Deep Learning with PyTorch

This course advances from fundamental machine learning concepts to more complex models and techniques in deep learning using PyTorch. This comprehensive course covers techniques such as Softmax regression, shallow and deep neural networks, and specialized architectures, such as convolutional neural networks. In this course, you will explore Softmax regression and understand its application in multi-class classification problems. You will learn to train a neural network model and explore Overfitting and Underfitting, multi-class neural networks, backpropagation, and vanishing gradient. You will implement Sigmoid, Tanh, and Relu activation functions in Pytorch. In addition, you will explore deep neural networks in Pytorch using nn Module list and convolution neural networks with multiple input and output channels. You will engage in hands-on exercises to understand and implement these advanced techniques effectively. In addition, at the end of the course, you will gain valuable experience in a final project on a convolutional neural network (CNN) using PyTorch. This course is suitable for all aspiring AI engineers who want to gain advanced knowledge on deep learning using PyTorch. It requires some basic knowledge of Python programming and basic mathematical concepts such as gradients and matrices.

• Logistic Regression Cross Entropy Loss
• In this module, you will understand problem with mean squared error, and discuss maximum likelihood estimation. And then we'll see how to go from maximum likelihood estimation to calculating cross entropy loss, then Train the model PyTorch. You will apply your learnings in labs and test your concepts in quizzes.
• Softmax Regression
• In this module, you will learn how to use Lines to classify data and understand the working of the Softmax function. The module also covers the argmax function and its utilization. You will create a custom module for Softmax using the nn.module package in PyTorch and use a Softmax classifier to create a model for performing classifications. You will apply your learnings in labs and test your concepts in quizzes.
• Shallow Neural Networks
• In this module, you will create a neural network with a hidden layer using nn.Module and nn.Sequential. You will learn to train a neural network model and how neurons can improve a model. The model will also explain how to construct networks with multiple dimensional input in PyTorch. In addition, you will explore Overfitting and Underfitting, multi-class neural networks, back propagation and vanishing gradient. Finally, you will implement Sigmoid, Tanh and Relu activation functions in Pytorch. You will apply your learnings in labs and test your concepts in quizzes.
• Deep Networks
• This module provides an overview of deep neural network in Pytorch. You will learn to implement deep neural network in Pytorch using nn Module list. The module includes concepts like Dropout, layers, and weights. It will also discuss the problem of not initializing the Weights in a Neural Network model correctly and how to fix it. The module will also explore different initialization methods in Pytorch, gradient descent, and batch normalization. You will apply your learnings in labs and test your concepts in quizzes.
• Convolutional Neural Networks
• This module describes convolution and how to determine the size of the activation map. The module also covers activation functions and max pooling. In addition, the modaule discusses convolution with multiple input and output channels. It summarizes Convolutional Neural Network Constructor, Forward Step, and training in PyTorch. You will learn concepts like graphics processing units (GPUs), CUDA, residual network, and Resnet18. You will apply your learnings in labs and test your concepts in quizzes.
• Final Project
• In this module, you can complete a peer-reviewed final project to demonstrate and prove the skills you gained in the previous modules