This event has been UPDATED since it was first published. View the UPDATED & Detailed PyTorch Training course for beginners Information here.

4 Weekends PyTorch Training is being delivered from December 26, 2020 - January 17, 2021 US Pacific time for 16 hours over  4 weekends, 8 sessions, 2 sessions per week, 2 hours per session.

• The medium of instruction is English
• All Published Ticket Prices are in US Dollars.

4 Weekends Only PyTorch Training Course Schedule

• December 26, 2020 - January 17, 2021 US Pacific time
• 4 Weekends | 2 hours on Saturdays, 2 hours on Sundays every weekend US Pacific time
• 7:30 AM - 9:30 AM US Pacific time each of those days
• Please click here to add your location and check your local date and time for first session to be held on December 26, 2020 at 7:30 AM US Pacific Time.

Course Objectives

This PyTorch training covers first the features of PyTorch, its pros and cons, and comparison with other alternatives, it then starts with the fundamentals of PyTorch. This training on PyTorch further covers Linear regression, Logistic regression, Neural networks, CNN, RNN, etc with the context of PyTorch

Prerequisites

If you do not have any of these prerequisite skills we will be happy to teach you these skills for additional cost before you take this training course.

• Python Programming Language
• Conditional statements
• Lists, Tuples in Python, dictionary
• Object-oriented programming
• List comprehension
• Generators in python

Who can take PyTorch Training Course?

• IT Professionals

Course Outline

1. Introduction to PyTorch

• Why PyTorch
• Tensorflow vs Pytorch vs Keras
• Matrix Basics
• Reproducibility
• GPU and CPU Toggling

2. PyTorch Fundamentals

• Basic Mathematical Tensor Operations
• PyTorch Tensors
• Two-Dimensional Tensors
• Variables and Gradients
• Practical Applications Using PyTorch

3. Linear Regression PyTorch Way

• What is Linear Regression
• Linear Regression in PyTorch
• Linear Regression From CPU to GPU in PyTorch
• Multiple Input Output Linear Regression

4. Logistic Regression with PyTorch

• Logistic Regression for classification
• Going deeper into Logistic Regression
• Logistic Regression with PyTorch
• From CPU to GPU in PyTorch

5. Basic Neural Network with PyTorch

• Hidden Layers
• Backpropagation
• Activation Functions
• Non-linearity
• Feedforward Neural Network in PyTorch
• More Feedforward Neural Network Models in PyTorch
• Feedforward Neural Network From CPU to GPU in PyTorch

6. Convolutional Neural Network (CNN) with PyTorch

• Feedforward Neural Network Transition to CNN
• One Convolutional Layer
• Multiple Convolutional Layers Overview
• Pooling Layers
• Padding for Convolutional Layers
• Output Size Calculation
• CNN in PyTorch
• More CNN Models in PyTorch
• Expanding CNN Model's Capacity
• From CPU to GPU in PyTorch
• Pre-trained CNNs

7. Recurrent Neural Networks (RNN) with PyTorch

• Introduction to RNN
• RNN in PyTorch
• More RNN Models in PyTorch
• RNN From CPU to GPU in PyTorch

8. Long Short-Term Memory Networks (LSTM) with PyTorch

• Introduction to LSTMs
• LSTM Equations
• LSTM in PyTorch
• More LSTM Models in PyTorch