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|Cache||Early diagnosis of Parkinson's disease, which causes
vital and permanent damage to both motor and non-motor symptoms, is very
important to prevent further deterioration of the patient condition. In the
present study, Parkinson's Disease data set from UCI repository is classified
using deep learning architecture. The deep learning architecture in the study
is a feed-forward neural network (FFNN) which is builded by Keras of Python.
The architecture in the study composes of an input layer, two hidden layers and
softmax function with ReLu (Rectified Linear Units) as an output layer. The
deep learning architecture solves binary classification problem since PD data
set has two classes. In order to classify the PD data set, many tests were
performed by splitting the test and train data in different ratios. The PD data
set classification was succeeded with 100% accuracy using deep learning
algorithm splitting in %20 of the data as the test and the remaining as train
data in epoch 30.|
Scania: 30 credits - Using historical vehicle data with deep learning for predicting health in commercial veCache
In the coming years the transport system for goods and people will undergo significant changes. Upcoming technological changes will reshape the val...
저자 : Osinga, Douwe / 발행처 : 느린생각|
|Cache||Be an internal champion for Deep Learning and HPC among the NVIDIA technical community. Experience programming embedded platforms e.g., NVIDIA Jetson or similar…|
From NVIDIA - Fri, 01 Nov 2019 13:55:26 GMT - View all Austin, TX jobs
|Cache||Subject matter expertise in machine and deep learning, including hardware and software experience. Ability to work independently and proactively, to take…|
From VION Corporation - Wed, 09 Oct 2019 18:41:40 GMT - View all Herndon, VA jobs
|Cache||We are working on a research project and following emotions should be detected from 40 students in class room. We are already identifying sad, happy, neutral and angry etc but we need following behaviors extracted... (Budget: ₹1500 - ₹12500 INR, Jobs: Deep Learning, Machine Learning (ML), Python)|
Canon Medical Introduces Aquilion ONE / PRISM Edition Combining Deep Learning Reconstruction and Wide-Area Spectral CTCache
|Cache||SunIRef:it Senior Data Engineer Cognizant Technology Solutions 12,256 reviews - Lewisville, TX 75022 Cognizant Technology Solutions 12,256 reviews Read what people are saying about working here. Our strength is built on our ability to work together. Our diverse backgrounds offer different perspectives and new ways of thinking. It encourages lively discussions, inspires thought leadership, and helps us build better solutions for our clients. We want someone who thrives in this setting and is inspired to craft meaningful solutions through true collaboration. If you're comfortable with ambiguity, excited by change, and excel through autonomy, we'd love to hear from you. Why Choose Cognizant? It takes a lot to succeed in today's fast-paced market, and Cognizant Digital Business has become a proven leader in the industry. Cognizant love big ideas and even bigger ambitions. We stand out because we put human experiences at the core. We help clients engage customers by envisioning and building innovative products and services. But we don't stop there. We develop go-to-market strategies and invent entirely new business models, ensuring that every company we work with walks away with both inspiration and a plan. Everything we do at Cognizant we do with passionfor our clients, our communities, and our organization. It's the defining attribute that we look for in our people. JD Skills : Senior Data Engineer At least 8 years of experience in software application development At least 3 years' experience with Big Data / Hadoop architecture and related technologies Hands-on experience with Spark - RDDs, Datasets, Dataframes, Spark SQL, Hands-on experience with streaming technologies such Spark Streaming and Kafka Hands-on experience using SQL, Spark SQL, HiveQL and performance tuning for big data operations Hands-on experience with Java 8 and use of IDEs for the same Hands-on experience using technologies such as Hive, Pig, Sqoop, Experience building micro-services based application Experience dealing with SQL and NoSQL databases such as Oracle, DB2, Teradata, Cassandra Experience using CI/CD processes for application software integration and deployment using Maven, Git, Jenkins, Jules Experience building scalable and resilient applications in private or public cloud environments and cloud technologies Experience using SDLC and Agile software development practices Experience building enterprise applications enabled for logging, monitoring, alerting and operational control Experience enabling scheduling for big data jobs Hands-on experience working in unix environment Good written, verbal, presentation and interpersonal communication skills, given an opportunity willing to work in a challenging and cross platform environment. Strong Analytical and problem-solving skills. Ability to quickly master new concepts and applications Preferable - experience in Financial industry Preferable - experience in Data Science, Machine Learning, Deep learning, Business Intelligence and Visualization. Cognizant is one of the world's leading professional services companies, transforming clients' business, operating and technology models for the digital era. Our excellent industry-based, consultative approach helps clients envision, build and run more creative