README.md

Important Note

Current students of this course at Washington University should refer to the PyTorch version of this course, which is what is currently offered at the university. This repository contains the previous Keras/TensorFlow version.

T81 558:Applications of Deep Neural Networks - TensorFlow

Washington University in St. Louis

Instructor: Jeff Heaton

The content of this course changes as technology evolves, to keep up to date with changes follow me on GitHub.

• Section 1. Spring 2023, Monday, 2:30 PM, Location: Eads / 216
• Section 2. Spring 2023, Online

Course Description

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction.

Textbook

The complete text for this course is here on GitHub. This same material is also available in book format. The course textbook is “Applications of Deep Neural networks with Keras“, ISBN 9798416344269.

If you would like to cite the material from this course/book, please use the following BibTex citation:

@misc{heaton2020applications,
    title={Applications of Deep Neural Networks},
    author={Jeff Heaton},
    year={2020},
    eprint={2009.05673},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Objectives

1. Explain how neural networks (deep and otherwise) compare to other machine learning models.
2. Determine when a deep neural network would be a good choice for a particular problem.
3. Demonstrate your understanding of the material through a final project uploaded to GitHub.

Syllabus

This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

Module	Content
Module 1 Meet on 01/23/2023	Module 1: Python Preliminaries Part 1.1: Course Overview Part 1.2: Introduction to Python Part 1.3: Python Lists, Dictionaries, Sets & JSON Part 1.4: File Handling Part 1.5: Functions, Lambdas, and Map/ReducePython Preliminaries We will meet on campus this week! (first meeting)
Module 2 Week of 01/30/2023	Module 2: Python for Machine Learning Part 2.1: Introduction to Pandas for Deep Learning Part 2.2: Encoding Categorical Values in Pandas Part 2.3: Grouping, Sorting, and Shuffling Part 2.4: Using Apply and Map in Pandas Part 2.5: Feature Engineering in Pandas Module 1 Program due: 01/31/2023 Icebreaker due: 01/31/2023
Module 3 Week of 02/06/2023	Module 3: TensorFlow and Keras for Neural Networks Part 3.1: Deep Learning and Neural Network Introduction Part 3.2: Introduction to Tensorflow & Keras Part 3.3: Saving and Loading a Keras Neural Network Part 3.4: Early Stopping in Keras to Prevent Overfitting Part 3.5: Extracting Keras Weights and Manual Neural Network Calculation Module 2: Program due: 02/07/2023
Module 4 Week of 02/13/2023	Module 4: Training for Tabular Data Part 4.1: Encoding a Feature Vector for Keras Deep Learning Part 4.2: Keras Multiclass Classification for Deep Neural Networks with ROC and AUC Part 4.3: Keras Regression for Deep Neural Networks with RMSE Part 4.4: Backpropagation, Nesterov Momentum, and ADAM Training Part 4.5: Neural Network RMSE and Log Loss Error Calculation from Scratch Module 3 Program due: 02/14/2023
Module 5 Meet on 02/20/2023	Module 5: Regularization and Dropout Part 5.1: Introduction to Regularization: Ridge and Lasso Part 5.2: Using K-Fold Cross Validation with Keras Part 5.3: Using L1 and L2 Regularization with Keras to Decrease Overfitting Part 5.4: Drop Out for Keras to Decrease Overfitting Part 5.5: Bootstrapping and Benchmarking Hyperparameters Module 4 Program due: 02/21/2023 We will meet on campus this week! (second meeting)
Module 6 Week of 02/27/2023	Module 6: CNN for Vision Part 6.1: Image Processing in Python Part 6.2: Using Convolutional Networks with Keras Part 6.3: Using Pretrained Neural Networks Part 6.4: Looking at Keras Generators and Image Augmentation Part 6.5: Recognizing Multiple Images with YOLOv5 Module 5 Program due: 02/28/2023
Module 7 Week of 03/06/2023	Module 7: Generative Adversarial Networks (GANs) Part 7.1: Introduction to GANS for Image and Data Generation Part 7.2: Train StyleGAN3 with your Own Images Part 7.3: Exploring the StyleGAN Latent Vector Part 7.4: GANS to Enhance Old Photographs Deoldify Part 7.5: GANs for Tabular Synthetic Data Generation Module 6 Assignment due: 03/07/2023
Module 8 Week of 03/20/2023	Module 8: Kaggle Part 8.1: Introduction to Kaggle Part 8.2: Building Ensembles with Scikit-Learn and Keras Part 8.3: How Should you Architect Your Keras Neural Network: Hyperparameters Part 8.4: Bayesian Hyperparameter Optimization for Keras Part 8.5: Current Semester's Kaggle Module 7 Assignment due: 03/21/2023
Module 9 Meet on 03/27/2023	Module 9: Transfer Learning Part 9.1: Introduction to Keras Transfer Learning Part 9.2: Keras Transfer Learning for Computer Vision Part 9.3: Transfer Learning for NLP with Keras Part 9.4: Transfer Learning for Facial Feature Recognition Part 9.5: Transfer Learning for Style Transfer We will meet on campus this week! (third meeting) Module 8 Assignment due: 03/28/2023
Module 10 Week of 04/03/2023	Module 10: Time Series in Keras Part 10.1: Time Series Data Encoding for Deep Learning, Keras Part 10.2: Programming LSTM with Keras and Part 10.3: Text Generation with Keras Part 10.4: Introduction to Transformers Part 10.5: Transformers for Timeseries Module 9 Assignment due: 04/04/2023
Module 11 Week of 04/10/2023	Module 11: Natural Language Processing Part 11.1: Hugging Face Introduction Part 11.2: Hugging Face Tokenizers Part 11.3: Hugging Face Data Sets Part 11.4: Training a Model in Hugging Face Part 11.5: What are Embedding Layers in Keras Module 10 Assignment due: 04/11/2023
Module 12 Week of 04/17/2023	Module 12: Reinforcement Learning Kaggle Assignment due: 04/17/2023 (approx 4-6PM, due to Kaggle GMT timezone) Part 12.1: Introduction to the OpenAI Gym Part 12.2: Introduction to Q-Learning for Keras Part 12.3: Keras Q-Learning in the OpenAI Gym Part 12.4: Atari Games with Keras Neural Networks Part 12.5: Application of Reinforcement Learning
Module 13 Meet on 04/24/2023	Module 13: Deployment and Monitoring Part 13.1: Flask and Deep Learning Web Services Part 13.2: Interrupting and Continuing Training Part 13.3: Using a Keras Deep Neural Network with a Web Application Part 13.4: When to Retrain Your Neural Network Part 13.5: Tensor Processing Units (TPUs) Final Project due 05/08/2023 We will meet on campus this week! (fourth meeting)

Datasets

• Datasets can be downloaded here