move the image corners to get the desired effect. You can also apply the antialiasing effect or create a forced perspective.

Overall, the plugin is incredibly simple to use, and it can create great perspective effects, so be sure to download it and try it out.

VTF files are associated with Valve’s games, and if you want to create your own VTF files, then this might be the perfect chúng tôi VTF plugin for you.

VTF Plug-In for chúng tôi brings support for a single frame or multi-frame VTF files. In addition, the plugin also works with 3 or 4 channel VTF files.

The plugin is simple to use, and it can be used to create simple VTF files. Although it’s not as powerful as other dedicated tools, it still offers solid features, so you should try it out.

If you’re looking for a bevel plugin for chúng tôi Object Bevel might be just what you’re looking for. The software allows you to create various types of bevel effects with ease.

Speaking of bevel effects, you can create 10 different types of effects. All effects can be customized, and you can change the position of the light source, overall depth, the color of the bevel, etc.

The plugin is simple to use, but it offers extensive features, and it’s one of the best bevel plugins for chúng tôi out there.

Paint.NET is a great image editing software, but it’s even better with plugins. We listed some of the best and most commonly used plugins in this article that will make chúng tôi even better than before.

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5 Best Janitor Ai Alternatives You Must Try Now

Janitor AI is a powerful tool for creating and engaging with customized characters. However, it has certain restrictions and there are some worthy Janitor AI alternatives to try now. Janitor AI, for example, can be difficult to employ and can occasionally create unrealistic or boring characters. If you’re looking for a Janitor AI alternatives, there are a few outstanding possibilities.

In recent days, a large number of Janitor AI users have reported a variety of faults with the software. As a result, many people have turned to Google to look for “Janitor AI Alternatives.” Many users have complained about Janitor AI not working or not responding. Many users have specifically identified issues with the software’s chatbot capability. Here are a few of the greatest Janitor AI alternatives

Character AI is a web application that allows you to talk with artificial intelligence (AI) characters, each with their own personality and the capacity to respond to your words naturally. You also have the option of creating your own characters and sharing them with other users. This platform is one of the best Janitor AI alternatives.

Character AI allows users to create their own unique “characters” by customizing their “personalities” and selecting particular criteria. These customized characters can then be shared with the community to start discussion. To make a character, people define themselves from the character’s point of view and write a greeting message. The character’s “voice” and identity are formed based on conversational examples, allowing them to respond organically in conversations. This tool allows users to build a wide range of characters with distinct personalities.

Character creation: Users can build their own characters or select from a library of pre-made characters. Each character has their own personality, story, and set of abilities.

Conversation: Character AI can chat with humans, reacting to their prompts in a genuine and engaging manner. The topic of the chat can range from the everyday to the magical.

Prompting: Users can direct Character AI to generate text, code, scripts, musical compositions, email, letters, and so on. The text can be written in a variety of ways, such as creative writing, formal writing, or technical writing.

Learning: Character AI is continually learning and improving itself. The more it interacts with users, the better it develops at generating writing and conversing.

Users can engage in conversations using AI-generated text within a chat-like environment, making it a great tool for activities such as role-playing, generating fan fiction, and talking with AI-based companions. This platform provides a simple and convenient way to interact with text-generation AI systems such as ChatGPT and NovelAI, making it a great resource for anyone looking for immersive and interactive artificial intelligence experiences.

With automated text generating technologies, the chatbot provides natural and smooth discussions. This is especially useful when looking for an AI partner, using an AI model for role-playing, or adding life into fan fiction.

Dynamic characters: The characters in Tavern AI are not static. They each have distinct personalities, goals, and backstories. This implies that your talks with them will be unique each time.

Multi-turn conversations: Tavern AI is capable of handling complex, multi-turn dialogues. This means you can have a free-flowing chat with the characters without having to divide it up into tiny bits.

Personalized responses: Tavern AI can tailor its responses to your specific preferences. This means that the characters will pick up on your communication style and modify their responses accordingly.

Customizable settings: Tavern AI allows you to change a lot of variables, including the background, character appearance, and chat length. This allows you to personalize a chat experience to your own requirements.

Chai AI creates a dynamic environment in which AI users can interact with one another. These AIs may communicate with clients via a cutting-edge smartphone app (also available online), simulating human-like interactions. The platform now contains a large number of bots, with fresh additions being added on a daily basis.

Chai created the Python module “Chaipy” to automate and simplify the bot construction process. Chaipy is a user-friendly tool for building, testing, and deploying chatbots. Although Chai AI has been around since 2023, it did not gain major traction and acceptance until February 2023.

