Gratis Practical Deep Learning for Cloud, Mobile& Edge: Computer Vision Projects Using Python, Keras & TensorFlow de Anirudh Koul,Siddha Ganju,Meher Kasam PDF [ePub Mobi] Gratis

Descargar PDF Practical Deep Learning for Cloud, Mobile& Edge: Computer Vision Projects Using Python, Keras & TensorFlow de Anirudh Koul,Siddha Ganju,Meher Kasam PDF [ePub Mobi] Gratis, Descargar libros completos Practical Deep Learning for Cloud, Mobile& Edge: Computer Vision Projects Using Python, Keras & TensorFlow

📘 Lee Ahora     📥 Download

Practical Deep Learning for Cloud, Mobile& Edge: Computer Vision Projects Using Python, Keras & TensorFlow de Anirudh Koul,Siddha Ganju,Meher Kasam

Descripción - Reseña del editor Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where do I begin? This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile using a hands-on approach. Relying on years of industry experience transforming deep-learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, CoreML, and TensorFlow Lite and go from zero to a production-quality system quickly. Develop deep learning applications for the desktop, cloud, smartphones, browser, and Raspberry Pi Learn by building examples such as Silicon Valley's 'Not Hotdog,' image search engines, and your own mini-autonomous car Use transfer learning to train models in minutes Optimize your apps to run efficiently on different hardware Discover strategies to scale up from a single user to millions Sharpen practical skills for data collection, model interoperability, and model debugging using visualizations Uncover the potential for bias and explore the ethical underpinnings for AI-driven technology Biografía del autor Anirudh Koul is the Head of AI & Research at Aira, and was previously at Microsoft AI & Research where he founded Seeing AI - the defacto app used by the blind community worldwide. With features shipped to about a billion people, he brings over a decade of production-oriented applied research experience on petabyte-scale datasets. He has been transforming ideas to reality using AI for Augmented Reality, Robotics, Speech, Productivity as well as building tools for people with disabilities. His work, which the IEEE has called 'life changing', has been honored by CES, FCC, Cannes Lions, American Council of the Blind, showcased at events by UN, White House, House of Lords, World Economic Forum, TEDx, on Netflix, National Geographic, and applauded by world leaders including Justin Trudeau and Theresa May. Siddha Ganju, who Forbes featured in their 30 under 30 list, is a Self-Driving Architect at Nvidia. Previously at Deep Vision, she developed deep learning models for resource constraint edge devices. A graduate from Carnegie Mellon University, her prior work ranges from Visual Question Answering to Generative Adversarial Networks to gathering insights from CERN's petabyte-scale data and has been published at top-tier conferences including CVPR and NeurIPS. Serving as an AI domain expert, she has also been guiding teams at NASA as well as featured as a jury member in several international tech competitions. Meher is a seasoned software developer with apps used by tens of millions of users every day. Currently at Square, and previously at Microsoft, he shipped features for a range of apps, from Square's Point of Sale to the Bing app. He was the mobile development lead for Microsoft's Seeing AI app, which has received widespread recognition and awards from Mobile World Congress, CES, FCC, American Council of the Blind to name a few. A hacker at heart with a flair for fast prototyping, he has won close to two dozen hackathons and converted them to features shipped in widely-used products. He also serves as a judge of international competitions including Global Mobile Awards, Edison Awards.

Practical deep learning book for cloud amp mobile launch website for the book practical deep learning book for cloud amp mobile , handson computer vision projects in keras and tensorflow practical deep learning for cloud, mobile amp edge fast, handson projects in keras and tensorflow 20 taking you from your first ai code to deploying at facebook scale Deep learning redes neuronales y deep learning españa cómo empezar con deep learningdiseñe redes neuronales complejas y experimente a escala para desplegar modelos optimizados de deep learning dentro de watson studio consulte los recursos y un tutorial de deep learning Practical deep learning for cloud, mobile, and edge book get practical deep learning for cloud, mobile, and edge now with oreilly online learning oreilly members experience live online training, plus books, videos, and digital content from 200 publishers

Practical deep learning dataiku check out the ebook from oreilly practical deep learning for cloud amp mobile for handson computer vision projects using python, keras, and tensorflow get the oreilly deep learning ebook may 21, 2019 data basics lynn heidmann share subscribe to the Practical deep learning for cloud and mobilefinelybook practical deep learning for cloud and mobile handson computer vision projects using python, keras amp tensorflow isbn10 Deep learning vm image google cloud deep learning vm image delivers a seamless notebook experience with integrated support for jupyterlab, the latest webbased interface for project jupyter, the de facto standard of interactive environments for running machine learning experiments

Detalles del Libro

• Name: Practical Deep Learning for Cloud, Mobile& Edge: Computer Vision Projects Using Python, Keras & TensorFlow
  
• Autor: Anirudh Koul,Siddha Ganju,Meher Kasam
  
• Categoria: Libros,Libros universitarios y de estudios superiores,Ciencias informáticas
  
• Tamaño del archivo: 14 MB
  
• Tipos de archivo: PDF Document
  
• Idioma: Español
  
• Archivos de estado: AVAILABLE
  

[Download] Practical Deep Learning for Cloud, Mobile& Edge: Computer Vision Projects Using Python, Keras & TensorFlow de Anirudh Koul,Siddha Ganju,Meher Kasam Ebooks, PDF, ePub

Onpremises deep learning solutions nvidia deep learning ai entrena los modelos de deep learning más complejos para resolver tus mayores retos con soluciones de deep learning basadas en las gpu del centro de datos de nvidia ideal para formación e inferencia de nivel de producción, el rendimiento líder a nivel mundial de nvidia acelera los marcos de deep learning más populares y más de 550 aplicaciones informáticas de alto rendimiento hpc Download practical deep learning for cloud, mobile, and download practical deep learning for cloud, mobile, and edge realworld ai amp computervision projects using python, keras amp tensorflow or any other file from books category http download also available at fast speeds Practical deep learning for cloud and mobile this preview edition of practical deep learning for cloud and mobile, chapters 2 and 3, is a work in progressthe final book is currently scheduled for release in october 2019 and will be available at oreilly once it is published

Practical deep learning for cloud, mobile, and edge o practical deep learning for cloud, mobile, and edge realworld ai amp computervision projects using python, keras amp tensorflow by anirudh koul, siddha ganju, meher kasam publisher oreilly media release date october 2019 pages 620 read on oreilly online learning with a 10day trial Deep learning neural networks and deep learning ibm deep learning is making business impact across industries in life sciences, deep learning can be used for advanced image analysis, scientific research, drug discovery, prediction of health problems and disease symptoms, and the acceleration of new insights from genomic sequencing Deep learning on mobile 2019 practitioners guide the 2019 guide to deep learning on mobile, from inference to training on ios and android smartphones featuring coreml, tensorflow lite, mlkit, fritz, automl approaches hardware aware neural architecture search to make models more efficient, and lots of videos

Share :