This Machine Learning project is in behalf of Delhi Public School presented by Siddhartha Roy student of grade XII. The project aims at innovating young minds towards technological and AI era.
Grammar is a lightweight, extensible Grammar analyser and auto corrector built with Gingerit module of Python3. It can be used to instantly check and autocorrect an grammatically incorrect sentence .
With this ML on-device Object Detection and Tracking , you can detect and track objects in live camera feed. Optionally, you can classify detected objects based upon the trained data. The models expects clear and standard quality feed.
When was the last time you read a book, or a substantial magazine article? Book Finder is a open source book library integrated with Google Books Api. Now available more than 40 million books to read, all for free!!
Machine Learning (ML) is a subset of Artificial Intelligence. ML is a science of designing and applying algorithms that are able to learn things from past cases. Artificial intelligence and machine learning are helping people and businesses achieve key goals, obtain actionable insights, drive critical decisions, and create exciting, new, and innovative products and services. The impact of AI is already being felt in a wide range of industries, from banking and retail to farming and manufacturing. Artificial Intelligence enhances the speed, precision and effectiveness of human efforts.
Security threats in finance are increasing along with the growing number of transaction, users, and third-party integrations. And machine learning algorithms are excellent at detecting frauds. Process automation is one of the most common applications of machine learning in finance. The technology allows to replace manual work, automate repetitive tasks, and increase productivity. As a result, machine learning enables companies to optimize costs, improve customer experiences, and scale up services
Deep learning is helping Facebook draw value from a larger portion of its unstructured datasets created by almost 2 billion people updating their statuses 293,000 times per minute. Most of its deep learning technology is built on the Torch platform that focuses on deep learning technologies and neural networks. Instagram also uses big data and artificial intelligence to target advertising and fight cyberbullying and delete offensive comments. As the amount of content grows in the platform, artificial intelligence is critical to be able to show users of the platform information they might like, fight spam and enhance the user experience.
Neural network machine learning algorithms are revolutionizing the classifications of galaxies and giving us a deeper understanding of the origins and evolution of the universe. In 2017, for example, NASA discovered an eighth planet circling Kepler-90. The planet was found by feeding data from NASA’s Kepler Space Telescope into an artificial neural network programmed to identify exoplanets. To create the neural network, researchers trained the algorithm “using 15,000 previously-vetted signals from the Kepler exoplanet catalogue.” Once the neural network achieved a certain level of accuracy (96%), they applied the algorithm to a previously unanalyzed set of 670 star systems.NASA now plans to apply the algorithm to the full set of 150,000 star systems
Neuroscience is the inspiration and foundation for Google’s DeepMind, creating a machine that can mimic the thought processes of our own brains. While DeepMind has successfully beaten humans at games, what’s really intriguing are the possibilities for healthcare applications such as reducing the time it takes to plan treatments and using machines to help diagnose ailments. Google is one of the pioneers of deep learning from its initial foray with the Google Brain project in 2011. Google first used deep learning for image recognition and now is able to use it for image enhancement. Google has also applied deep learning to language processing and to provide better video recommendations on YouTube, because it studies viewers’ habits and preferences when they stream content.
Apply cutting-edge research to train deep neural networks on problems ranging from perception to control. Tesla's per-camera networks analyze raw images to perform semantic segmentation, object detection and monocular depth estimation. Testla's birds-eye-view networks take video from all cameras to output the road layout, static infrastructure and 3D objects directly in the top-down view. Testla's networks learn from the most complicated and diverse scenarios in the world, iteratively sTestla'sced from Testla's fleet of nearly 1M vehicles in real time. A full build of Autopilot neural networks involves 48 networks that take 70,000 GPU hTestla'ss to train . Together, they output 1,000 distinct tensors (predictions) at each timestep.