This last point can be addressed by using the tf.data.Dataset.apply()

Understand the concepts and intuition of Machine Learning algorithms Build Machine Learning models within minutes Choose the correct Machine Learning Algorithm using the cheatsheet Deploy production grade Machine Learning algorithms Deploy Machine Learning webservices in the simplest form possible including excel Bring in great value to business you manage Basic Math is good enough. This course does not require background in Data Science. Will be great if you have one. Free or paid subscription to Microsoft Azure is required. It may ask for Phone and/or Credit Card for verification Machine Learning is one of the hottest and top paying skills.

Producing content for Massive Open Online Course (MOOC) platforms like Coursera and EdX might be academically rewarding (and potentially lucrative), but it's time-consuming -- particularly where videos are involved. Professional-level lecture clips require not only a veritable studio's worth of equipment, but significant resources to transfer, edit, and upload footage of each lesson. That's why research scientists formerly at Udacity, an online learning platform with over 150 courses, are investigating a machine learning framework that automatically generates lecture videos from audio narration alone. They claim in a preprint paper ("LumièreNet: Lecture Video Synthesis from Audio") on Arxiv.org that their AI system -- LumièreNet -- can synthesize footage of any length by directly mapping between audio and corresponding visuals. "In current video production pipeline, an AI machinery which semi (or fully) automates lecture video production at scale would be highly valuable to enable agile video content development (rather than reshooting each new video)," wrote the paper's coauthors.

One of the biggest challenges for beginners in machine learning / data science is that there is too much to learn simultaneously. Especially so, if you do not know how to code. You need to quickly get used to Linear Algebra, Statistics, other mathematical concepts and learn how to code them! It might end up being a bit overwhelming for the new users. If you have no background in coding and find it difficult to cope with, you can start learning data science with a tool which is GUI driven.

The FaaS and the Serverless Bending the IoT to Your Will with JavaScript Lean Native Blockchain Crash Course Debunking Myths: Imposter Syndrome Human vs. AI Building Rich Offline Applications A Guidebook to Contributing to OSS Adopt an AI-driven Chatbot Today Micro Apps - Breaking the Mobile App Monolith and Delivering Omnichannel Experiences Machine Learning on the Go with TensorFlow.js Design for the Mixed Reality World VS Live Share Can Do That? The FaaS and the Serverless Bending the IoT to Your Will with JavaScript Lean Native Blockchain Crash Course Debunking Myths: Imposter Syndrome Human vs. AI Building Rich Offline Applications A Guidebook to Contributing to OSS Adopt an AI-driven Chatbot Today Micro Apps - Breaking the Mobile App Monolith and Delivering Omnichannel Experiences Machine Learning on the Go with TensorFlow.js Design for the Mixed Reality World VS Live Share Can Do That?

Generative Adversarial Networks, or GANs for short, were first described in the 2014 paper by Ian Goodfellow, et al. titled "Generative Adversarial Networks." Since then, GANs have seen a lot of attention given that they are perhaps one of the most effective techniques for generating large, high-quality synthetic images. As such, a number of books have been written about GANs, mostly focusing on how to develop and use the models in practice. In this post, you will discover books written on Generative Adversarial Networks. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code.

When you want to learn a new technology for professional use, there are two mutually exclusive options, either you learn it yourself or you go for instructor based training. Self learning is least expensive but lot of time results in wasting time in finding right contents, setting up the environment, troubleshooting issues and may make you give up in the middle. Instructor based training can be expensive at times and need your time commitment. This course combines the best of both these options. The course is based on one of the most famous books in the field "Python Machine Learning (2nd Ed.)" by Sebastian Raschka and Vahid Mirjalili and provides you video tutorials on how to understand the AI/ML concepts from the books by providing out of box virtual machine with demo examples for each chapter in the book and complete preinstalled setup to execute the code.

The primary professional hat I wear is as a data science consultant working with machine learning in a variety of problem domains. Due to my academic past in computer science and applied statistics, my development environment of choice today is typically R. Lately however, Python is taking the lead position for working with deep learning workloads, so that's why I took special notice of this ODSC West 2018 talk "Deep Learning in R with Keras," presented by Gabriela de Queiroz, Senior Developer Advocate at IBM (Center for Open Source Data & AI). Gabriela is also Founder of the R-Ladies group. With Keras on top of R, I can get a little more mileage out of my R experience. For those of you still making the transition to deep learning, Keras is an open-source neural network library written in Python and capable of running on top of TensorFlow, Microsoft Cognitive Toolkit (CNTK), or Theano.

This notebook collection is inspired by the book Deep Learning with Python. To learn more about using Keras with TensorFlow, see the TensorFlow Keras Guide. Publisher's note: Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. To learn about machine learning fundamentals and concepts, consider taking the Machine Learning Crash Course.

In an attempt to revamp the education system at a primary level, the Central Board of Secondary Education (CBSE) has signed partnerships with Microsoft India and IBM to focus on tech reskilling for teachers and AI curriculum in schools. In its partnership with Microsoft India, CBSE is looking to conduct capacity building programmes for high school teachers with an aim to integrate cloud-powered technology in K12 teaching and inculcating digital teaching skills in educators through curriculum as well as extra-curricular training. The programme for teachers of grades VIII to X will be conducted in 10 cities across the country, starting September 11. The CBSE Microsoft association is expected to provide teachers better access to the latest information and communication technology (ICT) tools and help them integrate technology into teaching and the curriculum in a smart manner. The selected 1000 teachers nominated by CBSE will be undergoing a three-day project-based training for practical, hands-on knowledge of Microsoft 365 tools such as OneNote, Flipgrid, Teams, Outlook, Minecraft and Paint3D.