Kickstart PyTorch in a Day
- Created By raju2006
- Last Updated September 12th, 2024
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
TensorFlow has become integral part of Machine and Deep Learning techniques. However, it is not the only game in town, there is another python library – PyTorch which is extremely popular amongst many. The objective of this class is lay down the fundamentals of using PyTorch to build Artificial Neural Networks. You will learn about the understanding what are tensors and how to work with them using PyTorch. Then we will transition into building a neural networks with PyTorch. You will learn on how to apply PyTorch for structured as well as unstructured data such as images.
Course Code/Duration:
BDT214 / 1 Day
Learning Objectives:
After this course, you will be able to:
- PyTorch introduction
- Working with Tensors
- Artificial Neural Network (Regression)
- Artificial Neural Network (Classification)
- Convolution Neural Networks with PyTorch
- Basic knowledge of python and fundamentals of Machines Learning.
- This course is designed for anyone interested to get started with using PyTorch and build Artificial Neural Networks. It is geared towards Data Scientists, Data Engineers, Software Engineers, Software Architects, Quality Assurance Engineers.
- This course is designed for anyone interested to get started with using PyTorch and build Artificial Neural Networks. It is geared towards Data Scientists, Data Engineers, Software Engineers, Software Architects, Quality Assurance Engineers.
Topic Outline:
PyTorch Introduction
- Machine Learning v/s Deep Learning what’s the difference?
- General information about PyTorch
- Lab: Quick review of NumPy and Pandas
Working with Tensors
- What are tensors?
- Creating Tensors
- Getting Tensor Attributes
- Manipulating Tensors
- Operations on Tensors
- NumPy & Tensors
- Hands-on lab with Tensors
Using PyTorch for Artificial Neural Network – Regression
- Learn to build a neural network for a regression problem
- Understand the basics of building a PyTorch model
- Selecting the loss and the optimizers for the model
- Handling categorical and datetime data
- Understand the use of metrices
- Hands-on labs with ANN for Regression
Using PyTorch for Artificial Neural Network – Classification
- Build a neural network for a classification problem
- Learn about the loss functions, metrics and optimizers used for Classification
- Hands-on lab with ANN for Classification
Convolutional Neural Networks with PyTorch
- Understand basic terminology of Convolutional Neural Network (CNN)
- Understand how to build a image processing model using ANN
- Build a CNN model on the same data set as above
- Hands-on lab with ANN and CNN for image processing
Hands-on Lab: Instructions will be provided to install Anaconda and PyTorch on student’s machines.
The curriculum is empty
[INSERT_ELEMENTOR id="19900"]
