HomeCoursesIntroduction to Deep Learning & Neural Networks

4.5

Intermediate

4h 30min

Updated 5 months ago

Introduction to Deep Learning & Neural Networks

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

Overview

Content

Reviews

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 architectural components of deep learning. Whether you’re a data and computer scientist, computer and big data engineer, solution architect, or software engineer, you will benefit from this course.

This course is an accumulation of well-grounded knowledge and experience in deep learning. It provides you with the basic concep...Show More

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

Understanding of the most popular Deep Learning models

TAKEAWAY SKILLS

Machine learning paradigms

Deep learning basics

PyTorch basics

Learning Roadmap

52 Lessons8 Quizzes1 Assessment11 Challenges

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

5 Lessons

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

Convolutional Neural Networks

7 Lessons

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

Recurrent Neural Networks

5 Lessons

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

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

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

AI Summer

Developed by MAANG Engineers

Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

Meet the Instructor

AI Summer

Software Engineer at Educative

Every Educative resource is designed by our team of ex-MAANG software engineers and PhD computer science educators — subject matter experts who've shipped production code at scale and taught the theory behind it. No videos, no fluff.

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