While much work in data science to date has focused on algorithmic scale and sophistication, safety -- that is, safeguards against harm -- is a domain no less worth pursuing. This is particularly true in applications like self-driving vehicles, where a machine learning system's poor judgement might contribute to an accident. That's why firms like Intel's Mobileye and Nvidia have proposed frameworks to guarantee safe and logical decision-making, and it's why OpenAI -- the San Francisco-based research firm cofounded by CTO Greg Brockman, chief scientist Ilya Sutskever, and others -- today released Safety Gym. OpenAI describes it as a suite of tools for developing AI that respects safety constraints while training, and for comparing the "safety" of algorithms and the extent to which those algorithms avoid mistakes while learning. Safety Gym is designed for reinforcement learning agents, or AI that's progressively spurred toward goals via rewards (or punishments).

It's exciting to see the Pytorch Community continue to grow and regularly release updated versions of PyTorch! Recent releases improve performance, ONNX export, TorchScript, C frontend, JIT, and distributed training. Several new experimental features, such as quantization, have also been introduced. At the PyTorch Developer Conference earlier this fall, we presented how our open source contributions to PyTorch make it better for everyone in the community. We also talked about how Microsoft uses PyTorch to develop machine learning models for services like Bing.

Ever heard of the Nasca Lines? They're literal lines etched in the sands of southern Peru covering an area of nearly 1,000 square kilometers, which depict over 300 different figures including animals and plants. The best evidence suggests that they're pre-Columbian in origin, dating from between roughly 500 BC and 500 AD, and that they might mark solstice points or serve as offerings to ancient deities. Although the Nasca Lines have been studied for decades (and more intensely since they were designated a UNESCO World Heritage site in 1994), they've yet to be fully mapped. Yamagata used IBM's Watson Machine Learning Accelerator (WMLA) -- a framework designed to handle large-scale workloads spanning clusters of machines -- to expedite their analyses.

We built a bot that automatically reads doctors handwriting on Belgian death certificates with an accuracy of 47% of certificates of the usable data set correctly predicted. The bot supports government officials with the official death registration and allows for a faster such registration. The solution consists of three main components: an image-processing module, a neural net, and a natural-language processing module to output predictions of medical terms. When a person deceases, a medical practitioner must certify the deceased state of the person. There is a standard form which the physician fills in. This is done "in the field," through a handwritten statement on the form, which is subsequently forwarded to other officials under sealed envelope. The physician officially records the direct cause of death, and, if known, any secondary causes. An example of such a death certificate is given below. The example above is fairly straightforward to read, but notoriously difficult-to-read examples also exist. Because the physicians record death causes "in the field," a 100% digital solution is not feasible, and a lot of handwriting will exist for years to come. The raw data are one-page scans, provided as a PDF.

Dear Makers, We are hosting our fifth meetup on 27th November. Talk 1: Scalable Automatic Machine Learning in H2O by Erin LeDell The focus of this presentation is scalable and automatic machine learning using the H2O machine learning platform. H2O is an open source, distributed machine learning platform designed for big data. The core machine learning algorithms of H2O are implemented in high-performance Java, however, fully-featured APIs are available in R, Python, Scala, REST/JSON, and also through a web interface. Since H2O's algorithm implementations are distributed, this allows the software to scale to very large datasets that may not fit into RAM on a single machine.

Deep learning, probably the most advanced and challenging foundation of artificial intelligence (AI), is having a significant impact and influence on many applications, enabling products to behave intelligently like humans. Favored by the introduction of higher-performance computers and systems for parallel computing, deep learning has today become a reality, especially in the field of image recognition and classification, voice recognition, text analysis, and virtual assistants. In recent years, we have witnessed the development of numerous models and architectures of neural networks (the basic structure on which deep learning is built), which led to the definition of data sets, ready to be used in real applications. Compared to traditional machine learning, deep learning can provide superior accuracy, greater versatility and use of big data. Models used in deep learning are based on deep neural networks (DNNs), which in turn can use different architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The choice of which architecture to use depends on the specific application: CNNs are particularly suited to image classification, while RNNs are normally used for text or speech recognition.

Learn what deep learning is and how deep learning algorithms are used in real-world applications!

The Complete Machine Learning Course in Python has been FULLY UPDATED for November 2019! With brand new sections as well as updated and improved content, you get everything you need to master Machine Learning in one course! Foundations of Deep Learning covering topics such as the difference between classical programming and machine learning, differentiate between machine and deep learning, the building blocks of neural networks, descriptions of tensor and tensor operations, categories of machine learning and advanced concepts such as over- and underfitting, regularization, dropout, validation and testing and much more. Computer Vision in the form of Convolutional Neural Networks covering building the layers, understanding filters / kernels, to advanced topics such as transfer learning, and feature extrations. All the codes have been updated to work with Python 3.6 and 3.7 Get the most up to date machine learning information possible, and get it in a single course!

Model training and hyperparameter search is an iterative process that can take days, weeks, or even months. Data scientists can spend a significant amount of time training models to achieve the wanted accuracy. Together, IBM Watson Studio Local 2.0.2 and IBM Watson Machine Learning Accelerator 1.2.0 form an enterprise AI platform for accelerating the model training process, combining speed and accuracy to drive value and reduce the model's time to go live to market. Model training is GPU-accelerated and can scale up automatically, which allows for allocations of more GPUs where available. A data scientist can get results faster and reach the accuracy level needed with our enterprise AI platform.