Building Your First PyTorch Solution
This course covers the important practical aspects of installing PyTorch from scratch on a variety of different platforms and getting going with classification and regression models.
What you'll learn
PyTorch is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease-of-use, and built-in support for optimized hardware such as GPUs. Using PyTorch, you can build complex deep learning models, while still using Python-native support for debugging and visualization.
In this course, Building Your First PyTorch Solution, you will gain the ability to get up and running by building your first regression and classification models.
First, you will learn how to install PyTorch using pip and conda, and see how to leverage GPU support. Next, you will discover how to hand-craft a linear regression model using a single neuron, by defining the loss function yourself. You will then see how PyTorch optimizers can be used to make this process a lot more seamless.
You will understand how different activation functions and dropout can be added to PyTorch neural networks. Finally, you will explore how to build classification models in PyTorch.
You will round out the course by extending the PyTorch base module to implement a custom classifier.
When you’re finished with this course, you will have the skills and knowledge to move on to installing PyTorch from scratch in a new environment and building models leveraging and customizing various PyTorch abstractions.
Table of contents
- Version Check 0m
- Module Overview 1m
- Prerequisites and Course Outline 2m
- CUDA Support in PyTorch 6m
- Exploring PyTorch Install Options on a Local Machine 2m
- Setting up a Virtual Machine 4m
- Installing PyTorch with CPU Support Using Conda 7m
- Installing PyTorch with CPU Support Using Pip 3m
- Adding GPU Support to the VM and Installing the CUDA Toolkit 5m
- Installing PyTorch with GPU Support Using Conda 3m
- Installing PyTorch with CUDA Support Using Pip 2m
- Module Summary 1m
- Module Overview 1m
- Linear Regression 4m
- Finding the Best Fit Line 4m
- Gradient Descent 5m
- Training a Simple Neural Network with One Neuron 6m
- Visualizing Regression Results and Compare with Regression Using scikit-learn 3m
- Preventing Overfitting Using Regularization 5m
- Performing Ridge Regression Using a Neural Network with One Neuron 5m
- Module Summary 2m
- Module Overview 1m
- Training a Neural Network Forward and Backward Passes 3m
- Optimizers 4m
- Building a Neural Network Using PyTorch Layers 5m
- Training a Neural Network Using Optimizers 2m
- Dropout 3m
- Epochs and Batches 2m
- Exploring the Bike Sharing Dataset 5m
- Using Datasets and Data Loaders in PyTorch 3m
- Building and Train a Neural Network for Bike Sharing Demand Prediction 5m
- Working with Different Neural Network Architectures 3m
- Module Summary 1m
- Module Overview 1m
- Softmax and Cross Entropy 4m
- Softmax and LogSoftmax 3m
- Evaluating Classifiers 2m
- Exploring the Graduate Admissions Dataset 5m
- Preprocessing the Data 4m
- Building a Custom Neural Network 5m
- Training and Evaluating the Neural Network 4m
- Customizing and Evaluating Different Models 5m
- Summary and Further Study 2m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more