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https://github.com/the-ai-summer/learn-deep-learning

AI Summer's complete catalog of articles
https://github.com/the-ai-summer/learn-deep-learning
computer-vision data-science deep-learning deep-neural-networks machine-learning natural-language-processing
Last synced: about 1 month ago
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AI Summer's complete catalog of articles
Host: GitHub
URL: https://github.com/the-ai-summer/learn-deep-learning
Owner: The-AI-Summer
Created: 2021-07-11T06:44:53.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2021-12-30T09:52:19.000Z (almost 3 years ago)
Last Synced: 2024-04-09T03:26:40.208Z (9 months ago)
Topics: computer-vision, data-science, deep-learning, deep-neural-networks, machine-learning, natural-language-processing
Language: Jupyter Notebook
Homepage: https://theaisummer.com/
Size: 1.23 MB
Stars: 107
Watchers: 5
Forks: 25
Open Issues: 0
Metadata Files:
- Readme: README.md
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README

        














     

     

   

    







# Learn Deep Learning with AI Summer

A collection of all articles (almost 100) written for the AI Summer blog organized by topic.

## Deep Learning Theory

### Machine Learning

- [A journey into Optimization algorithms for Deep Neural Networks](https://theaisummer.com/optimization/)

- [Regularization techniques for training deep neural networks](https://theaisummer.com/regularization/)

- [In-layer normalization techniques for training very deep neural networks](https://theaisummer.com/normalization/)

- [Explainable AI (XAI): A survey of recents methods, applications and frameworks](https://theaisummer.com/xai/)

- [Spiking Neural Networks: where neuroscience meets artificial intelligenc](https://theaisummer.com/spiking-neural-networks/)

### Convolutional Neural Networks

- [Best deep CNN architectures and their principles: from AlexNet to EfficientNet](https://theaisummer.com/cnn-architectures/)

- [Intuitive Explanation of Skip Connections in Deep Learning](https://theaisummer.com/skip-connections/)

- [Understanding the receptive field of deep convolutional networks](https://theaisummer.com/receptive-field/)

### Recurrent Neural Networks

- [Recurrent neural networks: building a custom LSTM cell](https://theaisummer.com/understanding-lstm/) ([colab](https://colab.research.google.com/drive/1Rb8OiF-AZ_Y3uFj1O2S0IyocFMhHoTCV?usp=sharing) / [repo](https://github.com/The-AI-Summer/RNN_tutorial))

- [Recurrent Neural Networks: building GRU cells VS LSTM cells in Pytorch](https://theaisummer.com/gru/)

- [Predict Bitcoin price with Long sort term memory Networks (LSTM)](https://theaisummer.com/Bitcon_prediction_LSTM/)

### Autoencoders

- [How to Generate Images using Autoencoders](https://theaisummer.com/Autoencoder/)

- [The theory behind Latent Variable Models: formulating a Variational Autoencoder](https://theaisummer.com/latent-variable-models/)

### Generative Adversarial Networks

- [Decrypt Generative Adversarial Networks (GAN)](https://theaisummer.com/Generative_Artificial_Intelligence/)

- [GANs in computer vision - Introduction to generative learning](https://theaisummer.com/gan-computer-vision/) ([repo](https://github.com/The-AI-Summer/GANs-in-Computer-Vision))

- [GANs in computer vision - Conditional image synthesis and 3D object generation](https://theaisummer.com/gan-computer-vision-object-generation/)

- [GANs in computer vision - Improved training with Wasserstein distance, game theory control and progressively growing schemes](https://theaisummer.com/gan-computer-vision-incremental-training/)

- [GANs in computer vision - 2K image and video synthesis, and large-scale class-conditional image generation](https://theaisummer.com/gan-computer-vision-video-synthesis/)

- [GANs in computer vision - self-supervised adversarial training and high-resolution image synthesis with style incorporation](https://theaisummer.com/gan-computer-vision-style-gan/)

- [GANs in computer vision - semantic image synthesis and learning a generative model from a single image](https://theaisummer.com/gan-computer-vision-semantic-synthesis/)

### Attention and Transformers

- [How Attention works in Deep Learning: understanding the attention mechanism in sequence models](https://theaisummer.com/attention/)

- [How Transformers work in deep learning and NLP: an intuitive introduction](https://theaisummer.com/transformer/)

- [How the Vision Transformer (ViT) works in 10 minutes: an image is worth 16x16 words](https://theaisummer.com/vision-transformer/)

- [How Positional Embeddings work in Self-Attention (code in Pytorch)](https://theaisummer.com/positional-embeddings/)

- [Why multi-head self attention works: math, intuitions and 10+1 hidden insights](https://theaisummer.com/self-attention/)

