Course overview
This is the second course in a series of courses that will take you from no knowledge of deep learning to training your own GPT model (Generative Pre-trained Transformer). You’ll gain a deeper understanding of different network architectures, and you’ll learn to build neural networks from scratch using Python and to make predictions for both categorical and sequential outcomes.
After you have completed this course, you will understand different neural network architectures. You will be able to build a dense neural network from scratch using Python, train a neural network on a classification task, and predict sequence data using recurrent neural networks.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser!
Key skills
- Understand different neural network architectures
- Build a dense neural network from scratch using Python
- Train a neural network on a classification task
- Predict sequence data using recurrent neural networks
Course outline
Network Architectures [3 lessons]
Dense Networks 2h
Lesson Objectives- Implement backpropagation
- Implement gradient descent
- Build a dense neural network from scratch
Classification with Neural Networks 2h
Lesson Objectives- Train a neural network for binary classification
- Train a neural network for multiclass classification
Recurrent Networks 2h
Lesson Objectives- Build a recurrent neural network (RNN) from scratch.
- Make predictions using RNNs
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