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|Cache||Googleの新しい「TensorFlow Enterprise」（β版）は、「Google Cloud Platform」に最適化された「TensorFlow」ディストリビューションを長期サポートと共に企業に提供する。|
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KEY FEATURESVariational autoencoders and GANs have been two of the most interesting recent developments in deep learning and machine learning. GAN stands for generative adversarial network, where two neural networks compete with each other. Unsupervised learning means you're not trying to map input data to targets, you're just trying to learn the structure of that input data. In this course, you'll learn the structure of data in order to produce more stuff that resembles the original data.
Details & Requirements
THE EXPERTThe Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.
He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.
He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.
The machine learning platform TensorFlow, currently in version 2.0, is making its way toward the minor release 2.1.0: TensorFlow 2.1.0-rc0 is the first release candidate and includes some breaking changes. The upcoming version will be the last to support Python 2.7.
The post TensorFlow 2.1.0 will include breaking changes: First release candidate available appeared first on JAXenter.
Globally, more than 1 billion people are affected by vision impairment or blindness due to unaddressed cataracts (65.2 million), glaucoma (6.9 million), and retina disease (3 million).
Proposed here is the development of an AI-based system that uses the Azure Cognitive Services CustomVision tool to predict the probability of the existence of one of these chronic conditions in an eye scan.
Presented by: Sandeep Gupta
TensorFlow.js → https://goo.gle/2XLhMe0
Tensorflow.js Github → https://goo.gle/2DcgLCe
#ChromeDevSummit All Sessions → https://goo.gle/CDS19
Subscribe to the Chrome Developers channel → https://goo.gle/ChromeDevs
Event photos → https://goo.gle/CDS19Photos
“Using simplifications enabled by TensorFlow, we managed to bring the total number of known (stable or unstable) equilibrium solutions for one particular type of M-Theory spacetime geometries to 194″Cache
|Link. I need physicist commentary.|
Running ML on Particle Hardware, ML 101
Tensorflow Lite now runs on newer Particle devices! Brandon Satrom published a very detailed tutorial about running TFLite on Particle devices. - Particle
Do your chores or else I’ll cut off the internet!
AccidentalRebel is creating a device “that monitors and logs if my kids have done their chores and daily tasks. If not, their devices won’t have access to the internet.”. When their chores for the day are complete, their device will automatically re-connect them to the internet. - Hackaday.io
ESPRing is a NeoPixel ring with an onboard ESP module. The ESP connects to WiFi and fetches the NTP time. - Hackaday.io
AutoHome - Universal Home Automation with Raspberry Pi
rirozizo is building a home automation system powered by a Raspberry Pi “to remotely control any possible home appliances without the use of proprietary hardware and apps”. They’re using the (free) Adafruit IO service as the MQTT broker and for data visualization. - Github
Voice-Controlled PyPortal Smart Switch
Dan the Geek is improving their PyPortal-based Smart Switch. They connected it to Adafruit IO’s IFTTT integration so they can turn a light on or off using voice commands over Alexa or Google Assistant. - Twitter
Evaluating Motion Sensors, Microwave v.s. PIR
Akarush wrote a detailed log of his evaluation for two motion sensors - a RCWL-0516 and a PIR motion sensor. The results? Each sensor has unique advantages and disadvantages. - Hackaday.io
Bluetooth-based Costume Props using Arduino and ESP32
Juan Carlos Jiménez hosted a costume party and integrated their costume with the house decorations. This BLE-powered costume prop is spooky. - JCJC-Dev
RGB Weather Strip
This RGB LED Strip changes color based on the weather forecast outside. - Hackaday
Code-less IoT Projects with Node-RED on Raspberry Pi
Les Pounder posted a tutorial about using the Node-RED development tool…
Around the Internet - IoT News
PWNing MBEDTLS on ESP32
LimitedResults found vulnerabilities with the ESP32 which allows an attacker to compromise the cryptographic library on the ESP32, MbedTLS. It’s important to note that an adversary will need physical access to the ESP32 module as it’s been compromised using a voltage-glitching attack. While this doesn’t impact hobbyists, it is a an attack on the hardware module (you can not roll out new software to patch it). If you have an ESP32 module in the field, it is potentially vulnerable to this type of attack, given an attacker’s resources and time. It looks like Espressif is following this report. They tweeted after ESP32 was pwned a couple of months ago: “We have upgraded the hardware; stay tuned for ESP32v3 with improved security and performance!”. We are unsure if this impacts the ESP8266 or the upcoming ESP32-S2 module.
