Building Your First PyTorch Solution

سرفصل های دوره

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.

1. Course Overview

1. Course Overview

2. Installing PyTorch on a Local Machine

01. Version Check

02. Module Overview

03. Prerequisites and Course Outline

04. CUDA Support in PyTorch

05. Exploring PyTorch Install Options on a Local Machine

06. Setting up a Virtual Machine

07. Installing PyTorch with CPU Support Using Conda

08. Installing PyTorch with CPU Support Using Pip

09. Adding GPU Support to the VM and Installing the CUDA Toolkit

10. Installing PyTorch with GPU Support Using Conda

11. Installing PyTorch with CUDA Support Using Pip

12. Module Summary

3. Understanding Linear Regression with a Single Neuron

1. Module Overview

2. Linear Regression

3. Finding the Best Fit Line

4. Gradient Descent

5. Training a Simple Neural Network with One Neuron

6. Visualizing Regression Results and Compare with Regression Using scikit-learn

7. Preventing Overfitting Using Regularization

8. Performing Ridge Regression Using a Neural Network with One Neuron

9. Module Summary

4. Building a Regression Model Using PyTorch

01. Module Overview

02. Training a Neural Network Forward and Backward Passes

03. Optimizers

04. Building a Neural Network Using PyTorch Layers

05. Training a Neural Network Using Optimizers

06. Dropout

07. Epochs and Batches

08. Exploring the Bike Sharing Dataset

09. Using Datasets and Data Loaders in PyTorch

10. Building and Train a Neural Network for Bike Sharing Demand Prediction

11. Working with Different Neural Network Architectures

12. Module Summary

5. Building a Classification Model Using PyTorch

01. Module Overview

02. Softmax and Cross Entropy

03. Softmax and LogSoftmax

04. Evaluating Classifiers

05. Exploring the Graduate Admissions Dataset

06. Preprocessing the Data

07. Building a Custom Neural Network

08. Training and Evaluating the Neural Network

09. Customizing and Evaluating Different Models

10. Summary and Further Study

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تولید کننده: PluralSight