A collection tutorial materials for the "SYSEN 5888/6888: Deep Learning" course at Cornell University.

An environment setup is provided here for all following tutorials including the installation of Python 3.8 and Jupyter through Anaconda distribution. An overview of the Python programming language is also included.

A tutorial of NumPy for array operations and different packages for visualization.

A tutorial of the installation and basics of Tensorflow and Keras. Development in the course is mainly built on Tensorflow==2.5.0

A tutorial of the basics of Convolutional Neural Network including operations and MNIST example

A tutorial of more complex Convolutional Neural Networks and relevant techniques and algorithms.

A tutorial of transfer learning including fine-tuning and Tensorflow Hub

A tutorial for You Only Look Once V3 (YOLO3v) for object detection and overview of image segmentation

An introduction to word embedding techniques including Text Vactorization, Skip-gram and Negative Sampling, Bag of Words, and Word2Vec

A text classification tutorial for training a recurrent and a convolutional neural networks on the IMDB large movie review dataset for sentiment analysis

A transformer Implementation tutorial training a sequence to sequence (seq2seq) model for Spanish to English translation

A tutorial for generative modeling with autoencoders with a basic convolutional autoencoder and variational autoencoder

A tutorial for generative modeling with generative adversarial networks for generating handwritten digits, translating image-to-image with a conditional GAN, translating unpaired Image-to-Image using Cycle-GAN

A Deep Reinforcement Learning tutorial with two examples on: playing CartPole with the actor-critic method, and Deep Deterministic Policy Gradient (DDPG) for the classic Inverted Pendulum control problem

A tutorial of Graph Neural Networks with Spektral and some best practices in Deep Learning including Keras functional API, monitoring deep-learning models using Keras callbacks and TensorBoard, hyperparameter optimization, model ensembling, multi-GPU and distributed training, and training Keras models with TensorFlow Cloud.