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Building a Neural Network in PyTorch

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Overview

Embark on a journey to understand and build simple neural networks using PyTorch. This course explores neural networks, including essential concepts like layers, neurons, activation functions, and training a model. You’ll grasp these elements through progressive, interlocking code examples, culminating in the construction and evaluation of a simple neural network model for binary classification.

Syllabus

Unit 1: Initializing a Neural Network Model in PyTorch

Running a Simple Neural Network in PyTorch
Changing Network's Input and Output Sizes
Correcting PyTorch Neural Network Definition
Initialize and Instantiate a PyTorch Model
Building a 2-Layer Neural Network

Unit 2: Building Networks Easily with Sequential Models in PyTorch

Running a Sequential Model in PyTorch
Fixing an Error in Sequential Model Creation
Extending Sequential Model with Additional Layer
Building a Three-Layer Sequential Model

Unit 3: Training a Neural Network Model with PyTorch

Running a Neural Network Training Loop
Modifying Neural Network Learning Rate
Fix Neural Network Training Code
Implementing Forward Pass in PyTorch
Mastering PyTorch Model Training

Unit 4: Making Predictions with a Trained PyTorch Model

Running PyTorch Model Predictions
Modifying Probability Threshold for Prediction
Fixing PyTorch Model Prediction
Transitioning Models to Evaluation Mode
Mastering PyTorch Model Predictions

Unit 5: Evaluating a Model with PyTorch

Running Model Evaluation in PyTorch
Evaluating with Precision Metric
Debugging PyTorch Model Evaluation
Evaluating PyTorch Model Performance
Evaluating PyTorch Models from Scratch

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