Project Name  Stars  Downloads  Repos Using This  Packages Using This  Most Recent Commit  Total Releases  Latest Release  Open Issues  License  Language 

T81_558_deep_learning  5,408  10 days ago  4  other  Jupyter Notebook  
Washington University (in St. Louis) Course T81558: Applications of Deep Neural Networks  
Deep Learning Coursera  5,253  4 years ago  24  mit  Jupyter Notebook  
Deep Learning Specialization by Andrew Ng on Coursera.  
Dockercheatsheet  3,462  a month ago  mit  
🐋 Docker Cheat Sheet 🐋  
Practical_dl  1,336  2 days ago  14  mit  Jupyter Notebook  
DL course codeveloped by YSDA, HSE and Skoltech  
Deep Learning Coursera  1,236  3 years ago  14  Jupyter Notebook  
Deep Learning Specialization by Andrew Ng, deeplearning.ai.  
Learn Blockchain By Building Your Own In Javascript  627  a year ago  6  JavaScript  
Code out your very own blockchain and decentralized network in the javascript programming language.  
Deeplearning.ai  503  5 years ago  6  Jupyter Notebook  
Some work of Andrew Ng's course on Coursera  
Pycadl  355  1  3 years ago  11  March 06, 2018  4  other  Python  
Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"  
Neural Networks And Deep Learning  325  2 years ago  6  Jupyter Notebook  
This is my assignment on Andrew Ng's course “neural networks and deep learning”  
Deeplearning.ai Note  199  a year ago  2  mit  Jupyter Notebook  
网易云课堂终于官方发布了吴恩达经过授权的汉化课程“”深度学习专项课程“”，这是自己做的一些笔记以及代码。下为网易云学习链接 
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.
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 ShortTerm 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.
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}
}
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

Module 2 Week of 01/30/2023 
Module 2: Python for Machine Learning

Module 3 Week of 02/06/2023 
Module 3: TensorFlow and Keras for Neural Networks

Module 4 Week of 02/13/2023 
Module 4: Training for Tabular Data

Module 5 Meet on 02/20/2023 
Module 5: Regularization and Dropout

Module 6 Week of 02/27/2023 
Module 6: CNN for Vision

Module 7 Week of 03/06/2023 
Module 7: Generative Adversarial Networks (GANs)

Module 8 Week of 03/20/2023 
Module 8: Kaggle

Module 9 Meet on 03/27/2023 
Module 9: Transfer Learning

Module 10 Week of 04/03/2023 
Module 10: Time Series in Keras

Module 11 Week of 04/10/2023 
Module 11: Natural Language Processing

Module 12 Week of 04/17/2023 
Module 12: Reinforcement Learning

Module 13 Meet on 04/24/2023 
Module 13: Deployment and Monitoring
