This is a multi-part series of tutorials, in which we develop the mathematical and algorithmic underpinnings of deep neural networks from scratch and implement our own neural network library in Python, mimicing the TensorFlow API.
Start with the first part: I: Computational Graphs.
- Part I: Computational Graphs
- Part II: Perceptrons
- Part III: Training criterion
- Part IV: Gradient Descent and Backpropagation
- Part V: Multi-Layer Perceptrons
- Part VI: TensorFlow
Thanks for the great article!
Great stuff , very useful. Im learning AI, more interested in reinforcement learning.
Really great article… keep it up, Maybe you want to divide it into series of lecture, a blog for each algorithm.
Thanks for the tip, that’s what I’ve done now 🙂