DeepLearning  by wangshusen

Deep learning lecture slides covering core ML concepts

created 6 years ago
4,013 stars

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Project Summary

This repository provides a comprehensive curriculum for Deep Learning, covering foundational machine learning concepts, various neural network architectures, and advanced topics. It is designed for students and practitioners seeking a structured learning path through lectures, slides, and video resources, with a particular emphasis on Keras for implementation.

How It Works

The curriculum is organized into distinct modules, starting with traditional machine learning and progressing through multilayer perceptrons, CNNs, RNNs, Transformers, Autoencoders, GANs, and Reinforcement Learning. It leverages a mix of theoretical explanations, practical implementation guidance (primarily via Keras), and visual aids to facilitate understanding of complex deep learning concepts and their applications in areas like computer vision and natural language processing.

Quick Start & Requirements

This repository is a collection of learning materials, not a runnable codebase. To follow along with practical examples, users will need to install Python and deep learning libraries such as TensorFlow/Keras. Specific hardware requirements (e.g., GPUs) will depend on the complexity of the models being implemented.

Highlighted Details

  • Covers a broad spectrum of deep learning topics from basics to advanced architectures like Transformers and Vision Transformers.
  • Includes extensive resources for RNNs and their applications in NLP, such as word embeddings, text generation, and machine translation.
  • Features dedicated sections on parallel computing, adversarial robustness, and meta-learning.
  • Provides links to both English and Chinese video lectures and supplementary reading materials.

Maintenance & Community

Information regarding maintenance, contributors, or community channels is not available in the provided README.

Licensing & Compatibility

The licensing information is not specified in the README.

Limitations & Caveats

This repository primarily serves as an educational resource and does not contain executable code for all topics. Practical implementation will require users to set up their own development environment and potentially adapt code examples. The availability and quality of linked external resources (videos, papers) may vary.

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Last commit

4 years ago

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Inactive

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