Deep Learning Study Group
This study group focuses on learning more about deep learning. It's split between two areas -
- Fundamentals: We will work through academic textbooks and exercises so that we command strong theoretical foundations for neural networks and deep learning. Topics will cover calculus, algebra, probability, and computer science.
- Applications: We will develop hands-on experience building deep learning models. Initially, we’ll follow tutorials then we’ll move on to solving novel and illustrative data problems involving a broad range of techniques.
Initially, we'll focus on covering material in Michael Nielsen's book Neural Networks and Deep Learning.
- Read the first chapter of Michael Nielsen’s e-book and complete the exercises.
- Install and configure TensorFlow
- Work through this beginner TensorFlow tutorial involving the MNIST data set, which is also discussed in Nielsen’s first chapter
- Clone this repository and add the material in a
week1folder