The Complete Neural Networks Bootcamp: Theory, Applications

This course is a comprehensive guide to Deep Learning and Neural Networks. The theories are explained in depth and in a friendly manner. After that, we'll have the hands-on session, where we will be learning how to code Neural Networks in PyTorch, a very advanced and powerful deep learning framework.

The course includes the following Sections:----------------------------------------------------Section 1 - How Neural Networks and Backpropagation WorksIn this section, you will deeply understand the theories of how neural networks and the backpropagation algorithm works, in a friendly manner. We will walk through an example and do the calculations step-by-step. We will also discuss the activation functions used in Neural Networks, with their advantages and disadvantages.

Section 2 - Loss FunctionsIn this section, we will introduce the famous loss functions that are used in Deep Learning and Neural Networks. We will walk through when to use them and how they work. Section 3 - OptimizationIn this section, we will discuss the optimization techniques used in Neural Networks, to reach the optimal Point, including Gradient Descent, Stochastic Gradient Descent, Momentum, RMSProp, Adam, AMSGrad, Weight Decay and Decoupling Weight Decay, LR Scheduler and others.

Section 4 - Weight InitializationIn this section,we will introduce you to the concepts of weight initialization in neural networks, and we will discuss some techniques of weights initialization including Xavier initialization and He norm initialization. Section 5 - Regularization TechniquesIn this section, we will introduce you to the regularization techniques in neural networks. We will first introduce overfitting and then introduce how to prevent overfitting by using regularization techniques, inclusing L1, L2 and Dropout.