An introduction to Deep Learning

This repository contains the study plan for learning essential concepts in machine learning, deep learning, and advanced applications. Each session builds upon the previous one, providing a comprehensive foundation in developing and implementing machine learning models and deep neural networks.

Table of Contents

1. Programming for deep learning basics
2. Machine Learning and Experiment Design
3. Introduction to NLP
4. Introduction to Computer Vision
5. Deep Learning with TensorFlow
6. Introduction to PyTorch
7. Introduction to ANN in PyTorch
8. Backpropagation
9. Loss Functions and Optimization
10. Callbacks & Regularization
11. Recurrent Neural Networks (RNN)
12. Quantum Computing
13. Paper Study: GANs / Autoencoders - Part 1
14. Paper Study: GANs / Autoencoders - Part 2
15. Paper Study: GANs / Autoencoders - Part 3

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Bibliography

Machine Learning

• Introduction to Machine Learning by Ethem Alpaydin
  An accessible introduction to machine learning concepts and methods, covering supervised, unsupervised, and reinforcement learning.

• The Hundred-Page Machine Learning Book by Andriy Burkov
  A concise and practical guide to the key concepts of machine learning, suitable for beginners and practitioners looking to refresh their knowledge.

• Learning from Data by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin
  A foundational book focused on the principles of learning from data, with practical insights and theoretical foundations.

Deep Learning

• Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
  A comprehensive reference on deep learning, covering the fundamentals, advanced topics, and practical techniques.

• Understanding Deep Learning
  An in-depth book that explores the theory and practice of deep learning, offering insights into how deep neural networks work.

• Neural Networks and Learning Machines (Third Edition) by Simon Haykin
  A classic resource on neural networks, covering various architectures, learning algorithms, and applications.

• Perceptrons by Marvin Minsky and Seymour Papert
  A seminal work on the early concepts of neural networks, introducing the perceptron model and discussing its limitations.

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Additional Resources

• Scikit-learn Documentation
• TensorFlow Documentation
• PyTorch Documentation
• Introduction to Quantum Computing

This repository will be updated with additional resources and practical examples for each topic.

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