Machine Learning
445 learners
Building a Neural Network in PyTorch
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.
Python
PyTorch
Scikit-learn
5 lessons
24 practices
3 hours
Deep Learning and Neural Networks
Lessons and practices
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
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
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
Running PyTorch Model Predictions
Modifying Probability Threshold for Prediction
Fixing PyTorch Model Prediction
Transitioning Models to Evaluation Mode
Mastering PyTorch Model Predictions
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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