This repository contains -
✔️ Chapter-wise summarized notes.
✔️ Chapter-wise PDF.
✔️ Chapter-wise codes. (.ipynb files)
✔️ Summarized notes on Udacity's Nanodegree in AI (Bertelsmann Scholarship)
The images in this repository are taken from Udacity's Deep Learning Nanodegree program.
Over the course of time, I have enrolled in multiple MOOCs and read multiple books related to Deep Learning. I try to document all the important notes in one place so that it is easy for me to revise 😊.
Below are the list of projects/theorey that I have worked on/documented. Please see the Project List for the code and refer the Theorey List for the detailed explaination of various concepts.:
-
- Deep Learning with PyTorch - 60 minute blitz
- Verify PyTorch Installation
- Autograd Automatic Differentiation
- Single Layer Neural Network
- Neural Networks
- Multi-layer Neural Networks
- Implementing Softmax Function
- Training an Image Classifier
- Implementing ReLU Activation Function via PyTorch
- Playing with TensorBoard
- Training Neural Network via PyTorch
- Validation via PyTorch
- Regularization via PyTorch
- Loading Image Data via PyTorch
- Transfer Learning via PyTorch
-
- Naive Bayes Classifier
- POS Tagging
- Feature Extraction and Embeddings
- Topic Modelling
- Latent Dirichlet Allocation
- Sentiment Analysis
- Machine Translation
- Speech Recognition
- Autocorrect Tool via Minimum Edit Distance
- Autocomplete tool using n-gram language model
- Natural Language Generation
- Question Answering Models
- Text Classification
- Siamese Networks
This list basically contains summarized notes for each chapter from the book, 'Deep Learning' by 'Goodfellow, Benigo and Courville':
- Chapter 1: Linear Algebra
- Chapter 2: Probability and Information Theorey
- Chapter 3: Numerical Computation
- Chapter 4: Machine Learning Basics
- Chapter 5: Deep Forward Networks
5.1.Chapter 5.1: Back Propogation - Chapter 6: Regularization for Deep Learning
- Chapter 7: Optimization for Training Deep Models
- Chapter 8: Convolutional Neural Networks
- Chapter 9: Reccurent Neural Networks
9.1 Chapter 9.1: LSTMs
Please feel free to open a Pull Request to contribute towards this repository. Also, if you think there's any section that requires more/better explanation, please use the issue tracker to let me know about the same.
If you like this repo and find it useful, please consider (★) starring it (on top right of the page) so that it can reach a broader audience.