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Learn moreWhat is AI Summer
AI Summer is a free educational platform covering research and applied trends in AI and Deep Learning. We provide accessible and comprehensive content from the entire spectrum of AI that aims to bridge the gap between researchers and the public.
Our mission is to simplify complex concepts and drive scientific research. We try to accomplish that by writing highly-detailed overviews of recent deep learning developments as well as thorough tutorials on popular frameworks.
But above all, we are a community that seeks to demystify the AI landscape and enable new technological innovations.
Deep Learning research
Simplified but technically informed overviews of recent research trends and deep learning breakthroughs. Our articles cover both popular concepts in depth as well as state-of-the-art algorithms. Learn more
Machine Learning infrastructure
Thorough and highly-detailed tutorials on popular AI libraries and frameworks. We discuss best practices and principles on how to use deep learning architectures on real-life projects. Learn more
Machine Learning fundamentals
Clear explanations and step-by-step guides of fundamental architectures and concepts from the machine learning literature. In most cases, code is also available. Learn more
AI community
An online community that collaborates on novel articles and open-source projects. If you are looking to co-author and publish an article on our platform, join us on Discord.
Explore our content
An Artificial Intelligenge hub where you can find and learn anything related to Deep Learning. From fundamental principles to state of art research and real-life applications
Attention and Transformers
Computer Vision
Generative Learning
Medical
Unsupervised Learning
How Attention works in Deep Learning: understanding the attention mechanism in sequence models
New to Natural Language Processing? This is the ultimate beginner’s guide to the attention mechanism and sequence learning to get you started
How Transformers work in deep learning and NLP: an intuitive introduction
An intuitive understanding on Transformers and how they are used in Machine Translation. After analyzing all subcomponents one by one such as self-attention and positional encodings , we explain the principles behind the Encoder and Decoder and why Transformers work so well
How the Vision Transformer (ViT) works in 10 minutes: an image is worth 16x16 words
In this article you will learn how the vision transformer works for image classification problems. We distill all the important details you need to grasp along with reasons it can work very well given enough data for pretraining.
Transformers in computer vision: ViT architectures, tips, tricks and improvements
Learn all there is to know about transformer architectures in computer vision, aka ViT.
Best deep CNN architectures and their principles: from AlexNet to EfficientNet
How convolutional neural networks work? What are the principles behind designing one CNN architecture? How did we go from AlexNet to EfficientNet?
How Neural Radiance Fields (NeRF) and Instant Neural Graphics Primitives work
Explore the basic idea behind neural fields, as well as the two most promising architectures (Neural Radiance Fields (NeRF) and Instant Neural Graphics Primitives)
How diffusion models work: the math from scratch
A deep dive into the mathematics and the intuition of diffusion models. Learn how the diffusion process is formulated, how we can guide the diffusion, the main principle behind stable diffusion, and their connections to score-based models.
GANs in computer vision - self-supervised adversarial training and high-resolution image synthesis with style incorporation
The fifth article-series of GAN in computer vision - we discuss self-supervision in adversarial training for unconditional image generation as well as in-layer normalization and style incorporation in high-resolution image synthesis.
The theory behind Latent Variable Models: formulating a Variational Autoencoder
Explaining the mathematics behind generative learning and latent variable models and how Variational Autoencoders (VAE) were formulated (code included)
Deep learning in medical imaging - 3D medical image segmentation with PyTorch
The basic MRI foundations are presented for tensor representation, as well as the basic components to apply a deep learning method that handles the task-specific problems(class imbalance, limited data). Moreover, we present some features of the open source medical image segmentation library. Finally, we discuss our preliminary experimental results and provide sources to find medical imaging data.
Introduction to 3D medical imaging for machine learning: preprocessing and augmentations
Learn how to apply 3D transformations for medical image preprocessing and augmentation, to setup your awesome deep learning pipeline
Deep learning in MRI beyond segmentation: Medical image reconstruction, registration, and synthesis
How can deep learning revolutionize medical image analysis beyond segmentation? In this article, we will see a couple of interesting applications in medical imaging such as medical image reconstruction, image synthesis, super-resolution, and registration in medical images
Grokking self-supervised (representation) learning: how it works in computer vision and why
A general perspective on understanding self-supervised representation learning methods.
Self-supervised learning tutorial: Implementing SimCLR with pytorch lightning
Learn how to implement the infamous contrastive self-supervised learning method called SimCLR. Step by step implementation in PyTorch and PyTorch-lightning
BYOL tutorial: self-supervised learning on CIFAR images with code in Pytorch
Implement and understand byol, a self-supervised computer vision method without negative samples. Learn how BYOL learns robust representations for image classification.
Explore our courses and books
Although 99% of our content is available for free, we do offer some paid courses and books. Why?
Because we need a way to cover hosting and other expenses. So you can consider buying them just to support our work.
However, we invest even more effort into our paid content in order to keep the quality as high as possible. Towards that goal, we try to a) maximize flow between concepts, b) minimize the external links and c) update them as frequently as possible.
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.
- How to design a deep learning system from scratch
- How to structure and develop production-ready machine learning code
- How to develop efficient and scalable data pipelines
- How to optimize the model’s performance and memory requirements
- How to make it available to the public by setting up a service on the cloud
- How to scale and maintain the service as the user base grows
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. The course is 100% text-based and is hosted in educative.io.
More articles
An overview of classifier-free diffusion guidance: impaired model guidance with a bad version of itself (part 2)
How to apply classifier-free guidance (CFG) on your diffusion models without conditioning dropout? What are the newest alternatives to generative sampling with diffusion models? Find out in this article!
An overview of classifier-free guidance for diffusion models
Learn more about the nuances of classifier-free guidance, the core sampling mechanism of current state-of-the-art image generative models called diffusion models.
ICCV 2023 top papers, general trends, and personal picks
Do you want to learn all the latest state-of-the-art methods of the last year? Learn about the best and most famous papers that made the cut from this year’s ICCV. See the latest trends in AI and computer vision.
A complete Apache Airflow tutorial: building data pipelines with Python
Learn about Apache Airflow and how to use it to develop, orchestrate and maintain machine learning and data pipelines
Understanding Vision Transformers (ViTs): Hidden properties, insights, and robustness of their representations
We study the learned visual representations of CNNs and ViTs, such as texture bias, how to learn good representations, the robustness of pretrained models, and finally properties that emerge from trained ViTs.
Learn Pytorch: Training your first deep learning models step by step
This blogpost is about starting learning pytorch with a hands on tutorial on image classification.
How Neural Radiance Fields (NeRF) and Instant Neural Graphics Primitives work
Explore the basic idea behind neural fields, as well as the two most promising architectures (Neural Radiance Fields (NeRF) and Instant Neural Graphics Primitives)
How diffusion models work: the math from scratch
A deep dive into the mathematics and the intuition of diffusion models. Learn how the diffusion process is formulated, how we can guide the diffusion, the main principle behind stable diffusion, and their connections to score-based models.
BYOL tutorial: self-supervised learning on CIFAR images with code in Pytorch
Implement and understand byol, a self-supervised computer vision method without negative samples. Learn how BYOL learns robust representations for image classification.