Visual Studio Code Tools for AI is an extension to build, test, and deploy Deep Learning / AI solutions in Microsoft Visual Studio Code. This extension seamlessly integrates with Azure Machine Learning for robust experimentation capabilities, including but not limited to submitting data preparation and model training jobs transparently to different compute targets. Additionally, it provides support for custom metrics and run history tracking, enabling data science reproducibility and auditing. Enterprise ready collaboration, allow to securely work on project with other people. VS Code Tools for AI is a cross-platform extension that supports deep learning frameworks including Microsoft Cognitive Toolkit (CNTK), Google TensorFlow and more.

What can deep learning do?

But ask an engineer or software developer what it's like to create artificial intelligence and… well, that's a whole different level of building for the web. Even though machine learning may feel more like a sci-fi movie than reality, it's a lot closer -- and a lot more doable -- than you might think. You'll learn how possible it is for even novice web creators to get a firm grip on AI with the Deep Learning and Artificial Intelligence Introductory Bundle. With this training, you'll learn how to create the model for a machine that can learn from multiple inputs; go inside logistic regression (a key pillar in the architecture of Deep Learning); build your very first neural network, and utilize the powerful Theano and TensorFlow Python libraries.

The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Unified Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: •Optimizing cluster setup •Configuring the cluster •Ingesting data •Monitoring long-running jobs Speaker: Tim Hunter, Software Engineer -- Databricks Inc. Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

Neural networks are taking over every part of our lives. In particular -- thanks to deep learning -- Siri can fetch you a taxi using your voice; and Google can enhance and organize your photos automagically. Here at Datalogue, we use deep learning to structurally and semantically understand data, allowing us to prepare it for use automatically. Neural networks are massively successful in the domain of computer vision. Specifically, convolutional neural networks(CNNs) take images and extract relevant features from them by using small windows that travel over the image.

A few months ago I demonstrated how to install the Keras deep learning library with a Theano backend. In today's blog post I provide detailed, step-by-step instructions to install Keras using a TensorFlow backend, originally developed by the researchers and engineers on the Google Brain Team. I'll also (optionally) demonstrate how you can integrate OpenCV into this setup for a full-fledged computer vision deep learning development environment. To learn more, just keep reading. The first part of this blog post provides a short discussion of Keras backends and why we should (or should not) care which one we are using.

When I first started study PyTorch, I drop it after a few days. It was hard for me to get core concepts of this framework comparing with the TensorFlow. That's why I've put it on my "knowledge bookshelf" and forgot about it. But not so far ago a new version of PyTorch was released. So I've decided to give it a chance again.

NLP tasks are often limited by scarcity of manually annotated data. In social media sentiment analysis and related tasks, researchers have therefore used binarized emoticons and specific hashtags as forms of distant supervision. Our paper shows that by extending the distant supervision to a more diverse set of noisy labels, the models can learn richer representations. Through emoji prediction on a dataset of 1246 million tweets containing one of 64 common emojis we obtain state-of-the-art performance on 8 benchmark datasets within sentiment, emotion and sarcasm detection using a single pretrained model. Our analyses confirm that the diversity of our emotional labels yield a performance improvement over previous distant supervision approaches.

A self-driving car powered by one of the more popular artificial intelligence techniques may need to crash into a tree 50,000 times in virtual simulations before learning that it's a bad idea. But baby wild goats scrambling around on incredibly steep mountainsides do not have the luxury of living and dying millions of times before learning how to climb with sure footing without falling to their deaths. And a psychologist's 3-year-old daughter did not need to practice millions of times before she figured out, upon a whim, how to climb through an opening in the back of a chair. Today's most powerful AI techniques learn almost everything about the world from scratch with the help of powerful computational resources. By comparison, humans and animals seem to intuitively understand certain concepts--objects and places and sets of related things--that allow them to quickly learn about how the world works. That begs an important "nature vs. nurture" question: Will AI learning require built-in versions of that innate cognitive machinery possessed by humans and animals to achieve a similar level of general intelligence?

Gartner releases the top 10 strategic technology trends for 2018 2. Oracle adds GPU Accelerated Computing to Oracle Cloud Infrastructure 3. Chemistry and physics Nobel Prizes awarded to teams supported by GPUs 4. MIT uses deep learning to help guide decisions in ICU 5. Portfolio management firms are using AI to seek alpha 5. GARTNER RELEASES THE TOP 10 STRATEGIC TECH TRENDS FOR 2018 Gartner, Inc. announced its top strategic tech trends and predictions at the 2017 Gartner Symposium this week. "The first three strategic tech trends explore how AI and machine learning are seeping into virtually everything and represent a major battleground for technology providers over the next five years. READ ARTICLE 6. ORACLE ADDS GPU ACCELERATED COMPUTING TO ORACLE CLOUD INFRASTRUCTURE Oracle announced at Oracle OpenWorld this week it is now offering NVIDIA's P100 GPU instances in its public cloud, with plans to add the more powerful V100 GPUs in the near future. "This is the first time Oracle has offered access to GPU acceleration, reflecting an industry-wide move to provide access to cloud hardware optimized for artificial intelligence and machine learning. READ ARTICLE 7. CHEMISTRY & PHYSICS NOBEL PRIZES AWARDED TO TEAMS SUPPORTED BY GPUS It's not every day your work assists someone who wins a Nobel Prize.