List Deep Learning From Scratch Curated By Cristian Leo Medium
A comprehensive repository dedicated to recreating popular machine learning models from scratch in Python, designed to facilitate deep understanding through practical examples. Explore the Documentation » View Demo · Report Bug · Request Feature This repository is aimed at providing an educational resource for those seeking to understand the inner workings of machine learning algorithms. While these implementations may not match the efficiency or completeness of mature libraries like scikit-learn, the focus here is on simplicity and clarity, allowing users to see the mechanics behind each algorithm. Each subfolder contains a demo.ipynb notebook that demonstrates a practical application of the corresponding algorithm. This includes loading a dataset, performing basic exploratory data analysis (EDA), and fitting the model to the data.
In addition to the code, a series of articles have been published that provide detailed explanations of each model. These articles cover the mathematical foundations, use cases, assumptions, advantages, and limitations of each algorithm, as well as a thorough breakdown of the corresponding Python implementation. For a deeper understanding, I highly recommend referring to these articles. Here is a collection of articles detailing the theory and implementation of each model in this repository: How PageRank transformed how we searched the internet, and why it’s still playing an important… Turn your Pandas Data Frame into a Knowledge Graph using LLMs.
Build your own LLM… Google, Microsoft, LinkedIn, and many more tech companies are using Graph RAG. Why? Let’s understand… Optimizing Sparse Neural Networks: Understanding Gradient Flow for Faster Training, and Better Performance in Deep… Deep Dive into Multi-Head Attention, the secret element in Transformers and LLMs.
Let’s explore its… I'm a passionate Data Scientist with expertise in machine learning and AI. Currently focused on developing innovative solutions at the intersection of AI and real-world applications. I love turning complex data into actionable insights and building scalable ML systems.
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A Comprehensive Repository Dedicated To Recreating Popular Machine Learning Models
A comprehensive repository dedicated to recreating popular machine learning models from scratch in Python, designed to facilitate deep understanding through practical examples. Explore the Documentation » View Demo · Report Bug · Request Feature This repository is aimed at providing an educational resource for those seeking to understand the inner workings of machine learning algorithms. While the...
In Addition To The Code, A Series Of Articles Have
In addition to the code, a series of articles have been published that provide detailed explanations of each model. These articles cover the mathematical foundations, use cases, assumptions, advantages, and limitations of each algorithm, as well as a thorough breakdown of the corresponding Python implementation. For a deeper understanding, I highly recommend referring to these articles. Here is a ...
Build Your Own LLM… Google, Microsoft, LinkedIn, And Many More
Build your own LLM… Google, Microsoft, LinkedIn, and many more tech companies are using Graph RAG. Why? Let’s understand… Optimizing Sparse Neural Networks: Understanding Gradient Flow for Faster Training, and Better Performance in Deep… Deep Dive into Multi-Head Attention, the secret element in Transformers and LLMs.
Let’s Explore Its… I'm A Passionate Data Scientist With Expertise
Let’s explore its… I'm a passionate Data Scientist with expertise in machine learning and AI. Currently focused on developing innovative solutions at the intersection of AI and real-world applications. I love turning complex data into actionable insights and building scalable ML systems.