Read, Apply, Learn
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RNNs: Why Not Standard Networks?
In this article, I discuss the motivations behind the requirement of Recurrent Neural Networks outlining why we need them in the first place. This article is the second in the Sequence Models series. The next article discusses RNNs in greater detail. Basically, there are a couple of main problems that come up when using Standard…
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Sequence Models
Sequence models are a class of machine learning models designed for the tasks that involve sequential data, where the order of elements in the input is important. There are many kinds of sequential data, like textual data, time series data, audio signals, video streams or any ordered data. These sequences are usually of varying lengths…
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What is Machine Learning?
Machine Learning (ML) is an exciting and ever-evolving field that sits at the intersection of computer science and statistics, offering tools and techniques that enable computers to learn from and make decisions based on data. It’s a question that sparks curiosity widely – “What is Machine Learning?” – and it’s one that both beginners and…
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What is zk-SNARK?
Introduction In the fast-paced world of blockchain technology, privacy and scalability remain two critical challenges. However, with the emergence of zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARK), the blockchain industry has witnessed a groundbreaking solution to these obstacles. In this blog post, we will explore zk-SNARKs, their underlying principles, applications, and the transformative impact they…
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Generative AI: The Magic of AI
Imagine being able to create realistic images of people who don’t exist, catchy headlines for any topic, or original music in any style. Sounds like science fiction, right? Well, not anymore. Thanks to generative AI, a subfield of artificial intelligence that can generate new data instances that resemble human-created content, these tasks are now possible…
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How to Ace Categorical Variables in Data Science
Processing categorical variables in machine learning can be a complex and daunting task, but it is essential to unlock the full potential of your data. This article covers best practices for working with categorical variables, from encoding and transforming data to optimizing performance and achieving success. There are three main ways to handle categorical variables: Drop Categorical Variables This is the first and simplest method. It works by dropping categorical variables directly from…
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Handle Missing Data In Machine Learning
Dealing with missing data is a common challenge in machine learning, as it can have a significant impact on the accuracy and reliability of the model. There are several approaches to handling missing values, and it is important to choose the most appropriate one for your dataset and machine learning task. In this article, we…
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Configuring A Virtual Environment In Python
In my previous article, I mentioned installing virtualenv to isolate environments and using them instead of using the global python environment for specific projects. The process of installing and configuring virtual environments is fairly easy, but I instructed you guys to follow Corey Schafer’s video in order to do so. Well, here I explain similar…
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How To Install Python On MacOS Ventura 13.0.1+
Since macOS Ventura doesn’t come with the pre-installed python, many people face difficulty understanding why they can’t use python out of the box on their MacBook. I also met a similar issue which led me down this rabbit hole where I learned how to install python on my MacBook with macOS Ventura 13.0.1 and above,…
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TSP#3: Are You Craving For Attention?
Summary – Do not do things which are calculated just to get attention and nice comments. Getting nice comments is good because it indicates that you are not an outcast in the society. But at the same time, you have to do things that are for your own improvement. This letter by Seneca got me…
