A part of this course is based on “Deep Learning with Python” by Francois Chollet. Subsequent sources will be added to the following section.
Additional Course Materials
- Andrew Ng - deeplearning.ai and Coursera course
- Deep learning course at Udacity
- Deep Learning textbook - Ian Goodfellow, Yoshua Bengio and Aaron Courville
- A review of optimization techniques for DL
- A review of Transfer learning
- Google Colab tutorial - An introduction to Google Colab, How to upload files? How to connect to Google Drive and resume training.
- Fully connected networks for predicting house prices, sentiment analysis, text classification, and image classification
- MNIST classification using ConvNets
- ConvNet for image classification, data augmentation.
- ConvNet pre-training
- ConvNet visualization: activations, filters, and heatmaps
- IMDB sentiment analysis using MLP with pre-trained embeddings, SimpleRNN, and LSTM
- Temperature forecasting problem using GRUs
- Text processing using 1D ConvNets
- Keras functional API
- Text generation with LSTMs
- MNIST with VAE
- GAN for frog generation
- TensorFlow 2 Tutorial (Official Page) - A set of tutorials, prepared by IUST, on TensorFlow 2 (basically a mini-course): It covers most of the required materials to start coding in TensorFlow, such as defining the computation, designing the model, working the input pipeline, and many more.