Chat with AI chatbots: Chai AI allows users to communicate with a range of AI chatbots, each with its own unique personality and set of talents.

Design your own chatbot: Using a simple drag-and-drop interface, users may design their own chatbots.

Personalize your chatbot: Users can change the appearance, personality, and skill set of their chatbots.

Share your chatbots: Users can share their chatbots with others or publish them on the Chai AI marketplace.

Community: Chai AI has a huge and active user community that may share tips, tactics, and chatbots.

NovelAI is a GPT-powered creative sandbox that provides a monthly subscription service for AI-assisted composition, storytelling, and virtual companionship. NovelAI also contains a tool for image generation, expanding its powers even further.

Novel AI is an excellent AI story generator that employs powerful algorithms to generate professional-level writing. Its goal is not to entirely replace human writers, but rather to help them create captivating tales by providing narrative inspiration and assisting in story development.

Furthermore, the app can generate AI-generated visuals using text-to-image AI art, allowing for visual depictions of characters to supplement the storytelling experience. Novel AI, in contrast to other AI writing apps, concentrates on three important features: the first two are intended to help in narrative writing, while the third is dedicated to making visuals for stories and comics.

AI-assisted authorship: Using your own input, novel AI can help you generate human-like writing. It can also adapt to various writing styles, allowing you to write in the manner that feels most natural to you.

Storytelling: From tiny beginnings to massive sagas, novel AI may assist you in creating stories. There are other genres to pick from, including fantasy, science fiction, mystery, and romance.

Character creation: From simple character to complicated character, novel AI can assist you in creating characters. You can select from a wide range of races, classes, and professions.

World building: New AI can assist you in creating worlds ranging from modest villages to huge city. There are several environments to pick from, including forests, deserts, and mountains.

Image generation: Using your instructions, Novel AI can generate images. This might be an excellent method for visualizing your narrative and characters.

Collaboration: Novel AI allows you to work on stories with other users. This is an excellent opportunity to exchange ideas and receive feedback on your work.

Community: Novel AI has a huge and active user base. You may connect with other people and learn more about Novel AI by participating in forums, chatrooms, and other online communities.

Replika AI uses artificial intelligence to engage in unique and sympathetic conversations with people. While Replika AI is not human, it can adapt to your preferences and routines, resulting in a personalized experience that fits your needs.

Depending on your needs and interests, Replika AI can serve as a companion, assistance, or buddy. Replika AI can help you in a variety of ways, including mental health support, goal setting, hobby recommendations, and more. Replika AI has gained broad use and appreciation as a popular alternative to Janitor AI.

Conversational AI: Replika AI can hold conversations with users and respond to their requests in a natural and engaging manner. The topic of the chat can range from the everyday to the magical.

Personalization: Over time, Replika AI learns about the user’s interests and preferences and tailors its responses accordingly. This makes the conversation more personal and engaging.

Empathy: Replika AI is intended to be empathic and understanding. It can listen to the user’s issues and provide assistance and recommendations.

Motivation: Replika AI can assist users in setting objectives and tracking their success. It can also provide motivation and support along the road.

Also Read: Janitor AI: Free and Powerful Tool for Character Creation

In conclusion, there are a number of great Janitor AI alternatives available for creating characters. Each of these Janitor AI alternatives has its own strengths and weaknesses, so it is important to choose the one that best suits your needs.

Xiaomi Mi Air Purifier 2: Top 5 Reasons Why You Must Buy

The well known consumer electronics maker Xiaomi is bringing their latest product in India. To fight the problem of in house air pollution, Xiaomi has rolled out the Mi Air Purifier 2 today. We have been using this device at our office and we know how useful this thing is. You won’t realize but it definitely improves the quality of the air you breathe and also keeps you away from a lot of health related issues.

Diseases such as asthma are uncertain to any of us. In severe cases, the allergens can unknowingly attack on anyone and result in dysfunction of lungs. For patients with such problems and for those who want to almost kill the risk of such diseases, Mi Air Purifier is a must buy appliance for homes. It keeps the air quality high and allows easy breathing and reduces chances of respiratory diseases.

We all know smoking is not good for our health, so is passive or indirect smoking. Passive smoking can do incredible damages to a non-smoker as well. If you live with a smoker and don’t want to risk your health with such problem, you must get the Mi Air Purifier 2. It comes with a HEPA filter that is specially designed to eliminate smoke and tobacco pollutants and odors.