Adafruit joins the Zephyr Project
The Zephyr Project is a scalable real-time operating system (RTOS) supporting multiple hardware architectures, optimized for resource constrained devices, and built with safety and security in mind, and we’re thrilled to announced we’ve joined the project. - Adafruit
Mozilla is building a “Web of Things”
Mozilla is building an “open platform for monitoring and controlling devices over the web”.
Amazon’s long-term plan for Alexa
An interview with Rohit Prasad, Alexa’s head scientist, revealed details about where Amazon wants to head with their powerful voice assistant. - TechnologyReview
Hackable Smart Watch powered by Espruino
Bangle.js is a hackable, open-source smartwatch that can be easily customized. It’s currently crowdfunding on Kickstarter and may fill the space on our wrists from Pebble’s acquisition by Fitbit. The bangle packs more of a punch than a pebble with a nRF52832, 64kB RAM, heart rate monitor, accelerometer, magnetometer and a 350mAh battery. - Kickstarter
Best Buy discontinues Insignia IoT Products
Insignia, Best Buy’s generic hardware brand, has shut down every product which replies on their app (including a freezer). Each time this happens, we think about how many products in our lives rely on “other peoples servers”. Do you have a contingency plan for the IoT devices in your life? - Hackaday
Recognizing AI Snake Oil
AI has been intertwined with IoT (AIOT). But, “Much of what’s being sold as ‘AI’ today is snake oil — it does not and cannot work.”. This paper addresses the important questions of “Why is this happening? How can we recognize flawed AI claims and push back?” - Princeton
Analyzing NB-IoT and LoRaWAN Sensor Battery Life
Low power wide area network (LPWAN) technologies like NB-IoT and LoRaWAN are perfect for your projects requiring small packets, long battery life, and long distances. But how long will the batteries in your IoT project really last? - Semtech Developer Journal
Adafruit IoT Updates
Promotion: 1 Year of Adafruit IO Plus Free with $250 Adafruit Purchase
We’re running a special promotion! As of November 20th, 2019 5:30pm, if you place an order of $250 or more at Adafruit, you’ll receive a 1 year subscription to Adafruit IO+. You’ll receive a minimal yet elegant Adafruit IO+ Subscription Card! This card comes with a code on the back and when typed into your Adafruit IO account, will activate a full year of Adafruit IO+ service for all the IoT projects you can dream up.
Promotion: Google AIY Voice Kit for Black Girls CODE
For a limited time, whenever you buy a Google AIY Voice Full Kit the regular price of $59.95 here, on this page, Google will automatically donate one to Black Girls CODE. Black Girls CODE goal is to empower young women of color ages 7-17 to embrace the current tech marketplace as builders + creators. Check out the bundle on Adafruit’s website.
What is Adafruit.IO?
Adafruit.io has over 14,000+ active users in the last 30 days and 850+ Adafruit IO Plus subscribers. Sign up for Adafruit IO (for free!) by clicking this link. Ready to upgrade? Click here to read more about Adafruit IO+, our subscription-based service. We don’t have investors and we’re not going to sell your data. When you sign up for Adafruit IO+, you’re supporting the same Adafruit Industries whose hardware and software you already know and love. You help make sure we’re not going anywhere by letting us know we’re on the right track.
|Cache||ROS, TensorFlow, Gazebo, and PCL experience. You should be familiar with common open-source middleware and libraries like ROS, OpenCV, Gazebo, and PCL, because…|
From Indeed - Wed, 23 Oct 2019 16:07:29 GMT - View all Kitchener, ON jobs
Zwar gibt es mit Frameworks wie DL4J mächtige und umfangreiche Machine-Learning-Lösungen für die JVM, dennoch kann es in der Praxis vorkommen, dass der Einsatz von TensorFlow notwendig wird. Das kann beispielsweise der Fall sein, wenn es einen bestimmten Algorithmus nur in einer TensorFlow-Implementierung gibt und der Portierungsaufwand in ein anderes Framework zu hoch ist. Zwar interagiert man mit TensorFlow über ein Python API, die zugrunde liegende Engine jedoch ist in C++ geschrieben. Mit Hilfe der TensorFlow-Java-Wrapper-Bibliothek kann man deshalb sowohl Training als auch Inferenz von TensorFlow-Modellen aus der JVM heraus betreiben, ohne auf Python angewiesen zu sein. So können bestehende Schnittstellen, Datenquellen und Infrastruktur mit TensorFlow integriert werden, ohne die JVM zu verlassen.
