Differentiable programming has emerged as a key programming paradigm empowering rapid developments of deep learning while its applications to important computational methods such as Monte Carlo remain largely unexplored. Here we present the general theory enabling infinite-order automatic differentiation on expectations computed by Monte Carlo with unnormalized probability distributions, which we call "automatic differentiable Monte Carlo" (ADMC). By implementing ADMC algorithms on computational graphs, one can also leverage state-of-the-art machine learning frameworks and techniques to traditional Monte Carlo applications in statistics and physics. We illustrate the versatility of ADMC by showing some applications: fast search of phase transitions and accurately finding ground states of interacting many-body models in two dimensions. ADMC paves a promising way to innovate Monte Carlo in various aspects to achieve higher accuracy and efficiency, e.g. easing or solving the sign problem of quantum many-body models through ADMC.

As an efficient model for knowledge organization, the knowledge graph has been widely adopted in several fields, e.g., biomedicine, sociology, and education. And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks. In general, knowledge graph embedding techniques aim to learn vectorized representations which preserve the structural information of the graph. And conventional embedding learning models rely on structural relationships among entities and relations. However, in educational knowledge graphs, structural relationships are not the focus. Instead, rich literals of the graphs are more valuable. In this paper, we focus on this problem and propose a novel model for embedding learning of educational knowledge graphs. Our model considers both structural and literal information and jointly learns embedding representations. Three experimental graphs were constructed based on an educational knowledge graph which has been applied in real-world teaching. We conducted two experiments on the three graphs and other common benchmark graphs. The experimental results proved the effectiveness of our model and its superiority over other baselines when processing educational knowledge graphs.

"Passengers who were Female, paying high Fare and travelling 1st class had high probability of surviving. Passengers who were male, paying low Fare and were in 2nd or 3rd class had less probability of surviving" This is amazing, as it like having an expert analyst with you, who would look at rows and columns and try to understand what the data means.

In this post we will see how to implement a deep Neural Network in Python from scratch. It isn't something that we will do often in practise, but it is good way to understand the inner workings of a Deep Learning. First we will import libraries we will use in the following code. In the following code we will define activation functions: \(sigmoid \), \(ReLU\) and \(tanh\) we will also save values that we will need for the backward propagation step and that are \(Z \) values, and after that we will define function which will output \(\textbf{dZ}\). So, to be clear, when we calculate activation of any hidden unit or of a hidden layer and also caches the value of \( Z {[l]} \) and we have set of functions called "backward" which outputs \( \textbf{dZ} \) values.

Europilot is an open source project that leverages the popular Euro Truck Simulator(ETS2) to develop self-driving algorithms. A convolutional neural network (CNN) controls the steering wheel inside ETS2. Think of europilot as a bridge between the game environment, and your favorite deep-learning framework, such as Keras or Tensorflow. With europilot, you can capture the game screen input, and programmatically control the truck inside the simulator. Europilot can be used in one of two ways: training or testing. For training, europilot can capture the screen input and output a numpy array in realtime, while simultaenously getting the wheel-joystick values.

If you keep hearing about artificial intelligence but aren't quite sure what it means or how it works, you're not alone. There's been much confusion among the general public about the term, not helped by dramatic news stories about how "AI" will destroy jobs, or companies that overstate their abilities to "use AI." A lot of that confusion comes from the misuse of terms like AI and machine learning. So here's a short text-and-video guide to explain them: Think of it like the difference between economics and accounting. Economics is a field of study, but you wouldn't hire a Nobel Prize-winning economist to do your taxes.

Artificial intelligence is the future. Artificial intelligence is science fiction. And it is already part of our everyday lives. All these three statements are true, it all depends on what flavor of AI you are referring to. For instance, when Google DeepMind's AlphaGo program defeated the South Korean Master, Lee Se-dol in the board game "Go" earlier in 2016, the terms AI, machine learning and deep learning were used in the media to describe how DeepMind won.

Sign in to report inappropriate content. Infervision is an AI high-tech company that uses deep learning technology and computer vision to help diagnose cancers. The company's Founder & CEO, CHEN Kuan, shared the story of the birth of the company, and the un-easy entrepreneurship in China's Healthcare Industry.

A new white paper is available showing the advantages of running virtualized Spark Deep Learning workloads on Kubernetes. Recent versions of Spark include support for Kubernetes. For Spark on Kubernetes, the Kubernetes scheduler provides the cluster manager capability provided by Yet Another Resource Negotiator (YARN) in typical Spark on Hadoop clusters. Upon receiving a spark-submit command to start an application, Kubernetes instantiates the requested number of Spark executor pods, each with one or more Spark executors. The benefits of running Spark on Kubernetes are many: ease of deployment, resource sharing, simplifying the coordination between developer and cluster administrator, and enhanced security.

NVIDIA's DeepStream SDK delivers a complete streaming analytics toolkit for AI-based video and image understanding, as well as multi-sensor processing. DeepStream is an integral part of NVIDIA Metropolis, the platform for building end-to-end services and solutions for transforming pixels and sensor data to actionable insights. The DeepStream application framework features hardware-accelerated building blocks, called plugins, that bring deep neural networks and other complex processing tasks into a stream processing pipeline. DeepStream enables real-time understanding of video and sensor data that is rich and multi-modal. The SDK uses the open source GStreamer to deliver high throughput with a low-latency streaming framework.