- [Transformers in computer vision: ViT architectures, tips, tricks and improvements](https://theaisummer.com/transformers-computer-vision/)

### Graph Neural Networks

- [Graph Neural Networks - An overview](https://theaisummer.com/Graph_Neural_Networks/)

- [How Graph Neural Networks (GNN) work: introduction to graph convolutions from scratch](https://theaisummer.com/graph-convolutional-networks/) ([colab](https://colab.research.google.com/drive/1mMUKnvM_Byu8wEcJpFSYGnniPPhIOD7N?usp=sharing))

- [Best Graph Neural Network architectures: GCN, GAT, MPNN and more](https://theaisummer.com/gnn-architectures/)

### Self-supervised Learning

- [Grokking self-supervised (representation) learning: how it works in computer vision and why](https://theaisummer.com/self-supervised-representation-learning-computer-vision/)

- [Self-supervised representation learning on videos](https://theaisummer.com/self-supervised-learning-videos/)

- [Understanding SWAV: self-supervised learning with contrasting cluster assignments](https://theaisummer.com/swav/)

### Reinforcement Learning

- [The secrets behind Reinforcement Learning](https://theaisummer.com/Reinforcement_learning/)

- [Deep Q Learning and Deep Q Networks](https://theaisummer.com/Deep_Q_Learning/)

- [Q-targets, Double DQN and Dueling DQN](https://theaisummer.com/Taking_Deep_Q_Networks_a_step_further/)

- [Unravel Policy Gradients and REINFORCE](https://theaisummer.com/Policy-Gradients/)

- [The idea behind Actor-Critics and how A2C and A3C improve them](https://theaisummer.com/Actor_critics/)

- [Trust Region and Proximal policy optimization (TRPO and PPO)](https://theaisummer.com/TRPO_PPO/)

## Deep Learning Applications

### Image segmentation

- [An overview of Unet architectures for semantic segmentation and biomedical image segmentation](https://theaisummer.com/unet-architectures/)

- [Semantic Segmentation in the era of Neural Networks](https://theaisummer.com/Semantic_Segmentation/)

### Object detection

- [Localization and Object Detection with Deep Learning](https://theaisummer.com/Localization_and_Object_Detection/)

- [YOLO - You only look once (Single shot detectors)](https://theaisummer.com/YOLO/)

### Human pose estimation

- [Human Pose Estimation](https://theaisummer.com/Human-Pose-Estimation/)

### Face synthesis

- [A closer look on Deepfakes: face sythesis with StyleGAN, face swap with XceptionNet and facial attributes and expression manipulation with StarGAN](https://theaisummer.com/deepfakes/)

### Recommendation systems

- [An introduction to Recommendation Systems: an overview of machine and deep learning architectures](https://theaisummer.com/recommendation-systems/)

### Autonomous cars

- [Self-driving cars using Deep Learning](https://theaisummer.com/Self_driving_cars/)

### Medical imaging

- [Deep learning in medical imaging - 3D medical image segmentation with PyTorch](https://theaisummer.com/medical-image-deep-learning/) ([repo](https://github.com/black0017/MedicalZooPytorch))

- [Understanding coordinate systems and DICOM for deep learning medical image analysis](https://theaisummer.com/medical-image-coordinates/)

- [Introduction to 3D medical imaging for machine learning: preprocessing and augmentations](https://theaisummer.com/medical-image-processing/) ([colab](https://colab.research.google.com/drive/1fyU_YaZUO3B5qVzBJwGoYZ6XVvVLog30?usp=sharing))

- [Deep learning in MRI beyond segmentation: Medical image reconstruction, registration, and synthesis](https://theaisummer.com/mri-beyond-segmentation/)

- [Transfer learning in medical imaging: classification and segmentation](https://theaisummer.com/medical-imaging-transfer-learning/)

- [Introduction to medical image processing with Python: CT lung and vessel segmentation without labels](https://theaisummer.com/medical-image-python/) ([colab](https://colab.research.google.com/drive/1kUOkey3CjWoebA5tVu2oazydFKpJKhrU?usp=sharing) / [repo](https://github.com/black0017/ct-intensity-segmentation) )

- [3D Medical image segmentation with transformers tutorial](https://theaisummer.com/medical-segmentation-transformers/) ([colab](https://colab.research.google.com/drive/1TdGo33zpX6_KqK1TtGVzBZTRW6SdZsrh?usp=sharing))

### Audio

- [Speech synthesis: A review of the best text to speech architectures with Deep Learning](https://theaisummer.com/text-to-speech/)

- [Speech Recognition: a review of the different deep learning approaches](https://theaisummer.com/speech-recognition/)

### Biology

- [Deep learning on computational biology and bioinformatics tutorial: from DNA to protein folding and alphafold2](https://theaisummer.com/deep-learning-biology-alphafold/)

