Building PyTorch from Scratch: Tensor Operations, Transpose & Backward Method Explained

Learn how to build PyTorch from scratch! In this video, we implement transpose operation and the backward method for automatic differentiation. Understand how tensors track dependencies, compute gradients, and apply the chain rule for backpropagation.

MicroTorch Series Playlist:

Check the Jupyter Notebook:

Check my step-by-step MicroTorch tutorial:

Learn How To:
* Implement the transpose operation in both forward and backward modes
* Apply the chain rule in backpropagation for deep learning optimization
* Build the foundation for creating a neural network from scratch

Perfect for deep learning enthusiasts looking to dive deeper into PyTorch internals! Subscribe for the complete series where we'll expand our MicroTorch library with operations, optimizers, and more!

Join me on:

Timestamps:
00:07 - Introduction & Fixes from the Previous Chapter
02:01 - Reviewing Tensor Shape & Implementing Transpose Operation
07:10 - Fixing Issues & Running a Transpose Example
08:24 - Understanding the Chain Rule & Implementing the Backward Method
14:40 - Backpropagation in Action: Computing Gradients
16:44 - Summary & What's Next

#PyTorch #DeepLearning #Tensor #Backpropagation #AI #Python #MachineLearning