A comprehensive course on becoming an AI researcher from scratch. This repository contains hands-on Jupyter notebooks covering the fundamental concepts needed to understand and implement neural networks and deep learning.
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- Math functions, derivatives, vectors, and gradients
- Matrix operations and linear algebra
- Probability and statistics
- Creating and manipulating tensors
- Matrix multiplication, transposing, and reshaping
- Indexing, slicing, and concatenating tensors
- Special tensor creation functions
- Building neurons, layers, and networks from scratch
- Normalization techniques (RMSNorm)
- Activation functions
- Optimizers (Adam, Muon) and learning rate decay
Install dependencies with:
pip install -r requirements.txtOpen and run the Jupyter notebooks in order, starting with 1_math/ and progressing through 2_pytorch/ to 3_neural_networks/.
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