Smell is something that can play a major role in setting your mood. Also the smell is something that puts an impression on your guests or friends when they visit you at your home. You never know what your house smells like. You don’t realize but smells from the kitchen, the bathroom, smoking and even the paint of the house mix up and make an unpleasant and stinky. The Mi Air Purifier can help you get rid of such stench and keep you feeling positive when your guests visit you.

Having pets at home is nothing new for anyone, but there is something you all must know for self hygiene. Using a vacuum cleaner to clean up the hair is not the only solution to keep up the hygiene; there are several other particles that can cause allergies.

The tiny and invisible particles from your pets can be a major cause of allergies. Saliva, skin glands and urine are some more sources which can be on you beds, sofas, carpets etc. The Mi Air Purifier 2 with HEPA filter helps in tackling the pet dander. The carbon-activated filters capture the tiny pet danders as small as 0.3 microns and also remove the bad odor from the air.

In the end, we all are looking for things that make our lives better and easier. Mi Air Purifier is one of the products that do not need a second thought if you really care about your health. If you can afford to pay Rs. 9,999 for once, you can surely save it unknowingly by making your daily lives healthier on a longer run.

5 Lesser Known Ubuntu Based Distro You Have Not Heard Of

Poseidon Linux is an Ubuntu variant for scientists. It is developed and maintained by a group of young scientists from Brazil and Germany. Poseidon comes pre-installed with many software related to GIS, Mapping, CAd, 2D/3D modelling, statistics, genetics etc that are frequently used by Professors, students and scientists. The inclusion of many academics related software and the ease of use have made it a popular Linux distribution among academicians, who usually prefer to stay away from Linux.

Vinux is an Ubuntu-based Linux distribution for visually impaired people. It is developed by Tony Sales who teaches at National College for the Blind in Hereford, United Kingdom. Tony was frustrated with the default accessibility support provided by mainstream Linux distributions and this is when decided to develop a Linux distribution solely for blinds and partially sighted users.

Vinux provides a screen-reader, full-screen magnification and support for Braille displays out of the box. It can be used alongside Windows or as the sole operating system. It has been developed in such a way that a visually impaired person can install it on his/her own without much effort. A great OS, I would say.

Now, don’t run away from this Ubuntu just because you saw Math in its name. Mathbuntu is an Ubuntu based Linux distribution exclusively for maths lovers (yes! this species exists and I am one of them). Mathbuntu comes with bunch of free and open source Mathematics software. It also comes with lots of free textbooks. It has two variants available – one based on Ubuntu and other based on Kubuntu. Some of the pre-installed software include Sage (Mathematics Software System), Maxima (Computer Algebra System), R (Statistical Computing), Octave and Scilab (Numerical Computation), GeoGebra (Interactive Geometry and Algebra).

Peppermint Linux OS is a web application / cloud focused Linux distribution based on Ubuntu. Being a lightweight Linux distribution, it consists of mostly web applications instead of native desktop application for e.g., it uses Google Docs instead of a regular office product. It is pretty fast to boot-up and can be (loosely) termed as a competitor to Google’s Chrome OS.

If you thought Linux has nothing to do with religion, you were wrong. Sabily is an Ubuntu distribution for Muslims. It is not like that it is some sort of “halal Linux” but it comes with applications useful for practicing Muslims such as prayer time tool, Koran study tool etc. It has great support for Arabic language.

There are many more Ubuntu variants. I have deliberately not included popular ones like Backtrack, Bodhi Linux, Elementary Linux etc. What do you think of these weirdo Ubuntu variants? Do let us know your views.

Image Credits: Wikipedia

Abhishek Prakash

Abhishek is a Linux lover and Opens Source enthusiast. He takes a keen interest in day-to-day computer life and wishes to share his experience with others to make their computer experience better and easier. He is the owner of increasingly popular tech blog Computer And You and Open Source blog It’s FOSS.

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Master Dimensionality Reduction With These 5 Must

Overview

Singular Value Decomposition (SVD) is a common dimensionality reduction technique in data science

We will discuss 5 must-know applications of SVD here and understand their role in data science

We will also see three different ways of implementing SVD in Python

“Another day has passed, and I still haven’t used y = mx + b.“

Sounds familiar? I often hear my school and college acquaintances complain that the algebra equations they spent so much time on are essentially useless in the real world.

Well – I can assure you that’s simply not true. Especially if you want to carve out a career in data science.

Linear algebra bridges the gap between theory and practical implementation of concepts. A healthy understanding of linear algebra opens doors to machine learning algorithms we thought were impossible to understand. And one such use of linear algebra is in Singular Value Decomposition (SVD) for dimensionality reduction.

