T81_558_deep_learning

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T81-558 is an open-source educational repository for a deep learning course at Washington University in St. Louis. It provides materials for applying deep neural networks using Keras and TensorFlow.

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Free · Open Source

Free tier

Yes

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Technical

About

What is t81_558_deep_learning?

T81-558 is a comprehensive GitHub repository containing teaching materials for the T81-558: Keras - Applications of Deep Neural Networks course offered at Washington University in St. Louis. This resource focuses on the Keras/TensorFlow version of the curriculum, covering a wide array of deep learning topics. Students and enthusiasts can explore modules on Python preliminaries, Pandas for machine learning, TensorFlow and Keras fundamentals, training for tabular data, regularization, CNNs for vision, Generative Adversarial Networks (GANs), Kaggle competitions, transfer learning, time series analysis, reinforcement learning, and deploying models with Flask. The repository includes Jupyter notebooks for practical application and a complete textbook available on GitHub, making it an invaluable resource for learning and applying deep neural network concepts.

Best used for

Ideal for students who need to learn deep learning concepts, professors who need comprehensive course materials, and individuals looking to apply neural networks using Keras and TensorFlow. Especially valuable for those seeking practical, hands-on examples through Jupyter notebooks and a structured curriculum.

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learn deep learning

apply neural networks

study machine learning

teach AI concepts

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Capabilities

Key features

Keras/TensorFlow course materials
Jupyter notebooks
CNNs for vision
Generative Adversarial Networks
Reinforcement learning examples
Flask model deployment
Tabular data training

Target Audience

studentprofessorteacher

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Not yet documented

Pricing & Plans

Free · Open Source

Free

FAQs

Is this course material suitable for beginners in Python?

While familiarity with at least one programming language is assumed, the course includes Python preliminaries (Module 1) to get students up to speed. It covers essential Python concepts like data structures, file handling, and functional programming, making it accessible for those new to Python but experienced in other languages.

Does this repository cover the latest version of the Washington University deep learning course?

No, this repository contains the Keras/TensorFlow version of the course. Current students at Washington University are advised to refer to the PyTorch version, which is what is currently offered. This repository serves as an archive for the previous Keras/TensorFlow curriculum.

What deep learning architectures are covered in this course?

The course introduces classic neural network structures, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), Generative Adversarial Networks (GANs), and reinforcement learning. It applies these to computer vision, time series, security, natural language processing (NLP), and data generation.

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