Nowadays, mobile devices have enough computing power to run pre-trained models on them. This results in an optimal use of the hardware and an increase in speed, because the data are not sent over the Internet, which also means more privacy. In my presentation I will show different ways to integrate machine learning models into an iOS and Android application. The participants will learn how to integrate a pre-training model into an iOS and Android application with CoreML and Tensorflow Lite, and how to re-train a model for own pictures and use it instead of the pre-trained model. All examples are shown in a small demo.

Nowadays, mobile devices have enough computing power to run pre-trained models on them. This results in an optimal use of the hardware and an increase in speed, because the data are not sent over the Internet, which also means more privacy. In my presentation I will show different ways to integrate machine learning models into an iOS and Android application. The participants will learn how to integrate a pre-training model into an iOS and Android application with CoreML and Tensorflow Lite, and how to re-train a model for own pictures and use it instead of the pre-trained model. All examples are shown in a small demo.

This talk will cover how to accelerate deep learning with GPUs. GPUs have an architecture that is well-adapted to speeding up the massive parallel array calculations at the heart of deep learning. Today, manufacturers like NVIDIA are releasing GPUs with deep learning-specific features to further speed up model training and improve the throughput of deployed models. Installing and deploying GPU accelerated code can be challenging, so Anaconda has curated popular deep learning frameworks and packed them with GPU acceleration in the Anaconda Distribution. There they can be combined with Python packages like Pandas, Dask, and Jupyter to power data science experiments and production deployments.

An attendee looks at a Tifana.com Co. AI service character displayed on a screen at the Artificial Intelligence Exhibition & Conference in Tokyo, Japan, on Wednesday, April 4, 2018. The AI Expo will run through April 6. It's generally accepted that as technology moves into classrooms, teachers will move, as the saying goes, "from a sage on the stage to a guide on side." That shift has rightly troubled teachers and teaching advocates who fear that educators who instruct, analyze and provide vital context will be diminished or co-opted outright by soulless, algorithm-driven tech.

I recently wrote a brief guide on the Math required for Machine Learning. People liked it, and asked me to write one on how to master ML at a mathematically rigorous, conceptual level. That is the focus of this guide, no bullshit, no easy routes, and real, fundamental understanding. I'll be going through the later part of the curriculum myself. A quick question to ask yourself: Why do I want to learn ML?

Citrine is a company that builds data infrastructure and predictive data analysis software for the materials industry. Machine learning is a key tool in our toolbox. I have had a few professors and students in materials departments ask me (1) how machine learning could help in their research; and (2) how to quickly come up to speed in machine learning without going back to school for a degree in computer science. While a variety of machine learning courses and how-tos exist on the web already (see here, here, or here), none are specific to the field of materials science. I think the best way to master a new concept is by directly applying it, so this tutorial will show you how to build a machine learning-based model of a canonical solid-state materials property: band gap.

Learn how to use Google's Deep Learning Framework - TensorFlow with Python! IMPORTANT NOTE: THIS COURSE IS CURRENTLY IN EARLY BIRD RELEASE! Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand.

Events We've got four workshops running on October 17, covering different technologies and approaches to getting machine learning to work for your organisation. And places for all of them are filling fast. Oliver Zeigermann returns to take you through the basics of machine learning, before diving into neural networks and deep learning and working up to convolutional neural networks, all using TensorFlow and sklearn. To learn how to build basic models and crucially get them into production in the real world, join Terry McCann for his workshop on "From model to production using the cloud, Containers and Devops". As well as using Python to develop models, this highly interactive session will show how to exploit common technologies such as Azure, Docker and Kubernetes.

As bonus, you also learn how to share your analysis results with your collegues friends and others and create visual analysis of your results. You will also have access to some practice questions, which will give you hand on experience.

On Thursday, Udacity announced a new AI-based Nanodegree. Developed in partnership with WorldQuant, an international asset management firm, "Artificial Intelligence for Trading" will help learners bring machine learning to financial trading. Until recently, most banks have relied on historical data to map out future market trends. Computer modeling and machine learning algorithms, however, allow analysts to test millions of different scenarios to determine which will lead to the best outcomes. The course comprises of two three-month terms.