Course

Network Architectures

This course 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).

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Part of the Zero to GPT path.

4.8 (359 reviews)

550 learners enrolled in this course.

Intermediate friendly
10 hours
Self paced
3 lessons

“The learning paths on Dataquest are incredible. They give you a direction through the learning process – you don’t have to guess what to learn next.”

Otávio Silveira

Data Analyst @ Hortifruti

Network Architectures

Enroll For Free

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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