📖 You can now grab a copy of our new Deep Learning in Production Book 📖
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
Convolutional Neural Networks
Medical
MLOps
Software
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.
Understanding the receptive field of deep convolutional networks
An intuitive guide on why it is important to inspect the receptive field, as well as how the receptive field affect the design choices of deep convolutional networks.
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?
Intuitive Explanation of Skip Connections in Deep Learning
What are skip connections, why we need them and how they are applied to architectures such as ResNet, DenseNet and UNet.
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
Introduction to Kubernetes with Google Cloud: Deploy your Deep Learning model effortlessly
What is Kubernetes? What are the basic principles behind it? Why it might be the best option to deploy Machine Learning applications? What features it provides to help us maintain and scale our infrastructure? How to set up a simple Kubernetes cluster in Google cloud?
How to use Docker containers and Docker Compose for Deep Learning applications
Learn how to containerize a deep learning model using Docker. Start with the basic concepts behind containers, package a Tensorflow application with Docker and combine multiple images using Docker compose
Distributed Deep Learning training: Model and Data Parallelism in Tensorflow
How to train your data in multiple GPUs or machines using distributed methods such as mirrored strategy, parameter-server and central storage.
A complete Hugging Face tutorial: how to build and train a vision transformer
Learn about the Hugging Face ecosystem with a hands-on tutorial on the datasets and transformers library. Explore how to fine tune a Vision Transformer (ViT)
Build a Transformer in JAX from scratch: how to write and train your own models
How to develop and train a Transformer with JAX, Haiku and Optax. Learn by example how to code Deep Learning models in JAX
Understanding einsum for Deep learning: implement a transformer with multi-head self-attention from scratch
Learn about the einsum notation and einops by coding a custom multi-head self-attention unit and a transformer block
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
Best AI and Deep learning books to read in 2022
A list of the top books to learn deep learning divided into four distinct categories. Personal reviews are included for each one of them.
3D Medical image segmentation with transformers tutorial
Implement a UNETR to perform 3D medical image segmentation on the BRATS dataset
Spiking Neural Networks: where neuroscience meets artificial intelligence
Discorver how to formulate and train Spiking Neural Networks (SNNs) using the LIF model, and how to encode data so that it can be processed by SNNs
Transformers in computer vision: ViT architectures, tips, tricks and improvements
Learn all there is to know about transformer architectures in computer vision, aka ViT.
Understanding SWAV: self-supervised learning with contrasting cluster assignments
A mathematical explanation of the Swapping Assignments Between Views (SWAV) paper.
Best Graph Neural Network architectures: GCN, GAT, MPNN and more
Explore the most popular gnn architectures such as gcn, gat, mpnn, graphsage and temporal graph networks
Deep learning on computational biology and bioinformatics tutorial: from DNA to protein folding and alphafold2
A self-complete guide for understanding biology concepts that are necessary for applying deep learning in biology and bioinformatics focused on protein folding and alphafold2 related stuff
Speech Recognition: a review of the different deep learning approaches
Explore the most popular deep learning architecture to perform automatic speech recognition (ASR). From recurrent neural networks to convolutional and transformers.
Grokking self-supervised (representation) learning: how it works in computer vision and why
A general perspective on understanding self-supervised representation learning methods.