PyTorch Fundamentals
- Offered byMicrosoft
PyTorch Fundamentals at Microsoft Overview
Duration | 3 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
PyTorch Fundamentals at Microsoft Course details
- Introduction to PyTorch
- Introduction to Computer Vision with PyTorch
- Introduction to Natural Language Processing with PyTorch
- Introduction to audio classification with PyTorch
- Learn the fundamentals of deep learning with PyTorch
- This beginner friendly learning path will introduce key concepts to building machine learning models in multiple domains include speech, vision, and natural language processing
- Learn key concepts used to build machine learning models with PyTorch
- We will train a neural network model that recognizes and classifies images
- We'll learn about different computer vision tasks and focus on image classification, learning how to use neural networks to classify handwritten digits, as well as some real-world images, such as photographs of cats and dogs
- In this course, we will explore different neural network architectures for dealing with natural language texts
- We will learn about different NLP techniques such as using bag-of-words (BoW), word embeddings and recurrent neural networks for classifying text from news headlines to one of the 4 categories (World, Sports, Business and Sci-Tech)
PyTorch Fundamentals at Microsoft Curriculum
Introduction to PyTorch
Introduction
What are Tensors?
Loading and normalizing datasets
Building the model layers
Automatic differentiation
Learn about the optimization loop
Load and run model predictions
The full model building process
Summary
Introduction to Computer Vision with PyTorch
Introduction
Introduction to processing image data
Training a simple dense neural network
Use a convolutional neural network
Train multi-layer convolutional neural network
Use a pre-trained network with transfer learning
Solving vision problems with MobileNet
Summary
Introduction to Natural Language Processing with PyTorch
Introduction
Representing text as Tensors
Bag of Words and TF-IDF
Represent words with embeddings
Capture patterns with recurrent neural networks
Generate text with recurrent networks
Summary
Introduction to audio classification with PyTorch
Introduction
Understand audio data and concepts
Audio transforms and visualizations
Build the speech model
Summary
PyTorch Fundamentals at Microsoft Entry Requirements
- Basic Python knowledgeBasic knowledge about how to use Jupyter NotebooksBasic understanding of machine learning
- Not mentioned
PyTorch Fundamentals at Microsoft Admission Process
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