NITI Aayog is partnering with tech giant Google to work on a range of initiatives to help build Artificial Intelligence (AI) ecosystem across the country. Amitabh Kant, Chief Executive Officer, NITI Aayog, said in a statement: "India is embracing future technologies such as machine learning and AI to augment its capacity in healthcare, improve outcomes in education, develop innovative governance systems and improve overall economic productivity of the nation." "NITI's partnership with Google will unlock massive training initiatives, support start-ups and encourage AI research through Ph D scholarships, all of which contribute to the larger idea of a technologically-empowered New India," he added. Google and NITI Aayog will conduct hands-on training programmes that aim to sensitise policymakers and technical experts in governments about relevant AI tools and how they can be used to streamline governance. To help bolster the research ecosystem, one of the initiatives includes funding Indian researchers, scholars and university faculty for conducting AI-based research.

I am into my first term of Udacity's Self Driving Car Nanodegree and I want to share my experience regarding the final project of Term 1 i.e. The complete code can be found here. The basic objective of this project is to apply the concepts of HOG and Machine Learning to detect a Vehicle from a dashboard video. Sure, the Deep Learning implementations like YOLO and SSD that utilize convolutional neural network stand out for this purpose but when you are a beginner in this field, its better to start with the classical approach. The most important thing for any machine learning problem is the labelled data set and here we need to have two sets of data: Vehicle and Non Vehicle Images.

At the Microsoft Build conference on Monday, the company kicked off a new cloud offering that would provide machine learning resources to cloud customers using Intel FPGA-accelerated servers. "I think this is a first step in making the FPGAs more of a general-purpose platform for customers," said Mark Russinovich, chief technical officer for Microsoft's Azure cloud computing platform. The technology is being offered as "preview," which apparently means only a limited set of capabilities and allocations are available. Also, at this point, only customers with accounts in the East US 2 region will be able to access the platform. This represents the commercialization of Microsoft's Project Brainwave, an FPGA-based machine learning platform the company developed over the past year.

At the Microsoft Build conference on Monday, the company kicked off a new cloud offering that would provide machine learning resources to cloud customers using Intel FPGA-accelerated servers. "I think this is a first step in making the FPGAs more of a general-purpose platform for customers," said Mark Russinovich, chief technical officer for Microsoft's Azure cloud computing platform. The technology is being offered as "preview," which apparently means only a limited set of capabilities and allocations are available. Also, at this point, only customers with accounts in the East US 2 region will be able to access the platform. This represents the commercialization of Microsoft's Project Brainwave, an FPGA-based machine learning platform the company developed over the past year.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced investor. Learning machine trading analysis is indispensable for finance careers in areas such as computational finance research, computational finance development, and computational finance trading mainly within investment banks and hedge funds. It is also essential for academic careers in computational finance. And it is necessary for experienced investors computational finance trading research and development. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for back-testing to achieve greater effectiveness.

In this article, I will present several techniques for you to make your first steps towards developing an algorithm that could be used for a classic image classification problem: detecting dog breed from an image. By the end of this article, we'll have developed code that will accept any user-supplied image as input and return an estimate of the dog's breed. Also, if a human is detected, the algorithm will provide an estimate of the dog breed that is most resembling. This project was completed as part of Udacity's Machine Learning Nanodegree (GitHub repo). Convolutional neural networks (also refered to as CNN or ConvNet) are a class of deep neural networks that have seen widespread adoption in a number of computer vision and visual imagery applications.

Today we are launching the 2018 edition of Cutting Edge Deep Learning for Coders, part 2 of fast.ai's Just as with our part 1 Practical Deep Learning for Coders, there are no pre-requisites beyond high school math and 1 year of coding experience--we teach you everything else you need along the way. This course contains all new material, including new state of the art results in NLP classification (up to 20% better than previously known approaches), and shows how to replicate recent record-breaking performance results on Imagenet and CIFAR10. The main libraries used are PyTorch and fastai (we explain why we use PyTorch and why we created the fastai library in this article). Each of the eight lessons includes a video that's around two hours long, an interactive Jupyter notebook, and a dedicated discussion thread on the fast.ai

Welcome! "Machine Learning Prerequisites: Python Pandas & Matplotlib" is an excellent choice for both beginners and experts looking to expand their knowledge in Machine Learning field. Data Analysis is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. Machine Learning Prerequisites: Python Pandas & Matplotlib offers in-depth video tutorials in which we'll dive into tons of different datasets, short and long, broken and pristine. I'll take you step-by-step through Data Analysis process using the most powerful python libraries (Numpy, Pandas and Matplotlib), from installation to visualization! .

Bloomingdale's can now pinpoint which of its employee learning programs are generating results -- and by how much in real dollars. The Macy's division has deployed Axonify Impact, (from Axonify), a learning attribution engine that uses machine learning to evaluate the data collected through training programs. Results reveal the direct impact that employee training programs are having on real business metrics, such as increases in revenue or decreases in expenditures. As employees interact with the platform, the technology's machine learning capabilities reveal which programs are generating the greatest impact, and how employee knowledge and participation influence business results. It also uncovers gaps, and makes real-time recommendations to frontline managers when a business target is at risk.

Before the 1800s and the invention of the power loom, clothing was handmade at home, by necessity. Once clothing was mass produced in the 1900s thanks to the power loom and the Industrial Revolution, everyone wanted the ready-made styles that were offered. Yet, fast-forward to today, and it is once again in vogue to wear distinct styles. Only the wealthiest among us have custom-tailored clothing. The rise of the textile industry only goes to prove that the more things change the more they stay the same.