Course

Optimizing Network Parameters

In this course, you’ll deepen your understanding of backpropagation while building a basic deep learning framework. You’ll optimize network parameters and apply regularization to enhance model performance. This course is the third step in a series of courses that will take you on a journey from beginner to advanced deep learning practitioner.

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Part of the Zero to GPT path.

4.8 (359 reviews)

258 learners enrolled in this course.

Intermediate friendly
10 hours
Self paced
2 lessons

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Otávio Silveira

Data Analyst @ Hortifruti

Optimizing Network Parameters

Enroll For Free

Course overview

In this course, you’ll dive into the world of deep learning by gaining a deeper understanding of backpropagation and building a simple deep learning framework from scratch. As part of your journey, you’ll learn to optimize network parameters and employ regularization techniques to improve your models, creating a strong foundation in core deep learning concepts.

The course will also explore the essential role of optimizers in adjusting neural network parameters. You’ll delve into gradient descent and learn about batch size, learning rate schedules, weight decay, and momentum. Additionally, you’ll discover the popular Adam optimizer, which extends the idea of momentum, and learn how to optimize hyperparameters for neural networks while monitoring and comparing their performance.

By the end of this course, you’ll have a comprehensive understanding of fundamental deep learning concepts and be well-equipped to continue your deep learning journey.

Key skills

Build a 2-layer deep learning framework from scratch
Optimize network parameters
Prevent overfitting through regularization

Course outline

Optimizing Network Parameters [2 lessons]

Backpropagation 2h

Lesson Objectives

Build a computational graph
Compute the derivative of the softmax function
Build a 2-layer deep learning framework from scratch

Optimizers 2h

Lesson Objectives

Optimize hyperparameters for neural networks.
Monitor network performance and compare different runs to each other.
Implement the Adam optimizer.

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