# Slides for classes taught by Adi

Lecture 1 | Neural Networks and Deep Learning | Google Slides | |

Lecture 2 | Convolutional Neural Networks | Google Slides | |

Lecture 3 | Recurrent Neural Networks | Google Slides | |

Lecture 4 | Object localization and detection | Google Slides | |

Lecture 5 | Generative Adversarial Networks | Google Slides | |

Lecture 6 | Semantic Perceptual Image Compression | Google Slides | |

Lecture 7 | Neural Networks with Memory | Google Slides |

# Other presentations by Adi on Deep Learning

- General purpose GPU Computing and CUDA - Nov 30, 2015
- RNN, LSTM, NTM, GRU, RMVA, OMG !! - Nov 18, 2015
- Value of a feature - October 19, 2015
- Why CNN works - October 7, 2015
- Deeper inside CNN - September 29, 2015
- Inside CNN - September 16, 2015
- Deep Learning and CNN - February 22, 2016

(Very basics of Deep learning and CNN, meant as a gentle introduction to the field) - Neural Networks and Deep Learning - May 2, 2016
- Visual Question Answering - August 12, 2016

# Reading Material (Highly recommeded)

- Part 1: Nonlinear Classifiers and The Backpropagation Algorithm
- Part 2: Autoencoders, Convolutional Neural Networks and Recurrent Neural Networks

# Deep Learning References

## Courses

- UVA Deep Learning course. Contains easy to follow class video lectures, covers most of deep learning.
- CS231n: Convolutional Neural Networks for Visual Recognition Covers CNN only but in depth.

## Textbook

- Deep Learning. Very thorough and well written book. Covers almost everything.
- Neural Networks and Deep Learning. Light reading, only 6 chapters, easy to follow.
- Machine Learning. For better understanding of ML concepts, easy to follow.