Introduction to Deep Learning & Neural Networks

Gain insights into basic and intermediate deep learning concepts, including CNNs, RNNs, GANs, and transformers. Delve into fundamental architectures to enhance your machine learning model training skills.

Intermediate

52 Lessons

4h 30min

Certificate of Completion

Gain insights into basic and intermediate deep learning concepts, including CNNs, RNNs, GANs, and transformers. Delve into fundamental architectures to enhance your machine learning model training skills.

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Explanations

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Explanations

This course includes

1 Assessment
24 Playgrounds
11 Challenges
8 Quizzes

This course includes

1 Assessment
24 Playgrounds
11 Challenges
8 Quizzes

Course Overview

This course is an accumulation of well-grounded knowledge and experience in deep learning. It provides you with the basic concepts you need in order to start working with and training various machine learning models. You will cover both basic and intermediate concepts including but not limited to: convolutional neural networks, recurrent neural networks, generative adversarial networks as well as transformers. After completing this course, you will have a comprehensive understanding of the fundamental ar...Show More

TAKEAWAY SKILLS

Machine Learning Paradigms

Deep Learning Basics

Pytorch Basics

What You'll Learn

Understanding of the most popular Deep Learning models

A solid grasp on the mathematics and the intuition behind the algorithms

A good experience with Deep Learning Programming and Pytorch

What You'll Learn

Understanding of the most popular Deep Learning models

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Course Content

1.

Learn Deep Learning

Get familiar with core deep learning concepts, models, hands-on exercises, and PyTorch tools.
2.

Neural Networks

Walk through neural networks, including classifiers, optimization, backpropagation, and PyTorch basics.
3.

Training Neural Networks

Work your way through optimizing and training neural networks using key algorithms and techniques.
4.

Convolutional Neural Networks

Grasp the fundamentals of CNNs, including principles, applications, architectures, and improvements.
5.

Recurrent Neural Networks

Take a closer look at RNNs, LSTMs, and custom implementation in PyTorch for sequential data.
6.

Autoencoders

5 Lessons

Investigate generative learning principles and explore autoencoders for data reconstruction and generation.
7.

Generative Adversarial Networks

4 Lessons

Practice using GANs to generate realistic data and evaluate with discriminators for robustness.
8.

Attention and Transformers

10 Lessons

Step through transformers, enhancing NLP tasks with self-attention, multi-head attention, and encoder-decoder mechanisms.
9.

Graph Neural Networks

5 Lessons

Discover the logic behind Graph Neural Networks' applications, mathematics, and implementation details.
10.

Conclusion

2 Lessons

Examine deep learning advancements, essential tools, datasets, and resources for future learning.

Final Quiz

Assessment

Course Author

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Emma Bostian 🐞

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Evan Dunbar

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Carlos Matias La Borde

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Anthony Walker

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Emma Bostian 🐞

@EmmaBostian

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