You must have come across SVD a lot in data science. It’s everywhere, especially when we’re dealing with dimensionality reduction. But what is it? How does it work? And what are SVD’s applications?

I briefly mentioned SVD and its applications in my article on the Applications of Linear Algebra in Data Science. In fact, SVD is the foundation of Recommendation Systems that are at the heart of huge companies like Google, YouTube, Amazon, Facebook and many more.

We will look at five super useful applications of SVD in this article. But we won’t stop there – we will explore how we can use SVD in Python in three different ways as well.

And if you’re looking for a one-stop-shop to learn all machine learning concepts, we have put together one of the most comprehensive courses available anywhere. Make sure you check it out (and yes, SVD is in there as part of the dimensionality reduction module).

Applications of Singular Value Decomposition (SVD)

Image Compression

Image Recovery

Eigenfaces

Spectral Clustering

Background Removal from Videos

What is Singular Value Decomposition?

Rank of a Matrix

Singular Value Decomposition

Why is SVD used in Dimensionality Reduction?

3 Ways to Perform SVD in Python

We are going to follow a top-down approach here and discuss the applications first. I have explained the math behind SVD after the applications for those interested in how it works underneath.

You just need to know four things to understand the applications:

SVD is the decomposition of a matrix A into 3 matrices – U, S, and V

S is the diagonal matrix of singular values. Think of singular values as the importance values of different features in the matrix

The rank of a matrix is a measure of the unique information stored in a matrix. Higher the rank, more the information

Eigenvectors of a matrix are directions of maximum spread or variance of data

In most of the applications, the basic principle of Dimensionality Reduction is used. You want to reduce a high-rank matrix to a low-rank matrix while preserving important information.

It minimizes the size of an image in bytes to an acceptable level of quality. This means that you are able to store more images in the same disk space as compared to before.

Here’s how you can code this in Python:

Python Code:

Output:

If you ask me, even the last image (with n_components = 100) is quite impressive. I would not have guessed that it was compressed if I did not have the other images for comparison.

We’ll understand image recovery through the concept of matrix completion (and a cool Netflix example).

Matrix Completion is the process of filling in the missing entries in a partially observed matrix. The Netflix problem is a common example of this.

The basic fact that helps to solve this problem is that most users have a pattern in the movies they watch and in the ratings they give to these movies. So, the ratings-matrix has little unique information. This means that a low-rank matrix would be able to provide a good enough approximation for the matrix.

This is what we achieve with the help of SVD.

Where else do you see this property? Yes, in matrices of images! Since an image is contiguous, the values of most pixels depend on the pixels around them. So a low-rank matrix can be a good approximation of these images.

Here is a snapshot of the results:

Chen, Zihan. “Singular Value Decomposition and its Applications in Image Processing.” ACM, 2023

The original paper Eigenfaces for Recognition came out in 1991. Before this, most of the approaches for facial recognition dealt with identifying individual features such as the eyes or the nose and developing a face model by the position, size, and relationships among these features.

The Eigenface approach sought to extract the relevant information in a face image, encode it as efficiently as possible, and compare one face encoding with a database of models encoded similarly.

The encoding is obtained by expressing each face as a linear combination of the selected eigenfaces in the new face space.

Let me break the approach down into five steps:

Collect a training set of faces as the training set

Find the most important features by finding the directions of maximum variance – the eigenvectors or the eigenfaces

Choose top M eigenfaces corresponding to the highest eigenvalues. These eigenfaces now define a new face space

Project all the data in this face space

For a new face, project it into the new face space, find the closest face(s) in the space, and classify the face as a known or an unknown face

You can find these eigenfaces using both PCA and SVD. Here is the first of several eigenfaces I obtained after performing SVD on the Labelled Faces in the Wild dataset:

As we can see, only the images in the first few rows look like actual faces. Others look noisy and hence I discarded them. I preserved a total of 120 eigenfaces and transformed the data into the new face space. Then I used the k-nearest neighbors classifier to predict the names based on the faces.

You can see the classification report below. Clearly, there is scope for improvement. You can try adjusting the number of eigenfaces to preserve and experiment with different classifiers:

Have a look at some of the predictions and their true labels:

You can find my attempt at Facial Recognition using Eigenfaces here.

Clustering is the task of grouping similar objects together. It is an unsupervised machine learning technique. For most of us, clustering is synonymous with K-Means Clustering – a simple but powerful algorithm. However, it is not always the most accurate.

