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

AI-POWERED

Explanations

AI-POWERED

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

Course Content

Learn Deep Learning

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

About this Course

Why Learn Deep Learning?

Overview of the Course

Neural Networks

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

Linear Classifiers Optimization and Gradient Descent Neural Networks

Backpropagation Algorithm

Build a Neural Network With Pytorch Quiz Yourself on Neural Networks

Training Neural Networks

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

Convolutional Neural Networks

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

The Principles of the Convolution Convolution in Practice Build a Convolutional Network Batch Normalization and Dropout Skip Connections CNN Architectures Quiz Yourself on CNNs

Recurrent Neural Networks

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

A Simple RNN Cell LSTM: Long Short Term Memory Cells Writing a Custom LSTM Cell in Pytorch Connect LSTM Cells Across Time and Space Quiz Yourself on RNNs

Autoencoders

5 Lessons

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

Generative Adversarial Networks

4 Lessons

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

Attention and Transformers

10 Lessons

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

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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Anthony Walker

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Emma Bostian 🐞

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