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A collection of all articles (almost 100) written for the AI Summer blog organized by topic.
- A journey into Optimization algorithms for Deep Neural Networks
- Regularization techniques for training deep neural networks
- In-layer normalization techniques for training very deep neural networks
- Explainable AI (XAI): A survey of recents methods, applications and frameworks
- Spiking Neural Networks: where neuroscience meets artificial intelligenc
- Best deep CNN architectures and their principles: from AlexNet to EfficientNet
- Intuitive Explanation of Skip Connections in Deep Learning
- Understanding the receptive field of deep convolutional networks
- Recurrent neural networks: building a custom LSTM cell (colab / repo)
- Recurrent Neural Networks: building GRU cells VS LSTM cells in Pytorch
- Predict Bitcoin price with Long sort term memory Networks (LSTM)
- How to Generate Images using Autoencoders
- The theory behind Latent Variable Models: formulating a Variational Autoencoder
- Decrypt Generative Adversarial Networks (GAN)
- GANs in computer vision - Introduction to generative learning (repo)
- GANs in computer vision - Conditional image synthesis and 3D object generation
- GANs in computer vision - Improved training with Wasserstein distance, game theory control and progressively growing schemes
- GANs in computer vision - 2K image and video synthesis, and large-scale class-conditional image generation
- GANs in computer vision - self-supervised adversarial training and high-resolution image synthesis with style incorporation
- GANs in computer vision - semantic image synthesis and learning a generative model from a single image
- How Attention works in Deep Learning: understanding the attention mechanism in sequence models
- How Transformers work in deep learning and NLP: an intuitive introduction
- How the Vision Transformer (ViT) works in 10 minutes: an image is worth 16x16 words
- How Positional Embeddings work in Self-Attention (code in Pytorch)
- Why multi-head self attention works: math, intuitions and 10+1 hidden insights
- Transformers in computer vision: ViT architectures, tips, tricks and improvements
- Graph Neural Networks - An overview
- How Graph Neural Networks (GNN) work: introduction to graph convolutions from scratch (colab)
- Best Graph Neural Network architectures: GCN, GAT, MPNN and more
- Grokking self-supervised (representation) learning: how it works in computer vision and why
- Self-supervised representation learning on videos
- Understanding SWAV: self-supervised learning with contrasting cluster assignments
- The secrets behind Reinforcement Learning
- Deep Q Learning and Deep Q Networks
- Q-targets, Double DQN and Dueling DQN
- Unravel Policy Gradients and REINFORCE
- The idea behind Actor-Critics and how A2C and A3C improve them
- Trust Region and Proximal policy optimization (TRPO and PPO)
- An overview of Unet architectures for semantic segmentation and biomedical image segmentation
- Semantic Segmentation in the era of Neural Networks
- Localization and Object Detection with Deep Learning
- YOLO - You only look once (Single shot detectors)
- Deep learning in medical imaging - 3D medical image segmentation with PyTorch (repo)
- Understanding coordinate systems and DICOM for deep learning medical image analysis
- Introduction to 3D medical imaging for machine learning: preprocessing and augmentations (colab)
- Deep learning in MRI beyond segmentation: Medical image reconstruction, registration, and synthesis
- Transfer learning in medical imaging: classification and segmentation
- Introduction to medical image processing with Python: CT lung and vessel segmentation without labels (colab / repo )
- 3D Medical image segmentation with transformers tutorial (colab)
- Speech synthesis: A review of the best text to speech architectures with Deep Learning
- Speech Recognition: a review of the different deep learning approaches
MLOps (repo)
- Deep Learning in Production: Laptop set up and system design
- Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation
- How to Unit Test Deep Learning: Tests in TensorFlow, mocking and test coverage
- Logging and Debugging in Machine Learning - How to use Python debugger and the logging module to find errors in your AI application
- Data preprocessing for deep learning: How to build an efficient big data pipeline
- Data preprocessing for deep learning: Tips and tricks to optimize your data pipeline using Tensorflow
- How to build a custom production-ready Deep Learning Training loop in Tensorflow from scratch
- How to train a deep learning model in the cloud
- Distributed Deep Learning training: Model and Data Parallelism in Tensorflow
- Deploy a Deep Learning model as a web application using Flask and Tensorflow
- How to use uWSGI and Nginx to serve a Deep Learning model
- How to use Docker containers and Docker Compose for Deep Learning applications
- Scalability in Machine Learning: Grow your model to serve millions of users
- Introduction to Kubernetes with Google Cloud: Deploy your Deep Learning model effortlessly
JAX (repo)
- JAX for Machine Learning: how it works and why learn it
- Build a Transformer in JAX from scratch: how to write and train your own models
- JAX vs Tensorflow vs Pytorch: Building a Variational Autoencoder (VAE)
- Understanding einsum for Deep learning: implement a transformer with multi-head self-attention from scratch (repo)
- Document clustering
- Explain Neural Arithmetic Logic Units (NALU)
- How to get hired as a Machine Learning Engineer
- Apply Machine Learning to your Business
- Top 10 courses to learn Machine and Deep Learning
- Best Artificial Intelligence books to read
- The Best Machine Learning books to learn AI
- Best bootcamps and programs to learn Machine Learning and Data Science
- Best Resources to Learn Deep Learning Theory
- Top Resources to start with Computer Vision and Deep Learning
- Best AI and Deep learning books to read in 2022