Heart disease is the leading cause of death, and experts estimate that approximately half of all heart attacks and strokes occur in people who have not been flagged as "at risk." Thus, there is an urgent need to improve the accuracy of heart disease diagnosis. To this end, we investigate the potential of using data analysis, and in particular the design and use of deep neural networks (DNNs) for detecting heart disease based on routine clinical data. Our main contribution is the design, evaluation, and optimization of DNN architectures of increasing depth for heart disease diagnosis. This work led to the discovery of a novel five layer DNN architecture - named Heart Evaluation for Algorithmic Risk-reduction and Optimization Five (HEARO-5) -- that yields best prediction accuracy. HEARO-5's design employs regularization optimization and automatically deals with missing data and/or data outliers. To evaluate and tune the architectures we use k-way cross-validation as well as Matthews correlation coefficient (MCC) to measure the quality of our classifications. The study is performed on the publicly available Cleveland dataset of medical information, and we are making our developments open source, to further facilitate openness and research on the use of DNNs in medicine. The HEARO-5 architecture, yielding 99% accuracy and 0.98 MCC, significantly outperforms currently published research in the area.

Auto-Keras is an open source software library for automated machine learning (AutoML). It is developed by DATA Lab at Texas A&M University and community contributors. The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models. Note: currently, Auto-Keras is only compatible with: Python 3.6.

Lily AI is looking for an experienced machine learning engineer to join our Data Science team and help build our AI platform that powers emotionally intelligent user experiences on top e-commerce retailers digital storefronts. You will be part of the core team that builds and deploys ML models in the areas of Object Recognition (image / text), Classification and Recommender Systems. As one of the early engineers at Lily AI, you should feel comfortable working in a fast-paced environment and excited about transforming the user experience in retail. You will collaborate closely with the product and styling teams to provide the best personalization experience and insights by navigating massive amounts of data on products and users. Job Tags: ai, computer vision, keras, machine learning, nlp, pytorch, and tensorflow.

Researchers delivered a 15-petaflop deep-learning software and ran it on Cori, a supercomputer at the National Energy Research Scientific Computing Center, a Department of Energy Office of Science user facility. Machine learning, a form of artificial intelligence, enjoys unprecedented success in commercial applications. However, the use of machine learning in high performance computing for science has been limited. Why? Advanced machine learning tools weren't designed for big data sets, like those used to study stars and planets. A team from Intel, National Energy Research Scientific Computing Center (NERSC), and Stanford changed that situation.

The Neural Network Zoo shows different types of cells and various layer connectivity styles, but it doesn't really go into how each cell type works. A number of cell types I originally gave different colours to differentiate the networks more clearly, but I have since found out that these cells work more or less the same way, so you'll find descriptions under the basic cell images. A basic neural network cell, the type one would find in a regular feed forward architecture, is quite simple. The cell is connected to other neurons via weights, i.e. it can be connected to all the neurons in the previous layer. Each connection has its own weight, which is often just a random number at first.

This post is designed to be an overview highlighting three of the most common deep learning architectures: Multi-layer Perceptrons, Convolutional Neural Networks and Recurrent Neural Networks. The content in this post is high-level, introducing the main features of each architecture and describing the type of data it tends to be used for. It is complementary to the post Introduction to Deep Learning: What do I need to know…?. The above diagram is a multi-layer perceptron (MLP). An MLP can have many layers but must have at least three: the input, a hidden layer, and the output layer.

DeepMind has shaken the world of Reinforcement Learning and Go with its creation AlphaGo, and later AlphaGo Zero. It is the first computer program to beat a human professional Go player without handicap on a 19 x 19 board. It has also beaten the world champion Lee Sedol 4 games to 1, Ke Jie (number one world ranked player at the time) and many other top ranked players with the Zero version. The game of Go is a difficult environment because of its very large branching factor at every move which makes classical techniques such as alpha-beta pruning and heuristic search unrealistic. I will present my work on reproducing the paper as closely as I could. This article will again require background knowledge in Machine Learning and Python, as I will make references to my own implementation.

In this post, we will learn how to use YOLOv3 -- a state of the art object detector -- with OpenCV. YOLOv3 is the latest variant of a popular object detection algorithm YOLO – You Only Look Once. The published model recognizes 80 different objects in images and videos, but most importantly it is super fast and nearly as accurate as Single Shot MultiBox (SSD). We can think of an object detector as a combination of a object locator and an object recognizer. In traditional computer vision approaches, a sliding window was used to look for objects at different locations and scales.

Some of us remember Will Robinson's loyal robotic pal in the "Lost in Space" series of the 1960s. Others will trace the sci-fi vision of intelligent autonomous machines to the day Skynet became self-aware and turned on humanity in the "Terminator" films. The term artificial intelligence (AI) and the official pursuit of intelligent machines in the scientific community actually dates to a 1956 conference of researchers from Dartmouth and IBM. Today's AI is invading our everyday lives, albeit in more subtle ways, such as digital assistants like Alexa and Siri. And now, Artificial Intelligence in dentistry has arrived! Consider a daily task that we as dentists view as routine and relatively simple: finding caries on X-rays.

This call for a better understanding of deep learning was the core of Ali Rahimi's Test-of-Time Award presentation at NIPS in December 2017. By comparing deep learning with alchemy, the goal of Ali was not to dismiss the entire field, but "to open a conversation". This goal has definitely been achieved and people are still debating whether our current practice of deep learning should be considered as alchemy, engineering or science. Seven months later, the machine learning community gathered again, this time in Stockholm for the International Conference on Machine Learning (ICML). With more than 5,000 participants and 629 papers published, it was one of the most important events regarding fundamental machine learning research.