Biological Inspiration of Convolutional Neural Network (CNN)

From: https://medium.com/@gopalkalpande/biological-inspiration-of-convolutional-neural-network-cnn-9419668898ac Do you have the question how the CNN is related to Human Visual System or Brain? If YES, then here is my try to answer it according to my study, understanding and mapping of concepts. In this blog I will try to connect human brain and the layers of CNN as Edge detection, … Continue reading Biological Inspiration of Convolutional Neural Network (CNN)

Image Classification of Uploaded Files Using Streamlit’s Killer New Feature

From: https://towardsdatascience.com/image-classification-of-uploaded-files-using-streamlits-killer-new-feature-7dd6aa35fe0 It’s no secret I’m a massive fan of Streamlit. I think it’s the best way to share early versions of Data Science projects. It’s especially great for demoing your project to a less technical audience. Before investing in frontend developers to build you a proper UI, Streamlit allows you to get some features … Continue reading Image Classification of Uploaded Files Using Streamlit’s Killer New Feature

Cellular Automaton and Deep Learning

From: https://towardsdatascience.com/cellular-automaton-and-deep-learning-2bf7c57139b3 Cellular Automatons (CA) are evolving grid-based computational systems which model complexity growth. In simpler words, cellular automatons are evolving patterns that are unidirectional and chaotic. The rules of evolution are very elementary and simple however, the evolution gives rise to highly complex structures and behaviour. CA have been proposed as models of how … Continue reading Cellular Automaton and Deep Learning

How Modern Game Theory is Influencing Multi-Agent Reinforcement Learning Systems

From: https://medium.com/@jrodthoughts/how-modern-game-theory-is-influencing-multi-agent-reinforcement-learning-systems-2a64a3ba0c2c Most artificial intelligence(AI) systems nowadays are based on a single agent tackling a task or, in the case of adversarial models, a couple of agents that compete against each other to improve the overall behavior of a system. However, many cognition problems in the real world are the result of knowledge built by … Continue reading How Modern Game Theory is Influencing Multi-Agent Reinforcement Learning Systems

Deep Learning based Super Resolution with OpenCV

From: https://towardsdatascience.com/deep-learning-based-super-resolution-with-opencv-4fd736678066 Table of Content Super ResolutionSteps to upscale images in OpenCVDifferent pre-trained modelsSamples of resultsNotes and references Super Resolution in OpenCV OpenCV is an open-source computer vision library that has an extensive collection of great algorithms. Since one of the latest mergers, OpenCV contains an easy-to-use interface for implementing Super Resolution (SR) based on … Continue reading Deep Learning based Super Resolution with OpenCV

Attention Mechanism in Deep Learning : Simplified

From: https://medium.com/@prakhargannu/attention-mechanism-in-deep-learning-simplified-d6a5830a079d What exactly is the attention mechanism? Look at the image below and answer me, what is the color of the soccer ball? Also, which Georgetown player, the guys in white, is wearing the captaincy band? [Source] When you were trying to figure out answers to the questions above, did your mind do this … Continue reading Attention Mechanism in Deep Learning : Simplified

Retinal Vasculature Segmentation with a U-Net Architecture

From: https://towardsdatascience.com/retinal-vasculature-segmentation-with-a-u-net-architecture-d927674cf57b The structures exhibited by the retinal vasculature infer critical information about a wide range of retinal pathologies such as Prematurity (RoP), Diabetic Retinopathy(DR), Glaucoma, hypertension, and Age-related Macular Degeneration(AMD). These pathologies are amongst the leading causes of blindness. Accurate segmentation of retinal vasculature is important for various ophthalmologic diagnostic and therapeutic procedures. A … Continue reading Retinal Vasculature Segmentation with a U-Net Architecture

LSTM-FCN for cardiology

From: https://towardsdatascience.com/lstm-fcn-for-cardiology-22af6bbfc27b We will work on the application of the algorithm on a database containing electrocardiograms (ECG) of 2 different types, and see if this new model can help detect patients at a high risk of sudden death. LSTM-FCN architecture LSTM-FCN architecture (source: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8141873) This algorithm consists of 2 parts: a LSTM block and a FCN … Continue reading LSTM-FCN for cardiology

Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 4, Convolutional Residual Neural Networks

From: https://towardsdatascience.com/denosing-lung-ct-scans-using-neural-networks-with-interactive-code-part-4-convolutional-resnet-74335714a4ae Another attempt to denoise CT Scan of lungs, this time we are going to use more sophisticated Convolutional ResNet Architecture. Specifically, we are going to use the architecture proposed in this paper, “Deep Residual Learning for Image Recognition”. Also, as usual lets do manual back propagation to compare our results. Network Architecture (Image Form) Image … Continue reading Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 4, Convolutional Residual Neural Networks

Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 3, Convolutional Residual Neural Networks

From: https://towardsdatascience.com/denosing-lung-ct-scans-using-neural-networks-with-interactive-code-part-3-convolutional-residual-6dbb36b28be So since I will be using a lot of image data, I will move on to Tensorflow to harness the power of GPU however, no worries, we will implement all of our back propagation. (Also compare the final results with auto differentiation). Now due to midterms I wasn’t able to do much, so … Continue reading Denosing Lung CT Scans using Neural Networks with Interactive Code — Part 3, Convolutional Residual Neural Networks