In this first chapter, you will be focusing on obtaining a high-level overview of the deep learning field. At this point, we don't recommend you to jump into coding yet. The recommendations below will help you to understand a bit of where the field is and help you understand the motivations behind deep learning methods and their applications. All resources shared below are open and should be accesible to everyone. If you have any issues accessing any of the resources please open an issue.
- Deep learning (by Yann LeCun, Yoshua Bengio, Geoffrey Hinton)
- Deep Learning in Neural Networks: An Overview (by Jürgen Schmidhuber)
- Introduction to Deep Learning (by Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola)
- Introduction to Deep Learning (by Ian Goodfellow and Yoshua Bengio and Aaron Courville)
- Deep Learning: Our Miraculous Year 1990-1991 (by Jürgen Schmidhuber)
- Deep Learning Papers Reading Roadmap (by Flood Sung)
- Neural Networks and Deep Learning - Complete 4 weeks (by deeplearning.ai)
- MIT Deep Learning Basic: Introducton and Overview (by Lex fridman)
- Neural Networks Foundations (by DeepMind)
- History, motivation, and evolution of Deep Learning (by Yann LeCun)
- But what is a Neural Network? (by 3Blue1Brown)
- We don't recommend any code implementations for this particular section.
- Neural Networks and Deep Learning(by Michael Nielsen)
- Deep Learning (by Ian Goodfellow and Yoshua Bengio and Aaron Courville)
- Dive into Deep Learning (by Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola)