New Course: Introduction to Deep Learning and Neural Networks
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AI Summer is a free educational blog with one single purpose. To help you learn everything you need to know about Deep Learning
If you want to become a Machine Learning Expert, a Data Scientist or simply stay updated on the latest trends in the field, this is the site for you.
Let’s build the most amazing AI solutions together.
Learn Deep Learning
Discover the fundamental principles behind Deep Neural Networks both from a programming and a mathematical point of view. Then you can proceed to more advance concepts and state of the art techniques
Build AI Products
Develop and deploy real-life AI solutions on a variety of fields such as Computer Vision, Natural Language Processing, Robotics and Healthcare.
Add Machine Learning to your business
Utilize AI into your products and gain invaluable insights about your business performance. Or build entirely new Deep Learning startups that will provide value to our society.
Stay updated on the field
Explore the most promising and recent Artificial Intelligence research as well as the best Machine Learning products and tools.
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
Generative Learning
Machine Learning
Reinforcement Learning
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.
GANs in computer vision - Introduction to generative learning
The first article of the GANs in computer vision series - an introduction to generative learning, adversarial learning, gan training algorithm, conditional image generation, mode collapse, mutual information
GANs in computer vision - Conditional image synthesis and 3D object generation
The second article of the GANs in computer vision series - looking deeper in generative adversarial networks, mode collapse, conditional image synthesis, and 3D object generation, paired and unpaired image to image generation.
GANs in computer vision - semantic image synthesis and learning a generative model from a single image
The sixth article-series of GAN in computer vision - we explore semantic image synthesis and learning a generative model from a single image
In-layer normalization techniques for training very deep neural networks
How can we efficiently train very deep neural network architectures? What are the best in-layer normalization options? We gathered all you need about normalization in transformers, recurrent neural nets, convolutional neural networks.
A journey into Optimization algorithms for Deep Neural Networks
An overview of the most popular optimization algorithms for training deep neural networks. From stohastic gradient descent to Adam, AdaBelief and second-order optimization
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.
The secrets behind Reinforcement Learning
The central idea behind reinforcement learning and an overview of its algorithms
Q-targets, Double DQN and Dueling DQN
Fixed Q-targets, Double DQN, Dueling DQN, Prioritized Replay
Unravel Policy Gradients and REINFORCE
Explore Policy-based methods and dive into policy gradients
Data preprocessing for deep learning: How to build an efficient big data pipeline
How to develop high performance input pipelines in Tensorflow using the ETL pattern and functional programming
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.
How to use uWSGI and Nginx to serve a Deep Learning model
Serving a Tensorflow model to users with Flask, uWSGI as a web server and Nginx as a reverse proxy. Why we need both uWSGI and Flask, why we need Nginx on top of uWSGI and how everything is connected together?
Learn how Machine learning works
Start from the basics and learn about architectures such as Convolutional Networks and LSTMs.
Continue with more advance concepts like Reinfocement and Generative Learning by diving into state of the art research papers.
Finally it’s time to put your skills into practice by developing Computer Vision and Natural Language Processing applications using the most popular frameworks.
Audio
Applications of deep networks in audio generation and recognition. Examples include speech recognition, speech synthesis and music generation.
Autoencoders
Autoencoders are an unsupervised type of network that can learn compact representation of the data features. They can be deterministic or probabilistic
Attention and Transformers
Attention and Transformers have already been the standard in NLP applications and they are entering Computer Vision as well
Business
Artificial Intelligence is transforming business processes and will be a major driving force for future companies. Why stay behind?
Career
Become a Deep Learning Researcher or a Machine Learning Engineer. Everything you need to know to land your dream job.
Convolutional Neural Networks
A class of deep networks that use spatial structure and can be thought as regularized semi-connected feed forward networks. They have been extensively used in Computer vision applications.
Computer Vision
Computer Vision is the field that is dominated by Deep Learning. Face recognition, image classification, video prediction are only a tiny portion of applications
Data Processing
Developing high-performant big data pipelines using Tensorflow or Pytorch
Generative Learning
Deep Learning can be used to generate complete new real-like data such as images, text and more. But how is that possible?
Generative Adversarial Networks
GANs are constructed by two neural networks that compete against each other in an adversarial game, and are proven to be ideal for generating novel data.
Graph Neural Networks
GNNs are able to extract features from graphs and produce invaluable insights
Machine Learning
Funtamental Machine Learning principles and concepts that are extended into Deep Neural Networks
Medical
Accelerate healthcare progress by applying AI into disease prediction, treatment, medical images and electronic health records.
MLOps
Best practices on Machine Learning infrastructure. How to build, maintain and scale production-ready deep learning systems.
Natural Language Processing
Text generation, language translation, spoken language understanding, sentiment analysis and dialogue systems
Pytorch
Implement basic Deep Learning models and advanced real-life applications with Pytorch
Recurrent Neural Networks
Recurrent Neural Networks are deep networks that contain loop connections between nodes. Because of that, they can use their internal memory to process sequences of inputs.
Reinforcement Learning
A subfield of Machine Leaning that focuses on how agents interacting with the environment and how to make the best decisions depending on the end goal.
Resources
Curated collections of internal and external resources. The ultimate AI hub.
Software
Sharpen your software engineering skills and build robust models. MLOps, data processing and model deployment should definitely be in you arsenal
Tensorflow
Learn Tensorflow and Keras for building Deep Learning applications
More articles
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.
A complete Weights and Biases tutorial
Learn about the Weights and Biases library with a hands-on tutorial on the different features and visualizations.
Top Resources to start with Computer Vision and Deep Learning
A curated list of the best courses, books and blog to learn computer vision with deep learning methods
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)
Regularization techniques for training deep neural networks
Discover what is regularization, why it is necessary in deep neural networks and explore the most frequently used strategies: L1, L2, dropout, stohastic depth, early stopping and more
An introduction to Recommendation Systems: an overview of machine and deep learning architectures
Learn about the SOTA recommender system models. From collaborative filtering and factorization machines to DCN and DLRM
Speech synthesis: A review of the best text to speech architectures with Deep Learning
Explore the most popular deep learning models to perform text to speech (TTS) synthesis
Tensorflow Extended (TFX) in action: build a production ready deep learning pipeline
A tutorial on how to get started with Tensorflow Extended and how to design and execute a Deep Learning pipeline