We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.

Few-shot learning that trains image classifiers over few labeled examples per category is a challenging task. In this paper, we propose to exploit an additional big dataset with different categories to improve the accuracy of few-shot learning over our target dataset. Our approach is based on the observation that images can be decomposed into objects, which may appear in images from both the additional dataset and our target dataset. We use the object-level relation learned from the additional dataset to infer the similarity of images in our target dataset with unseen categories. Nearest neighbor search is applied to do image classification, which is a non-parametric model and thus does not need fine-tuning. We evaluate our algorithm on two popular datasets, namely Omniglot and MiniImagenet. We obtain 8.5\% and 2.7\% absolute improvements for 5-way 1-shot and 5-way 5-shot experiments on MiniImagenet, respectively. Source code will be published upon acceptance.

This paper is concerned with learning to solve tasks that require a chain of interdependent steps of relational inference, like answering complex questions about the relationships between objects, or solving puzzles where the smaller elements of a solution mutually constrain each other. We introduce the recurrent relational network, a general purpose module that operates on a graph representation of objects. As a generalization of Santoro et al. [2017]'s relational network, it can augment any neural network model with the capacity to do many-step relational reasoning. We achieve state of the art results on the bAbI textual question-answering dataset with the recurrent relational network, consistently solving 20/20 tasks. As bAbI is not particularly challenging from a relational reasoning point of view, we introduce Pretty-CLEVR, a new diagnostic dataset for relational reasoning. In the Pretty-CLEVR set-up, we can vary the question to control for the number of relational reasoning steps that are required to obtain the answer. Using Pretty-CLEVR, we probe the limitations of multi-layer perceptrons, relational and recurrent relational networks. Finally, we show how recurrent relational networks can learn to solve Sudoku puzzles from supervised training data, a challenging task requiring upwards of 64 steps of relational reasoning. We achieve state-of-the-art results amongst comparable methods by solving 96.6% of the hardest Sudoku puzzles.

The field of Machine Learning has gone through some phenomenal changes over the last decade. Starting off as just a pure academic and research-oriented domain, we have seen widespread industry adoption across diverse domains including retail, technology, healthcare, science and many more. Rather than just running lab experiments to publish a research paper, the key objective of data science and machine learning in the 21st century has changed to tackling and solving real-world problems, automating complex tasks and making our life easier and better. More than often, the standard toolbox of machine learning, statistical or deep learning models remain the same. New models do come into existence like Capsule Networks, but industry adoption of the same usually takes several years.

Machine learning and deep learning are increasingly making their way into consumer-related industries. What do these mysterious technologies entail? Should smaller companies invest in machine learning and deep learning? N-iX engineers try to answers these questions. Machine learning combines the principles of computer science and statistics.

Artificial Intelligence is one of the hottest field in computer science at the moment and has taken the world by storm as a major field of development and research. Python has emerged as a dominant language in AI/ML programming because of its simplicity and flexibility. Are you a Python developer who is interested to build real-world Artificial Intelligence applications? If so, A-Z Artificial Intelligence with Python is for you! This comprehensive 5-in-1 training course is designed such that you can add an intelligence layer to any application that's based on images, text, stock market, or some other form of data.

AI (artificial intelligence) opens up a world of possibilities for application developers. By taking advantage of machine learning or deep learning, you could produce far better user profiles, personalization, and recommendations, or incorporate smarter search, a voice interface, or intelligent assistance, or improve your app any number of other ways. You could even build applications that see, hear, and react. Which programming language should you learn to plumb the depths of AI? You'll want a language with many good machine learning and deep learning libraries, of course. It should also feature good runtime performance, good tools support, a large community of programmers, and a healthy ecosystem of supporting packages.

Deep Convolutional Neural Networks, like most connectionist models, suffers from catastrophic forgetting while training for a new, unknown task. One of the simplest solutions to this issue is adding samples of previous data, with the drawback of increasingly having to store training data; or generating patterns that evoke similar responses of the previous task. We propose a model using a Recurrent Neural Network-based image generator in order to provide a Deep Convolutional Network a limited number of samples for new training data. Simulation results shows that our proposal is able to retain previous knowledge whenever some few pseudo-samples of previously recorded patterns are generated. Despite having lower performance than giving the network samples of the real dataset, this model is more biologically plausible and might help to reduce the need of storing previously trained data on bigger-scale classification classification models.

Artificial Intelligence is a broader umbrella under which Machine Learning (ML) and Deep Learning (DL) comes. Diagram shows, ML is subset of AI and DL is subset of ML. AI is composed of 2 words Artificial and intelligence. Anything which is not natural and created by humans is artificial. Intelligence means ability to understand, reason, plan etc.

Aaeon has launched an "UP AI Edge" family of products that builds on a new Apollo Lake based "UP Core Plus" SBC with stacking AI companion boards based on the Movidius Myriad 2 or Intel Cyclone 10GX plus add-ons including a quad-GbE board and a camera. Aaeon Europe quickly met its modest $11K Kickstarter goal for the new UP AI Edge ecosystem, which builds on its UP board products and community. The centerpiece is a new UP Core Plus SBC, although the official, Ubuntu-equipped UP AI Edge development package uses the larger, more feature-rich UP Squared SBC. The Ubuntu stack also includes Intel's OpenVINO computer vision toolkit, which is optimized for the Myriad 2. Also available is the Arduino Create development environent, an Open CL/ Movidius Driver, Intel System Studio, and cloud connectors for Microsoft Azure, Amazon AWS, Google Cloud, and IBM Bluemix. You can also use the Neural Compute Stick SDK available for the Myriad 2 equipped Intel Movidius Neural Compute Stick for "rapid prototyping, validation and deployment of Deep Neural Network (DNN) inference applications at the edge," says Aaeon.