Consider the below case:

Clearly, there are 2 clusters in concentric circles. But KMeans with n_clusters = 2 gives the following clusters:

K-Means is definitely not the appropriate algorithm to use here. Spectral clustering is a technique that combats this. It has roots in Graph theory. These are the basic steps:

Start with the Affinity matrix (A) or the Adjacency matrix of the data. This represents how similar one object is to another. In a graph, this would represent if an edge existed between the points or not

Find the Laplacian (L) of the Affinity Matrix: L = A – D

Find the highest k eigenvectors of the Laplacian Matrix depending on their eigenvalues

Run k-means on these eigenvectors to cluster the objects into k classes

You can read about the complete algorithm and its math here. The implementation of Spectral Clustering in scikit-learn is similar to KMeans:

View the code on Gist.

You will obtain the below perfectly clustered data from the above code:

I have always been curious how all those TV commercials and programs manage to get a cool background behind the actors. While this can be done manually, why put in that much manual effort when you have machine learning?

Think of how you would distinguish the background of a video from its foreground. The background of a video is essentially static – it does not see a lot of movement. All the movement is seen in the foreground. This is the property that we exploit to separate the background from the foreground.

Here are the steps we can follow for implementing this approach:

Create matrix M from video – This is done by sampling image snapshots from the video at regular intervals, flattening these image matrices to arrays, and storing them as the columns of matrix M

We get the following plot for matrix M:

What do you think these horizontal and wavy lines represent? Take a moment to think about this.

The horizontal lines represent the pixel values that do not change throughout the video. So essentially, these represent the background in the video. The wavy lines show movement and represent the foreground.

We can, therefore, think of M as being the sum of two matrices – one representing the background and other the foreground

The background matrix does not see a variation in pixels and is thus redundant i.e. it does not have a lot of unique information. So, it is a low-rank matrix

So, a low-rank approximation of M is the background matrix. We use SVD in this step

We can obtain the foreground matrix by simply subtracting the background matrix from the matrix M

Here is a frame of the video after removing the background:

Pretty impressive, right?

We have discussed five very useful applications of SVD so far. But how does the math behind SVD actually work? And how useful is it for us as data scientists? Let’s understand these points in the next section.

I have used the term rank a lot in this article. In fact, through all the literature on SVD and its applications, you will encounter the term “rank of a matrix” very frequently. So let us start by understanding what this is.

The rank of a matrix is the maximum number of linearly independent row (or column) vectors in the matrix. A vector r is said to be linearly independent of vectors r1 and r2 if it cannot be expressed as a linear combination of r1 and r2.

Consider the three matrices below:

In matrix A, row r2 is a multiple of r1, r2 = 2 r1, so it has only one independent row. Rank(A) = 1

In matrix B, row r3 is a sum of r1 and r2, r3 = r1 + r2, but r1 and r2 are independent. Rank(B) = 2

In matrix C, all 3 rows are independent of each other. Rank(C) = 3

The rank of a matrix can be thought of as a representative of the amount of unique information represented by the matrix. Higher the rank, higher the information.

So where does SVD fit into the overall picture? SVD deals with decomposing a matrix into a product of 3 matrices as shown:

If the dimensions of A are m x n:

U is an m x m matrix of Left Singular Vectors

S is an m x n rectangular diagonal matrix of Singular Values arranged in decreasing order

V is an n x n matrix of Right Singular Vectors

You might be wondering why we should go through with this seemingly painstaking decomposition. The reason can be understood by an alternate representation of the decomposition. See the figure below:

The decomposition allows us to express our original matrix as a linear combination of low-rank matrices.

In a practical application, you will observe that only the first few, say k, singular values are large. The rest of the singular values approach zero. As a result, terms except the first few can be ignored without losing much of the information. See how the matrices are truncated in the figure below:

To summarize:

Using SVD, we are able to represent our large matrix A by 3 smaller matrices U, S and V

This is helpful in large computations

We can obtain a k-rank approximation of A. To do this, select the first k singular values and truncate the 3 matrices accordingly

We know what SVD is, how it works, and where it is used in the real world. But how can we implement SVD on our own?

The concept of SVD sounds complex enough. You might be wondering how to find the 3 matrices U, S, and V. It is a long process if we were to calculate these by hand.

Fortunately, we do not need to perform these calculations manually. We can implement SVD in Python in three simple ways.

NumPy is the fundamental package for Scientific Computing in Python. It has useful Linear Algebra capabilities along with other applications.

