Twitter started the trend of'People to Follow'. This later got replicated by other platforms such as Facebook, Linkedin, Quora and GitHub. This cool feature lets you connect with the rockstars of various domains and get an access to what is going on their end without bothering them much. For the influencers, this has become an effective way to communicate with their followers. The lives of people on GitHub doesn't appear to as tempting as you would observe on other platforms, but if you love coding, programming and data science, you'll surely enjoy the company of 9 million users on this platform!

Deep learning, the AI subset of machine learning that uses neural network algorithms to identify patterns in data, is proving to be extremely effective at analyzing digital representations of sensory information: images, sounds, even odors. The recent explosion in deep learning research is partly due to significant investments by online services like Baidu, Facebook, Google and Microsoft aimed at providing automatic identification and categorization of photos and videos to improve search accuracy, ad targeting, marketing data collection and other commercial uses. However, research into image processing and pattern recognition have origins that long precede the era of cat videos and vacation pictures. Early efforts focused on military applications that provide a glimpse at the far-reaching ramifications of recent research. Now, the flow of technology is reversed, from often trivial consumer uses to life-saving implementations of deep learning in medicine, agriculture and search and rescue.

The full publication is available for free on our website: www.themtank.org We would encourage readers to view the piece through our own website, as we include embedded content and easy navigational functions to make the report as dynamic as possible. We make no revenue through our website and would like to make it as engaging and intuitive for readers as possible, so any feedback on the presentation there is wholeheartedly welcomed by us! Computer Vision typically refers to the scientific discipline of giving machines the ability of sight, or perhaps more colourfully, enabling machines to visually analyse their environments and the stimuli within them. This process typically involves the evaluation of an image, images or video.

ND-Series VMs are available in the West US 2, East US, West Europe, and Southeast Asia regions. NCv2-Series VMs are available the West US 2 and East US regions and will be available later in December 2017 in West Europe and South Central US. NCv3-Series VMs are available for early customer access. To sign up for the preview, please complete the Access to Azure Next Generation GPU VMs online form.

I made a automated skin disease diagnosis DEMO website based on deep learning algorithm (Model Dermatology; http://ModelDerm.com). ResNet152 and VGG19 were used as a CNN model, around 300,000 images (179 class;176 skin disorders) were used as a trainining dataset. The training images were collected from 4 university hospitals in Korea. This CNN model is the successor to my onychomycosis model (http://nail.medicalphoto.org). The web-based test platform provides 3 differential diagnosis after analyzing image.

I decided to develop familiarity with computer vision and machine learning techniques. As a web developer, I found this growing sphere exciting, but did not have any contextual experience working with these technologies. I am embarking on a two year journey to explore this field. If you haven't read it already, you can see Part 1 here: From webdev to computer vision and geo. I ended up getting myself moving by exploring any opportunity I had to excite myself with learning.

The AWS Deep Learning AMIs are now available in four new AWS Regions: China (Beijing) operated by Sinnet, Europe (Frankfurt), Asia Pacific (Singapore), and Asia Pacific (Mumbai). The Amazon Machine Images (AMIs) provide provide machine learning practitioners with the infrastructure and tools to accelerate deep to quickly start experimenting with deep learning models. The AMIs come with pre-built packages of popular deep learning frameworks including Apache MXNet and Gluon, TensorFlow, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch, PyTorch, and Keras. In addition, to expedite development and model training, the AMIs are pre-configured with NVIDIA CUDA and cuDNN drivers, and are optimized for GPU acceleration on Amazon EC2 P2 and P3 instances. Companies are turning to deep learning to tackle a broad range of challenges.

Artificial intelligence (AI) has outperformed doctors at detecting breast cancer in a new study that will further jangle the nerves of medicos, already skittish in the face of a technology whose march into medicine seems unstoppable. The study, led by Babak Ehteshami Bejnordi at Radboud University Medical Centre in the Netherlands, reported the results of the Cancer Metastases in Lymph Nodes Challenge (also known as CAMELYON16), a competition that ran for the 12 months to November 2016. CAMELYON16 threw down the gauntlet to researchers, who had to come up with an automated way of detecting cancer cells in lymph node biopsies from women with breast cancer. During surgery doctors inject a radioactive tracer and blue dye into breast tissue near the tumour, which get funnelled by the lymphatic system to lymph nodes in the armpit. Doctors can then scan the lymph nodes with a Geiger counter, and the naked eye, to find the "hot" blue-coloured node, also called the sentinel node, which is the one the cancer will spread to first.

Artificial intelligence is years, even decades, from replicating functions of the human mind, but it's still getting serious work done today. And its influence will only expand. The irony of all that promise: Human minds are way behind. Relatively few have a baseline understanding about how AI and deep learning truly work. Techniques like machine learning, which underpin many of today's AI tools, aren't easy to grasp.

The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements. The most computationally expensive step in the simulation pipeline of a typical experiment at the Large Hadron Collider (LHC) is the detailed modeling of the full complexity of physics processes that govern the motion and evolution of particle showers inside calorimeters. We introduce \textsc{CaloGAN}, a new fast simulation technique based on generative adversarial networks (GANs). We apply these neural networks to the modeling of electromagnetic showers in a longitudinally segmented calorimeter, and achieve speedup factors comparable to or better than existing full simulation techniques on CPU ($100\times$-$1000\times$) and even faster on GPU (up to $\sim10^5\times$). There are still challenges for achieving precision across the entire phase space, but our solution can reproduce a variety of geometric shower shape properties of photons, positrons and charged pions. This represents a significant stepping stone toward a full neural network-based detector simulation that could save significant computing time and enable many analyses now and in the future.