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Introduction to Deep Learning & Neural Networks - AI-Powered Course
4.4
Intermediate
4h 30min
Updated 2 weeks 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.
Join 2.8M developers at
Overview
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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
Show more
TAKEAWAY SKILLS
Machine learning paradigms
Deep learning basics
PyTorch basics
Content
52 Lessons8 Quizzes1 Assessment11 Challenges
1.
Learn Deep Learning
3 Lessons
Get familiar with core deep learning concepts, models, hands-on exercises, and PyTorch tools.
2.
Neural Networks
6 Lessons
Walk through neural networks, including classifiers, optimization, backpropagation, and PyTorch basics.
3.
Training Neural Networks
5 Lessons
Work your way through optimizing and training neural networks using key algorithms and techniques.
4.
Convolutional Neural Networks
7 Lessons
Grasp the fundamentals of CNNs, including principles, applications, architectures, and improvements.
5.
Recurrent Neural Networks
5 Lessons
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.
Certificate of Completion
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Course Author:
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.
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