You can obtain the complete matrices U, S, and V using SVD in numpy.linalg. Note that S is a diagonal matrix which means that most of its entries are zeros. This is called a sparse matrix. To save space, S is returned as a 1D array of singular values instead of the complete 2D matrix.

View the code on Gist.

In most common applications, we do not want to find the complete matrices U, S and V. We saw this in dimensionality reduction and Latent Semantic Analysis, remember?

We are ultimately going to trim our matrices, so why find the complete matrices in the first place?

In such cases, it is better to use TruncatedSVD from sklearn.decomposition. You specify the number of features you want in the output as the n_components parameter. n_components should be strictly less than the number of features in the input matrix:

View the code on Gist.

Randomized SVD gives the same results as Truncated SVD and has a faster computation time. While Truncated SVD uses an exact solver ARPACK, Randomized SVD uses approximation techniques.

View the code on Gist.

I really feel Singular Value Decomposition is underrated. It is an important fundamental concept of Linear Algebra and its applications are so cool! Trust me, what we saw is just a fraction of SVD’s numerous uses.

I encourage you to check out this Comprehensive Guide to build Recommendation Engine from scratch to realize the power of SVD for yourself. Building this project will surely add value to your resume (and enhance your own skillset!).

5 Traps The (New) Pirate Bay Must Avoid

Ah, the Internet cafe; home to yuppies, flavored lattes, and The Pirate Bay? If you haven’t heard, Swedish Internet cafe operator, Global Gaming Factory X, today bought The Pirate Bay for $7.8 million. The new owner says it intends to sail The Pirate Bay out of its murky waters, and put the former rogues gallery on the straight and narrow. In other words, The Pirate Bay is going legitimate.

“Following the completion of the acquisitions, GGF intends to launch new business models that allow compensation to the content providers and copyright owners.”

If reading that news just upset you, then perhaps you are one of the millions of one-eyed curs who regularly pillage copyrighted content with the help of the site. But before you draw your sword in anger over the death of your beloved pirate haven, perhaps this is your shining moment to choose a life free from lurking in the seedier regions of the Internet. Pirates can reform themselves after all, and maybe it’s not too late for you to avoid the fate that led Jammie Thomas to the RIAA’s gallows.

So before you do something hasty — like moving over to torrent tracker alternative site Mininova — prop up your wooden leg and read my list of five things The (New) Pirate Bay needs to avoid to keep its six million registered users happy.

1) Don’t turn into Napster

Graphic: Diego AguirreIn its heyday, Napster was the ultimate file-sharing site. But its rogue life was cut short when heavyweight lawyers from the record industry caught up with Napster. Now, Napster is a shell of its former self, and has been re-launched at least three times with as many new strategies. Currently, Napster users can download five songs a month and get unlimited streaming for a monthly subscription fee of five dollars. Is it restrictive? Yes. Do people like it? Not as much as the first incarnation of Napster. The Pirate Bay needs to avoid Napster’s fate at all costs.

Illustration: Lou BeachPeople came to The Pirate Bay for everything from MP3s to episodes of their favorite TV shows. Would TPB’s user base be willing to pay? Maybe, but legitimacy means content deals; while that may be a good thing for some, content deals also mean restrictions on what can be viewed, shared and downloaded. If there’s one thing a TPB user hates, it’s to be held up by business deals and red tape. There are plenty of other torrent sites out there, and TPB’s reformation is not going to stop people from getting their hands on the most current episodes of popular shows like Weeds, Dexter, Californication and The Office. Not to mention rogue copies of movies and music.

Graphic: Diego AguirreSay what you want about the Swedish outlaws, but the creators of TPB knew how to run a popular Website. Sure, it took on some water every now and then, but most of the time the site was up and running. TPB has been out of its creator’s hands for only a few hours, and the site has already dropped off the digital horizon. At the time of this writing, chúng tôi was returning a network timeout. Has TPB gone to Davy Jones’ locker already?

The Pirate Bay was primarily a conduit for file sharers to find each other, so the site really had no control over download speeds. That being said, today’s torrent technology could stand to go a little faster. TPB’s new owner has promised to speed things up for users, and has acquired Peerialism, a p2p technology specialty firm, to aid its task. We’ll see if the tech company can help TPB increase its speeds, and if it can’t, well then it’s the plank for you Peerialism.

5) No Streaming

Pirates Overboard

If piracy is still the life for you, there are many sites out there to keep you sailing like ActiveDots, ISOHunt, LookTorrent, Mininova, NowTorrents, and Torrentz to name just a few.

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