The post Deep Learning: Training von TensorFlow-Modellen mit JVM-Sprachen appeared first on JAXenter.
|Cache||US CITIZENSHIP/GREEN CARD REQUIRED Summary We are searching for exceptional software developers with Image Analysis background. Join our team of brilliant mathematicians, physicists and engineers on the forefront of imaging and image analysis with tools from Machine Learning, Image Analysis and Pattern Recognition for aviation security and medical arena. Knowledge of recent advances in deep learning, support vector machine, image reconstruction, volume rendering with deep knowledge of software engineering is a huge plus. Job Description The job involves the development of advanced imaging and image processing/recognition algorithms. The ability to analyze the imaging system in detail for the selection/development of appropriate algorithms will be highly valued. The successful applicant will be assigned to any one or more of the following tasks: (1) recognition of objects (e.g., threats) in cluttered images, including X-ray projection and volumetric CT images, (2) advanced 3-D volume rendering workstations, (3) development of related grants and proposals and (4) present/publish papers in conferences and journals. The application software will be developed in a combination of C++ and Python. Knowledge of modern software tools such as Visual Studio, Qt and others will be required. Experience or desire to learn TensorFlow, Keras, etc. is a huge plus. Qualifications The applicant will have a degree in Engineering, Computer Science, Physics, or Mathematics, preferably a Ph.D. (B.S./M.S also acceptable). The ideal candidate will have expert knowledgeable in one or more of the following areas: (1) application programming, (2) machine learning, (3) statistical image/signal processing, and (4) X-ray and CT physics. Recent graduates as well as experienced senior level engineers will be considered. Experienced candidates must have 5-10 years of experience in one of the areas above. Submit your resumes to: ()|
A roundup of news about Artificial Intelligence, Machine Learning and Data Science. This is an eclectic collection of interesting blog posts, software announcements and data applications from Microsoft and elsewhere that I've noted recently.
Open Source AI, ML & Data Science News
PyTorch 1.3 is now available, with improved performance, deployment to mobile devices, "Captum" model interpretability tools, and Cloud TPU support.
The Gradient documents the growing dominance of PyTorch, particularly in research.
Keras Tuner, hyperparameter optimization for Keras, is now available on PyPI.
ONNX, the open exchange format for deep learning models, is now a Linux Foundation project.
AI Inclusive, a newly-formed worldwide organization to promote diversity in the AI community.
Databricks announces the MLflow Model Registry, to share and collaborate on machine learning models with MLflow.
Flyte, Lyft's cloud-native machine learning and data processing platform, has been released as open source.
RStudio introduces Package Manager, a commercial RStudio extension to help organizations manage binary R packages on Linux systems.
Exploratory, a new commercial tool for data science and data exploration, built on R.
GCP releases Explainable AI, a new tool to help humans understand how a machine learning model reaches its conclusions.
GCP AutoML Translation is now generally available, and the GCP Translation API is now available in Basic and Advanced editions.
GCP Cloud AutoML is now integrated with the Kaggle data science competition platform.
Amazon Rekognition adds Custom Labels, allowing users to train the image classification service to recognize new objects with as few as 10 training images per label.
Amazon Sagemaker can now use hundreds of free and paid machine learning models offered in Amazon Marketplace.
The AWS Step Functions Data Science SDK, for building machine learning workflows in Python running on AWS infrastructure, is now available.
ONNX Runtime 1.0 is now generally available, for embedded inference of machine learning models in the open ONNX format.
Many new capabilities have been added to Cognitive Services, including:
SandDance, Microsoft's interactive visual exploration tool, is now available as open source.
An essay about the root causes of problems with diversity in NLP models: for example, "hers" not being recognized as a pronoun.
Videos from the Artificial Intelligence and Machine Learning Path, a series of six application-oriented talks presented at Microsoft Ignite.
A guide to getting started with PyTorch, using Google Colab's Free GPU offer.
Public weather and climate datasets, provided by Google.
The Relightables: capture humans in a custom light stage, drop video into a 3-D scene with realistic lighting.
How Tesla builds and deploys its driving automation models with PyTorch (presentation at PyTorch DevCon).
OpenAI has released the full GPT-2 language generation model.
Spleeter, a pre-trained PyTorch model to separate a music track into vocal and instrument audio files.
Detectron2, a PyTorch reimplementation of Facebook's popular object-detection and image-segmentation library.
Find previous editions of the AI roundup here.
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