part serves as brief introduction to machine learningdeep learningand pytorch we explore the evolution of the fieldfrom early rule-based systems to the present-day sophisticated algorithmsin an accelerated fashion part ii explores the essential deep learning building blocks introduces simple feed-forward neural network incrementally and logicallywe uncover the various building blocks that constitute neural network and which can be reused in building any other network though foundational focuses on building baby neural network with the required framework that helps to construct and train networks of all kinds and complexities in we explore the core idea that enabled the possibility of training large networks through backpropagation using automatic differentiation and chain rule we explore pytorch' autograd module with small example to understand how the solution works programmatically in we look at orchestrating all the building blocks discussed through so faralong with the performance metrics of deep learning models and the artifacts required to enable an improved means for training-- regularizationhyperparameter tuningoverfittingunderfittingand model capacity finallywe leverage all this content to develop deep neural network for real-life dataset using pytorch in this exercisewe also explore additional pytorch constructs that help in the orchestration of various deep learning building blocks part iii covers three important topics within deep learning explores convolutional neural networks and introduces the field of computer vision we explore the core topics within convolutional neural networksincluding how they learn and how they are distinguished from other networks we also leverage few hands-on exercises--using small mnist dataset as well as the popular cats and dogs dataset--to study the practical implementation of convolutional neural network in we study recurrent neural networks and enter the field of natural language processing similar to we incrementally build an intuition xvi