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!
- 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
Network Architectures [3 lessons]
Dense Networks 2hLesson Objectives
- Implement backpropagation
- Implement gradient descent
- Build a dense neural network from scratch
Classification with Neural Networks 2hLesson Objectives
- Train a neural network for binary classification
- Train a neural network for multiclass classification
Recurrent Networks 2hLesson Objectives
- Build a recurrent neural network (RNN) from scratch
- Make predictions using RNNs
The Dataquest guarantee
Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.
We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.
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Go from zero to job-ready
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Work with real data from day one with interactive lessons and hands-on exercises.
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