In this blog post, we will create a simple web application that provides a canvas (mobile desktop laptop tablet ready) for the user to draw a digit and uses a deep neural network (MLP or CNN) to predict what digit the user had drawn. As we already know the capabilities offered by TensorFlow.js, we will extend the ideas to create two Deep Neural Networks (MLP and CNN) in Keras Python environment to recognize digits and use TensorFlow.js to predict the user drawn digit on a canvas in a web browser. In this learning path, we will restrict the user to draw a single digit between [0, 9] and later we will extend the idea to multiple digits. Below is the interactive demo that you can use to draw a digit between [0, 9] and the Deep Neural Network (MLP or CNN) that is running in your browser will predict what that digit is in the form of a bar chart. Important: This is a highly experimental demo for mobile and tablet devices.

Scientists from the University of Texas at Dallas announced a groundbreaking new approach for improving control of prosthetics with the use of artificial intelligence (AI) at the 2019 IEEE International Symposium on Measurement and Control in Robotics Symposium this month. The research findings show a huge leap forward in the goal of fully end-to-end optimization of electromyography (EMG) controlled prosthetic hands. There are more than 40 million amputees across the globe, according to the World Health Organization. Recent advances in prosthetic hand and limb technology have greatly improved the quality of life for upper-limb amputees. However, gaps remain in the control of prosthetic hands, specifically in using naturally generated electric signals from the patient's muscles.

Google revolutionised the way the world uses the internet with its landmark PageRank algorithm. Today, after two decades, Google has grown into an AI powerhouse that generates state-of-the-art algorithms that touch almost every domain known to mankind. As Google turns 21, we have compiled a list of 21 notable contributions from Google which has enriched the machine learning community across the globe. The core open source library to help you develop and train ML models developed by the team at Google Brain. TensorFlow's machine learning platform has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

Research in Deep Learning started as early as the 1960s, though the term itself was coined in 1986. With accurate predictions becoming the need of the hour, the amount of computing available and the massive data being collected, Deep Learning became the preferred algorithms at least over the last 5 years or so. As the complexity of problems arose, Deep Learning became the answer for problems that involved heavy datasets. A few millions of rows of supervised learning could effectively be crunched by ensemble tree based algorithms itself. However, for problems like computer vision or speech-to- text, deep learning was the answer.

Less than one percent of available studies on the effectiveness of artificial intelligence (AI) in detecting diseases is supported by high-quality data, according to new research. A comprehensive review of scientific literature led by University of Birmingham and University Hospitals Birmingham NHS Foundation Trust found that only a handful could be considered robust enough to back up their claims. It suggested that many studies were biased in favour of machine-learning and tended to over-hype the ability of computer algorithms when comparing them to those of human healthcare professionals. It consequently found that AI was able to detect diseases from medical images with a similar level of accuracy as healthcare professionals – contrary to several studies that have suggested AI can greatly outstrip human diagnosis. The study concluded that, while machine learning held promise to aid clinical diagnosis, its true potential remained uncertain, and called for higher standards of research and reporting to improve future evaluations.

Neural Networks is one of the most popular machine learning algorithms at present. It has been decisively proven over time that neural networks outperform other algorithms in accuracy and speed. With various variants like CNN (Convolutional Neural Networks), RNN(Recurrent Neural Networks), AutoEncoders, Deep Learning etc. neural networks are slowly becoming for data scientists or machine learning practitioners what linear regression was one for statisticians. It is thus imperative to have a fundamental understanding of what a Neural Network is, how it is made up and what is its reach and limitations. This post is an attempt to explain a neural network starting from its most basic building block a neuron, and later delving into its most popular variations like CNN, RNN etc.

Wherever you look these days, you can find AI affecting your life in one way or another. Whether it's the Netflix recommendation system or self driving cars, the use of deep learning is becoming ever more prevalent throughout our lives and is starting to make increasingly more crucial decisions. Since AI is becoming ingrained in our lives, you'd expect it to be safe and fool proof, right? The potential exists for bad actors to trick deep learning systems into misinterpreting the input on purpose causing it to give a wrong answer. We present a method for preventing these intentional misclassifications to help maintain trust in complex AI systems. Shortly after Alexnet brought neural networks back into the main stream in 2012 [Krizhevsky et al. 2012 ], people were immediately beginning to find ways to manipulate the fundamental structure of these tools.

This summer I was invited to take part in the 2018 NASA Frontier Development Lab, along with a small team including Michele Sasdelli (University of Adelaide), and a pair of planetary scientists, Megan Ansdel (University of California at Berkeley) and Hugh Osborn (Laboratoire d'Astrophysique de Marseille). Our team composed of both machine learning and planetary scientists, was challenged over the course of 8 weeks to combine our expert knowledge in order to improve the methods behind one of the most exciting frontiers of science: exoplanet discovery. Here I discuss some of the challenges of applying machine learning to real-world scientific data, in particular noisy and sparse periodic time-series data. Our knowledge of exoplanets, or planets that exist outside our Solar System, has advanced drastically over the last few decades. In fact, until relatively recently one could have called exoplanets a theoretical concept.

An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.