/Understanding-Neural-Networks

All the resources I am using to learn deep learning and neural networks.

Deep Learning and Neural Networks

We have seen how popular LLMs (infamous GPT) have got in recent years. Deep learning encompasses a broad range of techniques, including neural networks, which form the foundation for many state-of-the-art models like these.

I recently realised how important is to understand the underlying concepts of neural networks. It is essential to gain a comprehensive understanding of these technologies beyond merely utilizing pre-built libraries and frameworks.

Now, that I am starting from the basics, I thought of documenting my journey so that others can benefit too.

  • I've delved into various resources including YouTube videos, textbooks, and articles.

  • Below, I've compiled a collection of links to these resources, each offering valuable insights into the underlying concepts of neural networks and deep learning.

  • You'll find code implementations in the repository, organized into respective folders.

Books :

  1. Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow by Aurelien Geron. Perfect for beginners, the 2nd part is about Deep Learning.
  2. Neural Networks and Deep Learning by Michael Neilson. It's available for free online and covers all the core concepts with codes as well.

Web Links / Youtube :

  1. 3blue1brown: Basics and the maths behind the network learning. (Grant is the absolute best and his videos are just exceptional)
  2. StatQuest NeuralNetwork Playlist by Josh Starmer aka Mr BAM. His videos and visualizations are so easy to understand. He has covered all the core topics.
  3. Insightful articles on Deep Learning by Chris Olah