roatienza / Deep Learning Experiments
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Deep Learning Lecture Notes and Experiments
Advanced Deep Learning with Keras book) or contribute (Keras)
Code samples have links to other repo that I maintain (2020 Version
So much have changed since this course was offerred. Hence, it is time to revise. I will keep the original lecture notes at the bottom. They will no longer be maintained. I am introducing 2020 version. Big changes that will happen are as follows:
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Review of Machine Learning - Frustrated with the lack of depth in the ML part, I decided to develop a new course - Foundations of Machine Learning. Before studying DL, a good grasp of ML is of paramount importance. Without ML, it is harder to understand DL and to move it forward.
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Lecture Notes w/ Less Clutter - Prior to this version, my lecture notes have too much text. In the 2020 version, I am trying to focus more on the key concepts while carefully explaining during lecture the idea behind these concepts. The lecture notes are closely coupled with sample implementations. This enables us to quickly move from concepts to actual code implementations.
Lecture Notes and Experiments
- Course Roadmap
- Multilayer Perceptron (MLP)
- Lecture Notes
- Experiments:
- Convolutional Neural Network (CNN)
- Lecture Notes
- Experiments:
- Deep CNN
- Recurrent Neural Network (RNN)
- Lecture Notes
- Experiments:
- Transformer
- Lecture Notes
- Experiments:
- Regularizer
- Lecture Notes
- Experiments:
- Optimizer
- AutoEncoder
- Normalization
- Generative Adversarial Network (GAN)
- GAN
- Improved GAN
- Disentangled GAN
- Cross-Domain GAN
- Experiments: DCGAN , CGAN , WGAN , LSGAN , ACGAN , InfoGAN , CycleGAN
- Variational AutoEncoder (VAE)
- Lecture Notes
- Experiments:
- Object Detection
- Lecture Notes
- Experiments:
- Object Segmentation
- Lecture Notes
- Experiments:
Star, Fork, Cite
If you find this work useful, please give it a star, fork, or cite:
@misc{atienza2020dl,
title={Deep Learning Lecture Notes},
author={Atienza, Rowel},
year={2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/roatienza/Deep-Learning-Experiments}},
}
Lecture Notes (Old - will no longer be maintained)
- Course Roadmap
- Background Materials
- Machine Learning Basics
- Concepts, Capacity, Estimators, Linear Regression
- MLE, Bayesian, Other ML Algorithms
- Stochastic Gradient Descent, etc
- Deep Neural Networks
- Deep Feedforward Neural Networks, Cost, Output, Hidden Units
- Back Propagation
- PyTorch Sample Code
- Keras Sample Code
- Keras Sample Code
- Keras Sample Code
- Keras Sample Code
- Keras Sample Code
- Keras Sample Code
11a. Improved GANs
11b. Disentangled GAN
11c. Cross-Domain GAN
- Keras Sample Code
- Keras Sample Code