Introduction to Deep Learning (I2DL)
Our website offers an open and free introductory course on deep learning. The course is constructed as self-contained as possible, and enables self-study through lecture videos, PDF slides, cheatsheets, exercises (with solutions), and notebooks in both R (TensorFlow) and Python (PyTorch).
The lecture aims at providing a basic theoretical and practical understanding of neural networks. First, we cover the necessary background on traditional artificial neural networks, backpropagation, online learning, and regularization. Then we explain special methods used in deep learning, like drop-out and rectified linear units. We also talk about further advanced topics like convolutional layers, recurrent neural networks, auto-encoders, and generative adversarial networks (GANs).
- Topic 1: Introduction
- Chapter 01.01: Introduction
- Chapter 01.02: Single Neuron
- Chapter 01.03: XOR Problem
- Chapter 01.04: Single Hidden Layer NN
- Chapter 01.05: Single Hidden Layer Networks for Multi-Class Classification
- Chapter 01.06: MLP: Multi-Layer Feedforward Neural Networks
- Chapter 01.07: Matrix Notation
- Chapter 01.08: Universal Approximation
- Chapter 01.09: Brief History
- Topic 2: Optimization - Part I
- Topic 03: Regularization
- Topic 04: Optimization - Part II
- Topic 05: Convolutional Neural Networks - Part I
- Topic 06: Convolutional Neural Networks - Part II
- Topic 07: Deep Recurrent Neural Networks
- Chapter 08: Autoencoders
- Topic 09: Generative Adversarial Neural Networks