The process of building and training Machine Learning models is always in the spotlight. There is a lot of talk about different Neural Network architectures, or new frameworks, facilitating the idea-to-implementation transition. While these are the heart of an ML engine, the circulatory system, which enables nutrients to move around and connects everything, is often missing. But what comprises this system? What gives the pipeline its pulse? The most important component in an ML pipeline works silently in the background and provides the glue that binds everything together.

The next section talks about the intersection over union (IoU) which is how an object detection generates the prediction scores.

This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new--and much smaller--places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the "internet of things" (IoT). The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security.

This article describes the shortest path from training a python machine learning model to a proof of concept iOS app you can deploy on an iPhone. The goal is to provide the basic scaffolding while leaving room for further customization suited to one's specific use case. In the spirit of simplicity, we will overlook some tasks such as model validation and building a fully polished user interface (UI). By the end of this tutorial, you will have a trained model running on iOS that you can showcase as a prototype and load to your device. Next we will install Xcode.

The importance of integrating ML into healthcare, its strengths and weaknesses, and the ethical principles were discussed. It's beneficial to learn more about developing a telehealth platform based on machine learning algorithms (successful such projects are already in our portfolio). While working on the project, our objectives were to speed up a doctor and patient interactions, develop the product from scratch in accordance with the current regulations, and consider existing production support while developing a new version. These products give the possibilities to bring specialists and patients closer via online doctor appointments and video conferencing. The project scope includes cloud and on-premise software applied in accordance with legislation, backend and API development, communication between microservices to allow the product to perform well when distributed across a number of locations, developing a mobile application to improve product quality, etc. Innovecs supplies safe and quick company-level solutions based on existing regulations, and we're able to perform well due to microservice-focused infrastructure supported with the user-friendly mobile app.

The dream of autonomous vehicles is that they can avoid human error and save lives, but a new European Union Agency for Cybersecurity (ENISA) report has found that autonomous vehicles are "highly vulnerable to a wide range of attacks" that could be dangerous for passengers, pedestrians, and people in other vehicles. Attacks considered in the report include sensor attacks with beams of light, overwhelming object detection systems, back-end malicious activity, and adversarial machine learning attacks presented in training data or the physical world. "The attack might be used to make the AI'blind' for pedestrians by manipulating for instance the image recognition component in order to misclassify pedestrians. This could lead to havoc on the streets, as autonomous cars may hit pedestrians on the road or crosswalks," the report reads. "The absence of sufficient security knowledge and expertise among developers and system designers on AI cybersecurity is a major barrier that hampers the integration of security in the automotive sector."

In my previous article, I have highlighted 4 algorithms to start off in Machine Learning: Linear Regression, Logistic Regression, Decision Trees and Random Forest. Now, I am creating a series of the same. The equation which defines the simplest form of the regression equation with one dependent and one independent variable: y mx c. Where y estimated dependent variable, c constant, m regression coefficient and x independent variable. Let's just understand with an example: Say; There is a certain relationship between the marks scored by the students (y- Dependent variable) in an exam and hours they studied for the exam(x- Independent Variable).

This article is a part of a series that I'm writing, and where I will try to address the topic of using Deep Learning in NLP. First of all, I was writing an article for an example of text classification using a perceptron, but I was thinking that will be better to review some basics before, as activation and loss functions. Loss function also called the objective function, is one of the main bricks in supervised machine learning algorithm which is based on labeled data. A loss function guides the training algorithm to update parameters in the right way. In a much simple definition, a loss function takes a truth (y) and a prediction (ŷ) as input and gives a score of real value number. This value indicates how much the prediction is close to the truth.

Google is Google because of its lucrative advertising business--and that business works by letting advertisers target users based on what they do on the web. On Wednesday, Google announced what some observers have framed as a major shift in that setup: The company's Chrome browser will soon stop tracking individual users across different websites in order to serve them ads. While the change does allow the web giant and its advertising customers to continue tracking users to a certain extent, this appears to be a significant step away from Google's traditional model. David Temkin, Google's director of product management for ads privacy and trust, described the decision as a move to address growing concerns about digital privacy. "People shouldn't have to accept being tracked across the web in order to get the benefits of relevant advertising," he wrote in a blog post announcing the change. "And advertisers don't need to track individual consumers across the web to get the performance benefits of digital advertising."

The core idea is deceptively simple: every observable phenomenon in the entire universe can be modeled by a neural network. And that means, by extension, the universe itself may be a neural network. Vitaly Vanchurin, a professor of physics at the University of Minnesota Duluth, published an incredible paper last August entitled "The World as a Neural Network" on the arXiv pre-print server. It managed to slide past our notice until today when Futurism's Victor Tangermann published an interview with Vanchurin discussing the paper. We discuss a possibility that the entire universe on its most fundamental level is a neural network.