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Deep Learning topics

Over the past year, we have explored 21 different Deep Learning topics and have written almost 100 high detailed articles and tutorials

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Resources

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.

Medical · Attention and Transformers · Pytorch

3D Medical image segmentation with transformers tutorial

Implement a UNETR to perform 3D medical image segmentation on the BRATS dataset

Machine Learning

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

Attention and Transformers · Computer Vision

Transformers in computer vision: ViT architectures, tips, tricks and improvements

Learn all there is to know about transformer architectures in computer vision, aka ViT.

Unsupervised Learning

Understanding SWAV: self-supervised learning with contrasting cluster assignments

A mathematical explanation of the Swapping Assignments Between Views (SWAV) paper.

Graph Neural Networks

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

Biology

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

Audio

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.

Unsupervised Learning · Computer Vision

Grokking self-supervised (representation) learning: how it works in computer vision and why

A general perspective on understanding self-supervised representation learning methods.

Explore our topics

Audio

Speech synthesis: A review of the best text to speech architectures with Deep Learning
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Autoencoders

How to Generate Images using Autoencoders
The theory behind Latent Variable Models: formulating a Variational Autoencoder
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Attention and Transformers

How the Vision Transformer (ViT) works in 10 minutes: an image is worth 16x16 words
How Transformers work in deep learning and NLP: an intuitive introduction
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Business

Apply Machine Learning to your Business
Deep Learning in Production: Laptop set up and system design
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Career

How to get hired as a Machine Learning Engineer
Best bootcamps and programs to learn Machine Learning and Data Science
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Convolutional Neural Networks

Best deep CNN architectures and their principles: from AlexNet to EfficientNet
Understanding the receptive field of deep convolutional networks
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Computer Vision

Semantic Segmentation in the era of Neural Networks
Localization and Object Detection with Deep Learning
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Data Processing

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
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Generative Learning

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
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Generative Adversarial Networks

GANs in computer vision - Introduction to generative learning
GANs in computer vision - 2K image and video synthesis, and large-scale class-conditional image generation
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Graph Neural Networks

Graph Neural Networks - An overview
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Machine Learning

A journey into Optimization algorithms for Deep Neural Networks
Neural Network from scratch-part 2
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Medical

Deep learning in MRI beyond segmentation: Medical image reconstruction, registration, and synthesis
Introduction to medical image processing with Python: CT lung and vessel segmentation without labels
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MLOps

How to use Docker containers and Docker Compose for Deep Learning applications
How to use uWSGI and Nginx to serve a Deep Learning model
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Natural Language Processing

Document clustering
How Transformers work in deep learning and NLP: an intuitive introduction
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Pytorch

Understanding einsum for Deep learning: implement a transformer with multi-head self-attention from scratch
Best deep CNN architectures and their principles: from AlexNet to EfficientNet
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Recurrent Neural Networks

Recurrent neural networks: building a custom LSTM cell
Recurrent Neural Networks: building GRU cells VS LSTM cells in Pytorch
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Reinforcement Learning

Deep Q Learning and Deep Q Networks
The idea behind Actor-Critics and how A2C and A3C improve them
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Resources

Top 10 courses to learn Machine and Deep Learning (2020)
The Best Machine Learning books to learn AI
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Software

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
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Tensorflow

How to build a custom production-ready Deep Learning Training loop in Tensorflow from scratch
Distributed Deep Learning training: Model and Data Parallelism in Tensorflow
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