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Everything you need to know about Deep Learning

At AI Summer you can learn everything about Deep Learning from scratch. Start with fundamental concepts and basic architectures, continue with advanced topics and real-life applications, and finally deploy your model to the world

I’m new to Deep Learning

Deep Learning Fundamentals

Topics

Example articles

Recurrent neural networks: building a custom LSTM cell
In-layer normalization techniques for training very deep neural networks
How Attention works in Deep Learning: understanding the attention mechanism in sequence models
How Transformers work in deep learning and NLP: an intuitive introduction
Best deep CNN architectures and their principles: from AlexNet to EfficientNet
Intuitive Explanation of Skip Connections in Deep Learning
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I have some Deep Learning experience

Advanced Deep Learning

Topics

Example articles

Deep learning in medical imaging - 3D medical image segmentation with PyTorch
GANs in computer vision - Introduction to generative learning
Self-supervised representation learning on videos
The theory behind Latent Variable Models: formulating a Variational Autoencoder
How Graph Neural Networks (GNN) work: introduction to graph convolutions from scratch
Vision Language models: towards multi-modal deep learning
How diffusion models work: the math from scratch
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I am a Software Engineer and I'm interested in ML infrastructure

AI Software Engineering

Topics

Example articles

Data preprocessing for deep learning: How to build an efficient big data pipeline
Deploy a Deep Learning model as a web application using Flask and Tensorflow
Introduction to Kubernetes with Google Cloud: Deploy your Deep Learning model effortlessly
Understanding einsum for Deep learning: implement a transformer with multi-head self-attention from scratch
How distributed training works in Pytorch: distributed data-parallel and mixed-precision training
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Discover our books and courses

If you prefer a more structured approach with minimal external links, you can refer to our courses and books.

Deep Learning in Production Book

This book will teach you how to build, train, deploy, scale and maintain deep learning models. You will understand ML infrastructure and MLOps using hands-on examples with Tensorflow, Flask, Docker, Kubernetes, Google Cloud and more. Learn more

Introduction to Deep Learning & Neural Networks interactive course

This course is a higly-interactive, hands-on introduction into the most popular deep learning architectures. It will help you learn the intuition and the mathematics behind deep learning and will provide you with practical experience in Pytorch. Learn more