December 21, 2017: Business Wire India NVIDIA brought together the best minds in research, academia and industry across Hyderabad, Chennai, Mumbai, Pune, Delhi and Bangalore 42 speaker sessions from leading experts in fields such as computer vision, sensor fusion, software development, regulation and HD mapping provide expertise NVIDIA today completed its first edition of Developer Connect 2017 in Bangalore. The six-city developer roadshow witnessed over 5,000 attendees who experienced some of the highest quality workshops and demonstrations of AI and deep learning tools, designed to meet the challenges big data presents. Attendees got a closer look at NVIDIA's DGX systems, as well as the opportunity to learn more about its new Volta architecture. Both the DGX-1 and DGX Station were on display to demonstrate the full power of these AI supercomputers. The concluding segment witnessed prominent speakers from organizations such as Ola, Cognitive Computing, Microsoft, Hewlett Packard Enterprise Labs, Shell India, Sony India and Aditya Imaging Information Technologies provide their views.

PyTorch is the python version of Torch (a lua framework), which is a much older ML framework going all the way back to the early 2000s. Chainer only goes back a few years from what I can see, so your core assumption is slightly wrong.

Among main themes were AI and Deep Learning - both real progress and hype, Machine Learning, Security, Quantum Computing, AlphaGo Zero, and more. In 2017 we saw Big Data give way to AI at center stage of the technology hype cycle. This excessive media and practitioner attention on AI included positive news (increasingly powerful machine learning algorithms and AI applications in numerous industries, including automotive, medical imaging, security, customer service, entertainment, financial services) and negative news (threats of machines taking our jobs and taking over our world). We also witnessed a growth in value-producing innovations around data, including greater use of APIs, as-a-Service offerings, data science platforms, deep learning, and cloud machine learning services from the major providers. Specialized applications of data, machine learning, and AI included machine intelligence, prescriptive analytics, journey sciences, behavior analytics, and IoT.

This past month I had the luck to meet the founders of DeepCognition.ai. Deep Cognition breaks the significant barrier for organizations to be ready to adopt Deep Learning and AI through Deep Learning Studio. Before continuing and describe how Deep Cognition simplifies Deep Learning and AI, lets first define the main concepts for Deep Learning. Deep learning is a specific subfield of machine learning, a new take on learning representations from data which puts an emphasis on learning successive "layers" of increasingly meaningful representations. Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.

This paper enlightens the way companies can design Intelligent System to understand their customers' sentiments better to improve their experience, which will help the businesses change their market position. Sentiment analysis is widely acknowledged in the web and social media monitoring. It allows businesses to gain a comprehensive public opinion on the organization and its services. The ability to deduce insights from the text and emoticons from social media is a practice that is now widely adopted by the organizations worldwide. Digital media represents an extensive opportunity for businesses of any industry to acquire the needs, opinions and intent that users share on social media and web.

This blog is my first ever step towards applying deep learning techniques to Image data. This will be more of a practical blog wherein, I will be discussing how you can do a task like image classification without having much theoretical knowledge of mathematical concepts that lay the foundation of the deep learning models. I have been listening to all the amazing results (better than humans in some cases) that people have been producing for this task. I have been reading many blogs regarding VGG (Visual Geometry Group from Oxford) Model on how using this model people are making state-of-the-art models for image classification. In 2014, researchers from Oxford Visual Geometry Group(VGG) developed a CNN model for ILSVRC challenge.

Machine learning using deep convolutional neural networks (CNNs) can be used to detect fractures in plain radiographs, according to a new study published in Clinical Radiology. A team of researchers from the U.K. taught the CNNs using lateral wrist radiographs performed at a single facility from January 2015 to January 2016. Each image was classified as "fracture" or "no fracture" based on the existing radiology report. The distinction was personally verified by a human specialist before data was used to "train" the CNN. Overall, the area under the receiver operator characteristic curve (AUC) was 0.954, a number the authors said provided a proof of concept.

A new generation of A.I. algorithms, propelled by rising computational power, new hardware, and a shift in paradigms made its first notable impact in the creative world: The works of Gatys et al. and Krizhevsky et al. have not only gathered considerable public attention but have helped apps like Prisma to be adapted and used by millions. I strongly believe that this is merely the beginning. With the help of machine learning, we will fine-tune, simplify, and automate creative processes and ultimately empower new techniques for design and content creation. We've been following this topic for quite some time now and have spent considerable effort in researching the opportunities of deep learning for our PhotoEditorSDK. Portrait combines supervised deep learning with the visual power of our SDK.

As described in our recent announcement about AI pioneer Randy Goebel joining the ROSS team as an advisor, Goebel is a professor in the Department of Computing Science at the University of Alberta, a founder and researcher with the Alberta Machine Intelligence Institute (AMII) and is involved with the development of the University of Alberta Google DeepMind relationship, the group behind AlphaGo. Goebel's theoretical work on abduction, hypothetical reasoning and belief revision is internationally acclaimed and his recent application of practical belief revision and constraint programming to scheduling, layout, and web mining has had widespread impact across multiple industry verticals. More recently, Goebel has been working on the application of machine learning to visual explanation and natural language processing, with focus on legal reasoning. He has previously held faculty appointments at the University of Waterloo and the University of Tokyo, and is actively involved in academic and industrial collaborative research projects in Canada, Australia, Malaysia, Europe and Japan. Goebel is on the advisory boards of the German Research Centre for AI, the Japan Science and Technology Organization and the Japanese National Institute for Informatics.

Beauty is in the eye of the beholder, or so the saying goes, and the same is often true when trying to pick out a perfect photography. Say you've got ten relatively similar shots of a loved one, family pet, or a stunning landscape – which one is the perfect shot and, crucially, why? It's a tough question to answer as there are multiple factors at play. It could be the shot which is the most competent, with no sign of any pesky blur or noise, but, on the other hand, it could also be the shot which catches the light in a way that makes it more appealing than the rest, even if it isn't technically the best of the bunch. Even if we're not aware of it, the human brain tends to strike a balance between technical quality and aesthetic preference when judging photos.