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Learn moreAI 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.
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
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
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
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.
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
New to Natural Language Processing? This is the ultimate beginner’s guide to the attention mechanism and sequence learning to get you started
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
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.
Learn all there is to know about transformer architectures in computer vision, aka ViT.
How convolutional neural networks work? What are the principles behind designing one CNN architecture? How did we go from AlexNet to EfficientNet?
Explore the basic idea behind neural fields, as well as the two most promising architectures (Neural Radiance Fields (NeRF) and Instant Neural Graphics Primitives)
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.
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.
Explaining the mathematics behind generative learning and latent variable models and how Variational Autoencoders (VAE) were formulated (code included)
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.
Learn how to apply 3D transformations for medical image preprocessing and augmentation, to setup your awesome deep learning pipeline
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
A general perspective on understanding self-supervised representation learning methods.
Learn how to implement the infamous contrastive self-supervised learning method called SimCLR. Step by step implementation in PyTorch and PyTorch-lightning
Implement and understand byol, a self-supervised computer vision method without negative samples. Learn how BYOL learns robust representations for image classification.
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.
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.
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.
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!
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.
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.
Learn about Apache Airflow and how to use it to develop, orchestrate and maintain machine learning and data pipelines
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.
This blogpost is about starting learning pytorch with a hands on tutorial on image classification.
Explore the basic idea behind neural fields, as well as the two most promising architectures (Neural Radiance Fields (NeRF) and Instant Neural Graphics Primitives)
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.
Implement and understand byol, a self-supervised computer vision method without negative samples. Learn how BYOL learns robust representations for image classification.