and efficient businesses. Headquartered in the U.S., Cognizant, a member of the NASDAQ-100, ranked 205 on the Fortune 500 and consistently listed among the most admired companies in the world. Technical Skills SNo Primary Skill Proficiency Level * Rqrd./Dsrd. 1 Big Data Management PL1 Required 2 Apache Spark PL4 Required 3 Apache Hadoop PL3 Required 4 SQL Scripting PL4 Required 5 Core Java PL1 Required 6 Unix Shell Scripting PL1 Desired Domain Skills SNo Primary Skill Proficiency Level * Rqrd./Dsrd. 1 Acquirer & Acquirer Processor NA Required Proficiency Legends Proficiency Level Generic Reference PL1 The associate has basic awareness and comprehension of the skill and is in the process of acquiring this skill through various channels. PL2 The associate possesses working knowledge of the skill, and can actively and independently apply this skill in engagements and projects. PL3 The associate has comprehensive, in-depth and specialized knowledge of the skill. She / he has extensively demonstrated successful application of the skill in engagements or projects. PL4 The associate can function as a subject matter expert for this skill. The associate is capable of analyzing, evaluating and synthesizing solutions using the skill.Employee Status : Full Time Employee Shift : Day Job Travel : Yes, 5 % of the Time Job Posting : Nov 23 2019 About Cognizant Cognizant (Nasdaq-100: CTSH) is one of the world's leading professional services companies, transforming clients' business, operating and technology models for the digital era. Our unique industry-based, consultative approach helps clients envision, build and run more innovative and efficient businesses. Headquartered in the U.S., Cognizant is ranked 193 on the Fortune 500 and is consistently listed among the most admired companies in the world. Learn how Cognizant helps clients lead with digital at ***************** or follow us @Cognizant. Cognizant is recognized as a Military Friendly Employer and is a coalition member of the Veteran Jobs Mission. Our Cognizant Veterans Network assists Veterans in building and growing a career at Cognizant that allows them to leverage the leadership, loyalty, integrity, and commitment to excellence instilled in them through participation in military service. Cognizant - Just posted report job - original job ()|
Expires November 23, 2022 23:59 PST
Buy now and get 86% off
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.
|Cache||Learning Deep Learning For Beginners Concepts And Algorithms Data Sciences|
Intelligence artificielle vulgarisée - Le Machine Learning et le Deep Learning par la pratique, un livre de Aurélien VANNIEUWENHUYZE, une critique de rawsrcCache
|Bonjour chers membres du club,|
Je vous invite à lire la critique de rawsrc au sujet du livre :
Intelligence artificielle vulgarisée - Le Machine Learning et le Deep Learning par la pratique
Avec cet excellent livre, le domaine d'application auquel ce livre fait référence, « l'intelligence artificielle » (notez les guillemets c'est important) est un domaine qui n'est pas si récent théoriquement parlant mais un domaine qui a connu une renaissance assez spectaculaire depuis...
Implementing Active Learning and Student-Centered Pedagogy in Large Classes | Blended and Flipped Learning - Faculty FocusCache
|Nisha Malhotra, PhD, senior instructor for the Vancouver School of Economics explains, There is a vast pedagogical literature spelling out the benefits of student engagement and active participation (1). |
A recent meta-analysis study of 225 active learning classes further concludes that “active learning has a greater impact on student mastery of higher- versus lower-level cognitive skills” (p. 8411). Active learning places the student at the center of a lecture’s objective and its outcome. Students in these lectures are not only engaged in learning but are also involved in cognitive processes such as comprehension and evaluation. These processes then translate into (a) improved and deeper learning, (b) better grades, and (c) lower failure rates (2, 3). Given this growing evidence, it would be beneficial to incorporate these active learning strategies into the classroom. My aim was to adopt some form of active learning to enhance my traditional lectures, and to improve my students’ class experience.
There are wide-ranging theories of active and deep learning, and just as many applications of this kind of learning (1). So, how do we translate these theoretical frameworks into practical applications in our discipline? Not all strategies lend themselves well to different disciplines. Although bringing tactile elements to a classroom may help students in the sciences, a video case-study could be a better motivational tool for business studies. Thus, to improve learning, the game plan should be to motivate your students to participate in class with your class content.
Reducing the vast number of theories down to adaptable elements for my economics courses was honestly a process of trial and error. I struggled with time along with questions such as: How much class time should be devoted to active learning and participation? Should this be at the expense of course content? Given that first year undergraduate economics courses are mostly preparatory for advanced economics classes, the content of these courses is not up for debate, and none can be sacrificed. The solution was to use a blended learning approach: modifying the course structure, introducing online videos for review, and changing how the content was delivered in class.
