TOKYO, Sep 20, 2019 - (JCN Newswire) - Honda Foundation, the public interest incorporated foundation established by Soichiro Honda and his younger brother Benjiro and currently led by President Hiroto Ishida, is pleased to announce that the Honda Prize 2019 will be awarded to Dr. Geoffrey Hinton, Professor Emeritus of the University of Toronto and Chief Scientific Adviser of the Vector Institute for his pioneering research in the field of deep learning(1) in artificial intelligence (AI) and his contribution to practical application of the technology. The Honda Prize, established in 1980 and awarded once each year, is an international award that recognizes the work of individuals or groups generating new knowledge to drive the next generation, from the standpoint of eco-technology(2). Dr. Hinton has created a number of technologies that have enabled the broader application of AI, including the backpropagation algorithm(3) that forms the basis of the deep learning approach to AI. AI is expected to play an important role not only in the advancement of science and technology but also in resolving many different global issues that humankind must address in the areas of energy and climate change. The Prize will be awarded to Dr. Hinton for his outstanding achievements worthy of the highest recognition. This year marks the 40th award of the prize.

The article here presents some of the key details and characteristics of a Capsule Network, and how it improves upon the standard industry benchmark networks, mainly Convolution Neural Networks by overcoming its limitations and introducing new techniques. This article aims to provide an intuitive understanding of Capsule Networks and is not a coding tutorial. Neural networks have come a long way in recognizing images. From a basic neural network to state-of-the-art networks like InceptionNet, ResNets and GoogLeNets, the field of Deep Learning has been evolving to improve the accuracy of its algorithms. The algorithms are consuming more and more data, layers are getting deeper and deeper, and with the rise in computational power more complex networks are being introduced.

"Many a true word hath been spoken in jest." "O, beware, my lord, of jealousy; It is the green-ey'd monster, which doth mock The meat it feeds on." "There was a star danced, and under that was I born." Who can write like Shakespeare? Or even just spell like Shakespeare?

With modern technology, such questions are no longer bound to creative conjecture. You have just found Keras. Today i will give a brief introduction over this topic which created headache for me when i was learning this. All video and text tutorials are free. I use Anaconda package that almost wraps up all the Python packages including Jupyter notebook.

If you're doing applied natural language processing in 2019, you've probably heard about exciting deep learning models like BERT. Maybe you've even checked out their source code in an attempt to apply it to your own problems. To a deep learning researcher, publicly released code for bleeding-edge models is a thing of beauty. It has everything they need to reproduce results and build even better models. To the rest of us, even with some background in traditional machine learning, it looks absolutely terrifying.

And humans need health screenings, especially for the heart. That's because heart disease is the leading cause of death worldwide. With deep learning, heart disease diagnosis is becoming easier and more accessible -- which in turn can improve treatment and patient outcomes. Echocardiograms -- ultrasound tests that generate images of the heart -- are used to detect and manage heart disease cases. An echo, as it's commonly called, is also used as an assessment tool for specific populations, such as chemotherapy patients, because of their increased risk of heart failure.

Deep learning (DL) is becoming increasingly popular in sev- eral application domains and has made several new applica- tion features involving computer vision, speech recognition and synthesis, self-driving automobiles, drug design, etc. fea- sible and accurate. As a result, large scale "on-premise" and "cloud-hosted" deep learning platforms have become essential infrastructure in many organizations. These systems accept, schedule, manage and execute DL training jobs at scale. This paper describes the design, implementation and our experiences with FfDL, a DL platform used at IBM. We describe how our design balances dependability with scalability, elasticity, flexibility and efficiency.

This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. This course is not part of my deep learning series, so there are no mathematical prerequisites - just straight up coding in Python. You'll start by preparing your environment for NLP and then quickly learn about language structure and how we can break sentences down to extract information and uncover the underlying meaning.

Amharic is the official language of the Federal Democratic Republic of Ethiopia. There are lots of historic Amharic and Ethiopic handwritten documents addressing various relevant issues including governance, science, religious, social rules, cultures and art works which are very reach indigenous knowledge. The Amharic language has its own alphabet derived from Ge'ez which is currently the liturgical language in Ethiopia. Handwritten character recognition for non Latin scripts like Amharic is not addressed especially using the advantages of the state of the art techniques. This research work designs for the first time a model for Amharic handwritten character recognition using a convolutional neural network. The dataset was organized from collected sample handwritten documents and data augmentation was applied for machine learning. The model was further enhanced using multi-task learning from the relationships of the characters. Promising results are observed from the later model which can further be applied to word prediction.