A set of tasks that aim to gain a high level understanding of images or video. Typical tasks include image recognition, object detection, pose estimation and much more.See more
An intuitive guide on why it is important to inspect the receptive field, as well as how the receptive field affect the design choices of deep convolutional networks.
How convolutional neural networks work? What are the principles behind designing one CNN architecture? How did we go from AlexNet to EfficientNet?
A subfield of ML models focus on generating novel dataSee more
Explaining the mathematics behind generative learning and latent variable models and how Variational Autoencoders (VAE) were formulated (code included)
The fifth article-series of GAN in computer vision - we discuss self-supervision in adversarial training for unconditional image generation as well as in-layer normalization and style incorporation in high-resolution image synthesis.
GANs are constructed by two neural networks that compete against each other in a adversarial game, and are proven to be ideal for generating novel data.See more
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 third article-series of GAN in computer vision - we encounter some of the most advanced training concepts such as Wasserstein distance, adopt a game theory aspect in the training of GAN, and study the incremental/progressive generative training to reach a megapixel resolution.
Deep Learning can also be applied in healthcare and medical applications to solve problems such as diagnosis, prognosis and cure. Understanding medical images is a big part of that endeavourSee more
Multiple introductory concepts regarding deep learning in medical imaging, such as coordinate system and dicom data extraction from the machine learning perspective.
Learn how to apply 3D transformations for medical image preprocessing and augmentation, to setup your awesome deep learning pipeline
An area of Computer Science that focuses on processing and modeling Language. The most popular examples are language translation, voice recognition and text generation.See more
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
Reinforcement learning is an area of Machine Learning that is about taking suitable action to maximize reward in a particular situation. It has been widely used in solving games but has also numerous applications in real problems.See more
Actor critics, A2C, A3C
Explore Policy-based methods and dive into policy gradients
Unsupervised Learning is a research field where model are trained without labeled dataSee more
Use unsupervised learning to cluster documents based on their content
In this article, we dive into the state-of-the-art methods on self-supervised representation learning in computer vision, by carefully reviewing the fundamentals concepts of self-supervision on learning video representations.