Practical Deep Learning & Artificial Neural Nets with Python

Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems.

Created by: Mike X Cohen
Educator and writer

What Will I Learn?

• The theory and math underlying deep learning
• How to build artificial neural networks
• Architectures of feedforward and convolutional networks
• Building models in PyTorch
• The calculus and code of gradient descent
• Fine-tuning deep network models
• Learn Python from scratch (no prior coding experience necessary)
• How and why autoencoders work
• How to use transfer learning
• Improving model performance using regularization
• Optimizing weight initializations
• Understand image convolution using predefined and learned kernels
• Whether deep learning models are understandable or mysterious black-boxes!
• Using GPUs for deep learning (much faster than CPUs!)

Requirements

• Interest in learning about deep learning!
• Python/Pytorch skills are taught in the course
• A Google account (google-colab is used as the Python IDE)

Target audience

• Students in a deep learning course
• Machine-learning enthusiasts
• Anyone interested in mechanisms of AI (artificial intelligence)
• Data scientists who want to expand their library of skills
• Aspiring data scientists
• Scientists and researchers interested in deep learning