Free Download Python Write Your Own Deep Learning Framework From Scratch
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 7h 29m | Size: 3.91 GB
Master Deep Learning by building a PyTorch-like framework with NumPy: Autograd Engine, MLP, CNN & RNN.
What you'll learn
How to write a deep learning framework using pure Python and NumPy code.
How to build a functional Autograd Engine from scratch.
Be able to implement core classes like Variable, Function, and Module.
Be able to build a tensor engine that supports broadcasting and matrix operations.
How to implement activation functions like ReLU, Sigmoid, and Softmax.
How to build a Data Pipeline including Dataset and DataLoader for mini-batch training.
Be able to implement Optimizers like Stochastic Gradient Descent (SGD).
How to train and evaluate models on the MNIST dataset.
How to implement Convolutional Neural Networks (CNN) from the ground up.
Be able to understand the im2col algorithm for convolutions.
How to implement Recurrent Neural Networks (RNN) from the ground up.
How to develop Sequential model support for Recurrent Neural Networks (RNN).
Requirements
Basic Python programming skills (familiarity with classes, functions, and NumPy basics).
Basic Calculus and Linear Algebra, specifically derivatives and the Chain Rule, matrix multiplication. If you're not a fan of math, you can simply follow the code to see how it works in action
Basic Deep Learning concepts: Knowing the basics of how models train and common architectures like CNNs and RNNs. We'll cover the basics, and more importantly, we'll take it a step further through learning by doing.
A curiosity to see how a deep learning framework is built and a willingness to follow along with the code.
No prior experience in deep learning framework development is required-we will build everything step by step.
Description
Welcome to Python: Write Your Own Deep Learning Framework From Scratch.This course teaches you how to build a simple, PyTorch-like deep learning framework from scratch. It covers the core mechanics of automatic differentiation and neural network abstractions. In this course, I will take you through the process of building a modular working system step by step, using only Python and NumPy.The first part of the course teaches all you need to know (computation graphs, backpropagation logic, gradient checking, etc.) before you can build a functional autograd engine. In this part, we start with scalar-valued variables and move on to handling complex logic, such as dealing with the same inputs and advanced operators. You will learn how to automate the chain rule and verify your engine's accuracy.The second part of the course teaches you how to transition from scalars to tensors. You will learn how to implement broadcasting, matrix multiplication, and shape manipulation. We will then restructure our code into a modular framework called NanoTorch. By the end of this part, you will implement essential framework components like Datasets, DataLoaders, and Optimizers to train models on the real-world MNIST dataset.The final part of the course focuses on implementing core neural network architectures. We will deep-dive into Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). You will see how to implement the im2col algorithm for efficient convolution and handle sequential data for time-series tasks. Ultimately, we will write fully functional CNN and RNN architectures from the ground up, ensuring an in-depth understanding of these powerful models.In this course you will learn:How to write a deep learning framework using pure Python and NumPy code.How to build a functional Autograd Engine from scratch.Be able to implement core classes like Variable, Function, and Module.Be able to build a tensor engine that supports broadcasting and matrix operations.How to implement activation functions like ReLU, Sigmoid, and Softmax.How to build a Data Pipeline including Dataset and DataLoader for mini-batch training.Be able to implement Optimizers like Stochastic Gradient Descent (SGD).How to train and evaluate models on the MNIST dataset.Be able to understand the im2col algorithm for convolutions.How to implement Convolutional Neural Networks (CNN) from the ground up.How to implement Recurrent Neural Networks (RNN) from the ground up.How to develop Sequential model support for Recurrent Neural Networks (RNN).At the end of the course, you should be able to develop your own deep learning framework and understand the low-level mechanics of deep learning structures.
Who this course is for
Students who learned deep learning concepts and want to put them into practice by building their own engine.
People curious about the fundamental mechanisms of automatic differentiation and how autograd engines work under the hood.
Students who want to build a hobby deep learning framework but don't know how and where to start.
Anyone who wants to fully understand how deep learning works by building every component from the ground up.
Developers who want to skip the math entirely and focus only on the code implementation to make it work.
People who are curious about how Deep Learning frameworks like PyTorch really work.
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