Artificial Intelligence

Get the most out of your training, scoring, algorithms and frameworks on Intel® architecture for Deep Learning and Artificial Intelligence.

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Antoine C. updated status

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Antoine Coppin

Hi there !

I'm studying Business Informatics and Decision at Paris Dauphine University, currently designing recommendation systems with Python in the advertising industry.

Look forward to become an Intel ambassador on my campus.

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Yash A. created project Game development frameworks and Tensorflow

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Game development frameworks and Tensorflow

This project aims to create a way to enable communication between two frameworks. Such as allowing a simulation in say Unity, to continuously send packets of data, that can be used by another computer in the local network to learn, or make inferences and return a suitable response. This can be useful for computationally expensive tasks, where both the simulation and the neural network are typically huge. In the link, you will see a blog indicating a rudimentary implementation of a local server client connection which has a simple neural network.

Looking for collaboration to improve the speed, and alternate methods to do this in a local network or even over the internet.

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suryaveer s. created project Virtual Multi-controller

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Virtual Multi-controller

Virtual Multi-controller is a virtual game controlling gesture. This can control each and every PC game virtually using a single device. We can calibrate this device according to user’s choices that he /she wants to play. Most of us want to play video games virtually because it looks so fascinating, but can’t play because these type of devices are so expensive and even cannot be afforded. But with the help of this controller we can play virtually. Construction Idea: We are going to use some basic sensors like orientation sensor, heart beat sensor etc. These sensors can give us position and behavior of player which we need during playing the game.

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donel a. updated status

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donel adams

Google or Bing name to view associate networks fostering business development by maximizing any platform potential for growth utilizing AI

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Dhruv R. created project Business Card Scanner

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Business Card Scanner

Here’s how it works:

It uses Google’s Tesseract to built an Optical Character Recognition Engine inside the phone once installed, thus it works completely offline.

Then using Leptonica Image Processing and various other algorithms the image clicked is enhanced so as to best suite for the OCR purpose.

The engine then extracts the text which undergoes entity detection using Open Natural Language Processing(OpenNLP).

The entities are put under appropriate fields and the contact is saved in the phone directory along with the business card.

Using Parse as backend, Android Studio as IDE, stackoverflow as mentor I finally completed the app in 1 month time period.

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Dhruv R. updated status

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Dhruv Rathi

Fishes classification from under water is a huge problem for fishermen and survey guides, organisers and government itself. I am working on a new algorithm to classify fishes based on textures from underwater images into the corresponding species.

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Ganesh K. created project AiHello

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AiHello

AiHello uses Machine Learning to analyze existing inventory,past product sales and competitor information to help sellers optimize their inventory for better sales by geography.

Online ecommerce is $2 trillion dollars industry and growing in double digits but it is incredibly fragmented - There are 12 million ecommerce sites globally including Amazon, Ebay, FlipKart, Independent Shopify & Woocommerce sites etc. An online retailer faces multiple challenges and decisions starting up and most of the decisions made are either by guessing or gut feeling. The retailer also spends huge amount of money trying to market his products online & price his product correctly.

Inventory overhead costs are roughly 25% of the product while marketing especially for new sellers far exceeds the cost of the product.

Online e-commerce, in spite of being a trillion dollar industry, is just 6% of global retail industry and these problems will be magnified with the passage of time as ecommerce catches up with retail.

Our technology addresses these problems by 1) Inventory management AiHello manages your inventory across channels. The system can be used to list a product on any one channel or a combination of channels. A retailer selling on one channel can push his products to other channels in a click.

2) Optimizing inventory: AiHello uses deep learning to optimize your inventory by geography. Using its pre-existing knowledge of similar products combined with current inventory & sales , AiHello can optimize the price of a product depending on the channel and location of selling. The system will also suggest the best channel and the geo-location to target your product for maximum success

There is efficiency in learning when humans are not involved. The whole process is extremely cheap and the system learns and becomes smarter as time progresses.

We help to keep the startup business costs low, streamlined and extremely efficient.

Our pitch deck is at : https://1drv.ms/p/s!Am-DHg55IDBMgQfKvewouOFju_ms

You can reach us at https://www.aihello.com

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Suraj R. created project Vehicle Detection

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Vehicle Detection

Detecting vehicles in a video stream is an object detection problem. An object detection problem can be approached as either a classification problem or a regression problem. In the classification approach, the image are divided into small patches, each of which will be run through a classifier to determine whether there are objects in the patch. The bounding boxes will be assigned to patches with positive classification results. In the regression approach, the whole image will be run through a convolutional neural network directly to generate one or more bounding boxes for objects in the images.

The goal of this project is to detect the vehicles in a camera video. The You Only Look Once (YOLO) algorithm is used here to detect the vehicles from a dash camera video stream. This feature is an extremely important breakthrough for self-driving cars as we can train the model to also recognize birds, people, stop signs, signals and much more.

In this project, we will implement the version 1 of tiny-YOLO in Keras, since it’s easy to implement and is reasonably fast.

The YOLO approach of the object detection is consists of two parts: the neural network part that predicts a vector from an image, and the postprocessing part that interpolates the vector as boxes coordinates and class probabilities. For the neural network, the tiny YOLO v1 is consist of 9 convolution layers and 3 full connected layers. Each convolution layer consists of convolution, leaky relu and max pooling operations. The output of this network is a 1470 vector, which contains the information for the predicted bounding boxes. The 1470 vector output is divided into three parts, giving the probability, confidence and box coordinates.

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Surendra Naraga

Student at AP IIIT RK Valley, Rajiv Gandhi University of Knowledge Technologies and Intern at Intel

Bengaluru, Karnataka, India

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Victor

Zapopan, Jalisco, Mexico

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OnTheWall

Thumb paul langdon Paul Langdon

Created: 01/13/2017

MagicMirror+ for Intel Joule - Adding Face/Voice Recognition & Control using RealSense, Voice and...

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Triton UAS

Default user avatar 57012e2942 Marco Flowers

Created: 03/03/2017

Triton UAS is an engineering student organization at UC San Diego that develops deep learning sys...

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