New Course: Introduction to Deep Learning and Neural NetworksLearn more
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.
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
Develop and deploy real-life AI solutions on a variety of fields such as Computer Vision, Natural Language Processing, Robotics and Healthcare.
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.
Explore the most promising and recent Artificial Intelligence research as well as the best Machine Learning products and tools.
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.
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
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.
The sixth article-series of GAN in computer vision - we explore semantic image synthesis and learning a generative model from a single image
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.
An overview of the most popular optimization algorithms for training deep neural networks. From stohastic gradient descent to Adam, AdaBelief and second-order optimization
What are skip connections, why we need them and how they are applied to architectures such as ResNet, DenseNet and UNet.
The central idea behind reinforcement learning and an overview of its algorithms
Fixed Q-targets, Double DQN, Dueling DQN, Prioritized Replay
Explore Policy-based methods and dive into policy gradients
How to develop high performance input pipelines in Tensorflow using the ETL pattern and functional programming
How to train your data in multiple GPUs or machines using distributed methods such as mirrored strategy, parameter-server and central storage.
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?
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.
Applications of deep networks in audio generation and recognition. Examples include speech recognition, speech synthesis and music generation.
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 have already been the standard in NLP applications and they are entering Computer Vision as well
Artificial Intelligence is transforming business processes and will be a major driving force for future companies. Why stay behind?
Applications of Deep Learning in Computational Biolology and Bioinformatics
Become a Deep Learning Researcher or a Machine Learning Engineer. Everything you need to know to land your dream job.
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 is the field that is dominated by Deep Learning. Face recognition, image classification, video prediction are only a tiny portion of applications
Developing high-performant big data pipelines using Tensorflow or Pytorch
Deep Learning can be used to generate complete new real-like data such as images, text and more. But how is that possible?
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.
GNNs are able to extract features from graphs and produce invaluable insights
Funtamental Machine Learning principles and concepts that are extended into Deep Neural Networks
Accelerate healthcare progress by applying AI into disease prediction, treatment, medical images and electronic health records.
Best practices on Machine Learning infrastructure. How to build, maintain and scale production-ready deep learning systems.
Text generation, language translation, spoken language understanding, sentiment analysis and dialogue systems
Implement basic Deep Learning models and advanced real-life applications with Pytorch
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.
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.
Curated collections of internal and external resources. The ultimate AI hub.
Sharpen your software engineering skills and build robust models. MLOps, data processing and model deployment should definitely be in you arsenal
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
Explore the most popular deep learning architecture to perform automatic speech recognition (ASR). From recurrent neural networks to convolutional and transformers.
A general perspective on understanding self-supervised representation learning methods.
Learn about the Weights and Biases library with a hands-on tutorial on the different features and visualizations.
A curated list of the best courses, books and blog to learn computer vision with deep learning methods
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)
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
Learn about the SOTA recommender system models. From collaborative filtering and factorization machines to DCN and DLRM
Explore the most popular deep learning models to perform text to speech (TTS) synthesis