## MLOps ([repo](https://github.com/The-AI-Summer/Deep-Learning-In-Production))

### Best practices

- [Deep Learning in Production: Laptop set up and system design](https://theaisummer.com/deep-learning-production/)

- [Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation](https://theaisummer.com/best-practices-deep-learning-code/)

- [How to Unit Test Deep Learning: Tests in TensorFlow, mocking and test coverage](https://theaisummer.com/unit-test-deep-learning/)

- [Logging and Debugging in Machine Learning - How to use Python debugger and the logging module to find errors in your AI application](https://theaisummer.com/logging-debugging/)

### Data Processing

- [Data preprocessing for deep learning: How to build an efficient big data pipeline](https://theaisummer.com/data-preprocessing/)

- [Data preprocessing for deep learning: Tips and tricks to optimize your data pipeline using Tensorflow](https://theaisummer.com/data-processing-optimization/)

### Training

- [How to build a custom production-ready Deep Learning Training loop in Tensorflow from scratch](https://theaisummer.com/tensorflow-training-loop/)

- [How to train a deep learning model in the cloud](https://theaisummer.com/training-cloud/)

- [Distributed Deep Learning training: Model and Data Parallelism in Tensorflow](https://theaisummer.com/distributed-training/)

### Deployment

- [Deploy a Deep Learning model as a web application using Flask and Tensorflow](https://theaisummer.com/deploy-flask-tensorflow/)

- [How to use uWSGI and Nginx to serve a Deep Learning model](https://theaisummer.com/uwsgi-nginx/)

- [How to use Docker containers and Docker Compose for Deep Learning applications](https://theaisummer.com/docker/)

- [Scalability in Machine Learning: Grow your model to serve millions of users](https://theaisummer.com/scalability/)

- [Introduction to Kubernetes with Google Cloud: Deploy your Deep Learning model effortlessly](https://theaisummer.com/kubernetes/)

## Libraries tutorials

### JAX ([repo](https://github.com/The-AI-Summer/JAX-examples))

- [JAX for Machine Learning: how it works and why learn it](https://theaisummer.com/jax/)

- [Build a Transformer in JAX from scratch: how to write and train your own models](https://theaisummer.com/jax-transformer/)

- [JAX vs Tensorflow vs Pytorch: Building a Variational Autoencoder (VAE)](https://theaisummer.com/jax-tensorflow-pytorch/)

### Tensorflow Extended (TFX)

- [Tensorflow Extended (TFX) in action: build a production ready deep learning pipeline](https://theaisummer.com/tfx/)

### Hugging face

- [A complete Hugging Face tutorial: how to build and train a vision transformer](https://theaisummer.com/hugging-face-vit/) ([repo](https://github.com/The-AI-Summer/Hugging_Face_tutorials))

### Weights and Biases (wandb)

- [A complete Weights and Biases tutorial](https://theaisummer.com/weights-and-biases-tutorial/)

### Einsum and einops

- [Understanding einsum for Deep learning: implement a transformer with multi-head self-attention from scratch](https://theaisummer.com/einsum-attention/) ([repo](https://github.com/The-AI-Summer/self-attention-cv))

### GPU programming

- [Neural Network from scratch-part 1](https://theaisummer.com/Neural_Network_from_scratch/)

- [Neural Network from scratch-part 2](https://theaisummer.com/Neural_Network_from_scratch_part2/)

## Miscellaneous

- [Document clustering](https://theaisummer.com/Document_clustering/)

- [Explain Neural Arithmetic Logic Units (NALU)](https://theaisummer.com/NALU/)

- [How to get hired as a Machine Learning Engineer](https://theaisummer.com/Get_hired_as_a_Machine_Learning_Engineer/)

- [Apply Machine Learning to your Business](https://theaisummer.com/Machine-Learning-Business)

## Resources

- [Top 10 courses to learn Machine and Deep Learning](https://theaisummer.com/Top_10_courses_to_learn_Machine_and_Deep_Learning/)

- [Best Artificial Intelligence books to read](https://theaisummer.com/Best-Artificial-Intelligence-books-to-read/)

- [The Best Machine Learning books to learn AI](https://theaisummer.com/machine-learning-books/)

- [Best bootcamps and programs to learn Machine Learning and Data Science](https://theaisummer.com/ml-bootcamps/)

- [Best Resources to Learn Deep Learning Theory](https://theaisummer.com/deep-learning-theory-resources/)

- [Top Resources to start with Computer Vision and Deep Learning](https://theaisummer.com/computer-vision-resources/)

- [Best AI and Deep learning books to read in 2022](https://theaisummer.com/deep-learning-books-2022/)