A rigorous scientific and mathematical approach to mastering neural networks, moving from basic perceptrons to advanced architectures. Covers gradient descent, backpropagation, and specialized networks like CNNs and RNNs using an experimental learning style.
LEARNING_IN_PROGRESS
Learning In Progress
Instructor
Mike X Cohen
Duration
57.5 hours
Platform
Udemy
Status
Learning
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!)
