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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.
4.6
52 Lessons
4h 30min
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- 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
Learning Roadmap
1.
Learn Deep Learning
Learn Deep Learning
Get familiar with core deep learning concepts, models, hands-on exercises, and PyTorch tools.
2.
Neural Networks
Neural Networks
Walk through neural networks, including classifiers, optimization, backpropagation, and PyTorch basics.
3.
Training Neural Networks
Training Neural Networks
5 Lessons
5 Lessons
Work your way through optimizing and training neural networks using key algorithms and techniques.
4.
Convolutional Neural Networks
Convolutional Neural Networks
7 Lessons
7 Lessons
Grasp the fundamentals of CNNs, including principles, applications, architectures, and improvements.
5.
Recurrent Neural Networks
Recurrent Neural Networks
5 Lessons
5 Lessons
Take a closer look at RNNs, LSTMs, and custom implementation in PyTorch for sequential data.
6.
Autoencoders
Autoencoders
5 Lessons
5 Lessons
Investigate generative learning principles and explore autoencoders for data reconstruction and generation.
7.
Generative Adversarial Networks
Generative Adversarial Networks
4 Lessons
4 Lessons
Practice using GANs to generate realistic data and evaluate with discriminators for robustness.
8.
Attention and Transformers
Attention and Transformers
10 Lessons
10 Lessons
Step through transformers, enhancing NLP tasks with self-attention, multi-head attention, and encoder-decoder mechanisms.
9.
Graph Neural Networks
Graph Neural Networks
5 Lessons
5 Lessons
Discover the logic behind Graph Neural Networks' applications, mathematics, and implementation details.
10.
Conclusion
Conclusion
2 Lessons
2 Lessons
Examine deep learning advancements, essential tools, datasets, and resources for future learning.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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.
ABOUT THE AUTHOR
AI Summer
AI Summer provides educational content about Deep Learning and Artificial Intelligence. We are trying to collect and organize all Deep Learning related information from mathematics and models to programming and machine learning infrastructure.
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Anthony Walker
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Evan Dunbar
ML Engineer
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Software Developer
Carlos Matias La Borde
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Souvik Kundu
Front-end Developer
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Vinay Krishnaiah
Software Developer
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