Building-and-Optimizing-Neural-Networks-with-Pytorch
About
Building Neural Networks with PyTorch is a foundational aspect of deep learning, a branch of artificial intelligence that uses interconnected layers of mathematical computations to mimic the behavior of the human brain. This process leverages PyTorch, a versatile and dynamic framework, to design, train, and optimize neural networks for solving complex problems such as image recognition, natural language understanding, and predictive modeling.
Learning Objectives
You will be able to:
- Learn PyTorch basics (tensors, DataLoaders, Transforms)
- Define and implement deep neural networks (DNNs)
- Implement regression and classification models using PyTorch
- Explore model optimization: loss functions, gradient descent, and backpropagation.
Content
| Topic | About | ||
|---|---|---|---|
| Full Lesson Deck | To be used for the full session, includes the labs below | ||
| PyTorch Ecosystem | Introduction to PyTorch and its ecosystem | ||
| Building Basic Feed-Forward Neural Network Models | Hands-on implementation of regression and classification models | Optimization | Understanding loss functions, gradient descent, and backpropagation |
| Lab | Practical application and problem-solving session |
Prerequisites
- Basic Python programming
- Prior exposure to Machine Learning concepts (e.g. linear regression, logistic regression)
References
Important note
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