Introduction to Neural Networks and PyTorch

The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.

• Tensor and Datasets
• This module provides an overview of tensors and datasets. It will cover the appropriate methods to classify the type of data in a tensor and the type of tensor. You will learn the basics of 1D and 2-D tensors and the Numel method. Then you will learn to differentiate simple and partial derivatives. The module lists the different attributes that PyTorch uses in order to calculate a derivative. You will build a simple dataset class and object and a dataset for images. You will apply your learnings in labs and test your concepts in quizzes.
• Linear Regression
• This module describes linear regression. You will learn about classes, and how to build custom modules using nn.Modules to make predictions. Then you will explore the state_dict() method that returns a python dictionary. Then you will learn how to train the model, define a dataset and the noise assumption. You will further see how to minimize the cost and how to calculate loss using PyTorch. You will understand the Gradient Descent method and how to apply it on the cost function. You will learn to determine the bias and slope using the Gradient Descent method and define the cost surface. You will apply your learnings in labs and test your concepts in quizzes.
• Linear Regression PyTorch Way
• This module covers implementing stochastic gradient descent using PyTorch’s data loader. Then you will explore batch processing techniques for efficient model training. You will compare Mini-Batch Gradient Descent and Stochastic Gradient Descent. Next, you will learn about Convergence Rate and using PyTorch’s optimization modules. Finally, you will learn the best practices for splitting data to ensure robust model evaluation and how hyperparameters are applied to train data. You will apply your learnings in labs and test your concepts in quizzes.
• Multiple Input Output Linear Regression
• In this module, you will learn to use the class linear to perform linear regression in multiple dimensions. In addition, you will learn about model parameters and how to calculate cost and perform gradient descent in PyTorch. You will learn to extend linear regression for multiple outputs. You will apply your learnings in labs and test your concepts in quizzes.
• Logistic Regression for Classification
• In this module, you will learn the fundamentals of linear classifiers and logistic regression. You will learn to use the nn.sequential model to build neural networks in PyTorch. You will implement logistic regression for prediction. The module also covers statistical concepts like Bernoulli Distribution and Maximum Likelihood Estimation underpinning logistic regression. In addition, you will understand and implement the cross entropy loss function. You will apply your learnings in labs and test your concepts in quizzes.
• Practice Project and Final Project
• In this module, you will implement the final project applying all concepts learned. You will build a logistic regression model aimed at predicting the outcomes of League of Legends matches. Leveraging various in-game statistics, this project will utilize your knowledge of PyTorch, logistic regression, and data handling to create a robust predictive model.