A deep understanding of deep learning (with Python intro), is a deep learning course in PyTorch using a scientific and practical method with many examples and practical problems. By taking this course, you will gain a flexible, principled and lasting experience in deep learning. You will learn the basic concepts with the help of this course and you will be able to easily learn new topics and trends in the future.
Note that this course is not just a summary of in-depth learning, but instead comprehensively examines why and how in-depth learning works and the many important concepts involved.
What you will learn in A deep understanding of deep learning (with Python intro):
Theory and mathematics behind deep learning
Build an artificial neural network
Feedforward and convolutional network architecture
Construction of models in the base of Torch
Differential and gradient descent coding
Scaling deep network models
Learn Python from scratch (without any coding experience)
How and why autoencoders work
Use transfer learning
Improve the performance of models using regularization
Weight optimization
Understand image convolution using pre-trained kernels
Use GPU for deep learning
