A deep understanding of deep learning (with Python intro)

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! Whether deep learning models are understandable or mysterious black-boxes! Deep learning is increasingly dominating technology and has major implications for society. From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology. But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications.