PyTorch Tutorial – Neural Networks & Deep Learning in Python

Pytorch - Introduction to deep learning neural networks : Neural network applications tutorial : AI neural network model

Created by: Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

What Will I Learn?

• Deep Learning Basics - Getting started with Anaconda, an important Python data science environment
• Neural Network Python Applications - Configuring the Anaconda environment for getting started with PyTorch
• Introduction to Deep Learning Neural Networks - Theoretical underpinnings of the important concepts (such as deep learning) without the jargon
• AI Neural Networks - Implementing artificial neural networks (ANN) with PyTorch
• Neural Network Model - Implementing deep learning (DL) models with PyTorch
• Deep Learning AI - Implement common machine learning algorithms for Image Classification
• Deep Learning Neural Networks - Implement PyTorch based deep learning algorithms on imagery data

Requirements

• The Ability To Install the Anaconda Environment On Your Computer/Laptop
• Know how to install and load packages in Anaconda
• Interest in Learning to Process Image Data
• Basic Knowledge of Python Programming Syntax and Concepts is Needed to Follow the Code (e.g. functions and programming flows)
• Prior Exposure to Python Data Science Concepts Will be Useful

Target audience

• Students interested in using the Anaconda environment for Python data science applications
• Students interested in getting started with the PyTorch environment
• Students Interested in Implementing Machine Learning Algorithms using PyTorch
• Students Interested in Implementing Machine Learning Algorithms on Real Life Image Data
• Students Interested in Learning the Basic Theoretical Concepts behind Neural Networks techniques Such as Convolutional neural network
• Implement ANN on Real Data
• Implement Deep Neural Networks
• Implement Convolutional Neural Networks (CNN) on Imagery data
• Build Image Classifiers Using Real Imagery Data and Evaluate Their Performance
• Introduction to Transfer Learning