Pytorch Course And Certification

What is PyTorch?

PyTorch is an open source machine learning library that is based on the Torch library, which is used for several applications such as computer vision and NLP, primarily developed by the Facebook's AI Research lab (FAIR). It is a free and open-source software that is released under the Modified BSD license. Although the Python interface is way more polished and the primary focus of development, PyTorch also has a C++ web interface.

A number of pieces of Deep Learning software are developed on top of PyTorch, which includes Tesla Autopilot, Uber's Pyro, PyTorch Lightning, HuggingFace's Transformers, and Catalyst.

PyTorch makes use of a method called Automatic Differentiation. A recorder would record what operations have performed, and then it would replay it backward to compute the gradients. This method is especially very powerful when developing neural networks to save time on one epoch by calculating the differentiation of the parameters at the forward pass.

PyTorch autograd makes it very easy to define computational graphs and take the gradients, but raw autograd can be a bit too low-level for defining well complex neural networks. This is where the nn module can helpout .

Features of PyTorch

There are many features of Pytorch, and some of them are:

1. PRODUCTION READY: With TorchScript, PyTorch comes with ease-of-use and flexibility in an eager mode, while seamlessly transitioning to graph mode for fast speed, optimization, and functionality in the C++ runtime environments.

2. TORCHSERVE: TorchServe is an easy to use tool for deploying PyTorch models at high scale. It is both cloud and environment agnostic and it supports several features such as multi-model serving, logging, metrics and the development of RESTful API endpoints for the application integration.

3. DISTRIBUTED TRAINING: Well optimized performance in both research and production by taking advantage of several native support for asynchronous execution of organized and collective operations and peer-to-peer communication that is all accessible from Python and C++.

4. ROBUST ECOSYSTEM: An active community of researchers and developers have developed a rich ecosystem of tools and libraries for extending PyTorch and offering support for development in areas from computer vision to reinforcement learning.

Why Study PyTorch

There are many benefits of studying Pytorch, some of them are:

1. PyTorch is Pythonic

2. Easy to learn

3. Higher developer productivity 

4. Easy debugging 

5. Data Parallelism

6. Dynamic Computational Graph Support

7. Hybrid Front-End

8. Useful Libraries

9. Open Neural Network Exchange support 

10. Cloud support

11. Job opportunities and career advancement

12. Enrich your CV and increase your earning potential

PyTorch Course Outline: 

PyTorch - Introduction

PyTorch - Installation

PyTorch - Mathematical Building Blocks of Neural Networks

PyTorch - Neural Network Basics

PyTorch - Universal Workflow of Machine Learning

PyTorch - Machine Learning vs. Deep Learning

PyTorch - Implementing First Neural Network

PyTorch - Neural Networks to Functional Blocks

PyTorch - Terminologies

PyTorch - Loading Data

PyTorch - Linear Regression

PyTorch - Convolutional Neural Network

PyTorch - Recurrent Neural Network

PyTorch - Datasets

PyTorch - Introduction to Convents

PyTorch - Training a Convent from Scratch

PyTorch - Feature Extraction in Convents

PyTorch - Visualization of Convents

PyTorch - Sequence Processing with Convents

PyTorch - Word Embedding

PyTorch - Recursive Neural Networks

PyTorch - Video Lectures

PyTorch - Exams and Certification