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PyTorch Training

  • Beginner & Advanced level Classes.
  • PyTorch Programming With Practical Experience.
  • Presented by a 10+ Year of PyTorch Certified Expert.
  • Affordable Fees and Curriculum Created by Industry Experts.
  • Over 12,402 Students Have Received Training, With Over 350 Potential Recruits.
  • The Upcoming PyTorch Training Batch to Begin This Week –  Enroll Your Name Now!
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What is PyTorch?

PyTorch is an open-source Torch based Machine Learning library for natural language processing using Python. It is similar to NumPy but with powerful GPU support. It offers Dynamic Computational Graphs that you can modify on the go with the help of autograd. PyTorch is also faster than some other frameworks. It was developed by Facebook’s AI Research Group in 2016.

In this pytorch tutorial, you will learn all the concepts from scratch. This tutorial covers basic to advanced topics like pytorch definition, advantages and disadvantages of pytorch, comparison, installation, pytorch framework, regression, and image classification. This pytorch tutorial is absolutely free.

Syllabus of pytorch Training

Module 1: Introduction
  • Lesson 1 - PyTorch Prerequisites - Syllabus for Neural Network Programming Course
  • Lesson 2 - PyTorch Explained - Python Deep Learning Neural Network API
  • Lesson 3 - PyTorch Install - Quick and Easy
  • Lesson 4 - CUDA Explained - Why Deep Learning uses GPUs
Module 2: Introduction to Tensors
  • Lesson 1 - Tensors Explained - Data Structures of Deep Learning
  • Lesson 2 - Rank, Axes, and Shape Explained - Tensors for Deep Learning
  • Lesson 3 - CNN Tensor Shape Explained - Convolutional Neural Networks and Feature Maps
  • Lesson 4 - PyTorch Tensors Explained - Neural Network Programming
  • Lesson 5 - Creating PyTorch Tensors for Deep Learning - Best Options
Module 3: Introduction to Tensor Operations
  • Lesson 1 - Flatten, Reshape, and Squeeze Explained - Tensors for Deep Learning with PyTorch
  • Lesson 2 - CNN Flatten Operation Visualized - Tensor Batch Processing for Deep Learning
  • Lesson 3 - Tensors for Deep Learning - Broadcasting and Element-wise Operations with PyTorch
  • Lesson 4 - Code for Deep Learning - ArgMax and Reduction Tensor Ops
Module 4: Data and Data Processing
  • Lesson 1 - Dataset for Deep Learning - Fashion MNIST
  • Lesson 2 - CNN Image Preparation Code Project - Learn to Extract, Transform, Load (ETL)
  • Lesson 3 - PyTorch Datasets and DataLoaders - Training Set Exploration for Deep Learning
Module 4: Neural Networks and PyTorch Design
  • Lesson 1 - Build PyTorch CNN - Object Oriented Neural Networks
  • Lesson 2 - CNN Layers - PyTorch Deep Neural Network Architecture
  • Lesson 3 - CNN Weights - Learnable Parameters in PyTorch Neural Networks
  • Lesson 4 - Callable Neural Networks - Linear Layers in Depth
  • Lesson 5 - How to Debug PyTorch Source Code - Deep Learning in Python
  • Lesson 6 - CNN Forward Method - PyTorch Deep Learning Implementation
  • Lesson 7 - CNN Image Prediction with PyTorch - Forward Propagation Explained
  • Lesson 8 - Neural Network Batch Processing - Pass Image Batch to PyTorch CNN
  • Lesson 9 - CNN Output Size Formula - Bonus Neural Network Debugging Session
Module 5: Training Neural Networks
  • Lesson 1 - CNN Training with Code Example - Neural Network Programming Course
  • Lesson 2 - CNN Training Loop Explained - Neural Network Code Project
  • Lesson 3 - CNN Confusion Matrix with PyTorch - Neural Network Programming
  • Lesson 4 - Stack vs Concat in PyTorch, TensorFlow & NumPy - Deep Learning Tensor Ops
  • Lesson 4 - TensorBoard with PyTorch - Visualize Deep Learning Metrics
  • Lesson 4 - Hyperparameter Tuning and Experimenting - Training Deep Neural Networks
Module 6: Neural Network Experimentation
  • Lesson 1 - Training Loop Run Builder - Neural Network Experimentation Code
  • Lesson 2 - CNN Training Loop Refactoring - Simultaneous Hyperparameter Testing
  • Lesson 3 - PyTorch DataLoader num_workers - Deep Learning Speed Limit Increase
  • Lesson 4 - PyTorch on the GPU - Training Neural Networks with CUDA
  • Lesson 5 - PyTorch Dataset Normalization - torchvision.transforms.Normalize()
  • Lesson 6 - PyTorch DataLoader Source Code - Debugging Session
  • Lesson 7 - PyTorch Sequential Models - Neural Networks Made Easy
  • Lesson 8 - Batch Norm in PyTorch - Add Normalization to Conv Net Layers
  • Lesson 9 - Reset Weights PyTorch Network - Deep Learning Course
  • Lesson 10 - Training Multiple Networks - Deep Learning Course
  • Lesson 11 - Max Pooling vs No Max Pooling - Deep Learning Course
  • Lesson 12 - Deep Learning with PyTorch - Course Conclusion
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Summary of PyTorch

PyTorch is an open-source Torch based Machine Learning library for natural language processing using Python.


Advantages of PyTorch: 1) Simple Library, 2) Dynamic Computational Graph, 3) Better Performance, 4) Native Python


PyTorch uses Tensor for every variable similar to numpy’s ndarray but with GPU computation support.


One of the popular methods to learn the basics of deep learning is with the MNIST dataset.


Get Certified By PyTorch & Industry Recognized I.T. Ridge Certificate

All significant economic firms have accredited I.T. Ridge Credential. Following the completion of theoretical and practical workshops, we offer training either to freshmen and corporate trainees alike. The practice of testing application software produced for handheld mobile devices for functionality, usability, and consistency is known as mobile application testing. Tests of mobile applications might be automated or manual.

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