In order to free up lecture time, roughly 15% of traditional lecture-style classes are now substituted with online reviews. Students watch video tutorials in order to review basic concepts before class...
Larger classes are, however, a big challenge. An average class size of a first year economics course can consist of 80 to 150 students. It is, thus, not feasible to interact with every group, let alone every student in a class of >100 students. Big lecture halls with fixed seats are not designed for group work. Have you ever tried to get students to walk around in these big lecture halls and form groups? You might as well forget about teaching that day. After much thought, I decided to rely on peer interaction and trust that students, if asked, might engage in solving posed problems. The aim was to only ‘spark’ a discussion, not a debate. I wanted students to at least question their knowledge.
Source: Faculty Focus
|Cache||Machine learning applications have gained popularity over the years and now, incorporated with advanced algorithms has been introduced, deep learning applications, as Robots.net reports. |
What Is Deep Learning?
Deep learning is an artificial intelligence that mimics the workings of a human brain in processing different data, creating patterns and interpreting information that is used for decision making. It is a subfield of machine learning in artificial intelligence. Its networks has the capability to learn, supervised or unsupervised, from data that is either structured or labelled...
Types Of Deep Learning
There are two types of deep learning, supervised and unsupervised. Supervised learning is when you give an AI a set of input and tell it the expected results. Basically, if the output generated is wrong, it will readjust its calculation and will be done repeatedly over the data set until it makes no more mistakes. Unsupervised learning is the process of machine learning using data sets with no structure specified.
Applications of deep learning have been applied to several fields including speech recognition, social network filtering, audio recognition, natural language processing, machine translation, bioinformatics, computer design, computer vision, drug design, medical image analysis, board games programs and material inspection where they need to produce results that are comparable to or superior to human experts.
Let’s go over more details on applications of deep learning and what can deep learning do.
|Cache||TextXD brings together researchers from across a wide range of disciplines, who work with text as a primary source of data. We work to identify common principles, algorithms and tools to advance text-intensive research, and break down the boundaries between domains, to foster exchange and new collaborations among like-minded researchers. We encourage scholars and practitioners from a broad disciplinary and geographic range to apply. Talks will range from the theory of text analysis and deep learning to applied analyses or new software packages. In addition to presentations, the event will include a poster session to create dialogue around NLP projects by students and collaborators.|
|Cache||Educator and entrepreneur Sebastian Thrun wants us to use AI to free humanity of repetitive work and unleash our creativity. In an inspiring, informative conversation with TED Curator Chris Anderson, Thrun discusses the progress of deep learning, why we shouldn't fear runaway AI and how society will be better off if dull, tedious work is done with the help of machines. "Only one percent of interesting things have been invented yet," Thrun says. "I believe all of us are insanely creative ... [AI] will empower us to turn creativity into action."|
|Cache||DVDFab (https://www.videohelp.com/software/DVDFab) Enlarger AI, the world's 1st complete, deep learning capable video upscaling solution, can enlarge the video images by 300%, and in the meantime, add in great details to enhance the video quality exponentially, like from 480p (SD) to 1080p (Full...|
|Cache||Open Position: An established company is looking for deep learning/AI professional for video-based action detection recognition. You will need to train Neural networks with deep learning tools and techniques to identify figures actions based on video frames sequence.|
|Cache||French startup Foodvisor has raised a $4.5 million funding round after generating 2 million app downloads. Agrinnovation is leading the round and various business angels are also participating. I covered Foodvisor last month, so I’m not going to describe the app once again. In a few words, the startup uses deep learning to enable image […]|
|Cache||Founded out of Prague in 2017, Rossum adopts deep learning and an entirely cloud-based approach to automate data extraction from any document.|
|Cache||LSTM neural networks can purportedly be used to predict crypto prices in real-time, demonstrates data scientist |
A data scientist at the prestigious Vellore Institute of Technology has outlined a method for...
The post Data Scientist Uses Deep Learning to Predict BTC Price in Real-Time appeared first on Icolancer.
Correction to: Predicting protein inter-residue contacts using composite likelihood maximization and deep learningCache
|Following publication of the original article , the author explained that there are several errors in the original article|
|Cache||Avoid real time computation “The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws|
|Cache||New Delhi, Dec 2 : Silicon Valley-based learning platform Udacity on Monday said it plans to double the headcount at its New Delhi office to support its growing student and enterprise client base in India. The company trains workers on next generation skills such as Artificial Intelligence, Machine Learning, automation, deep learning, data analytics, through […]|
El Black Friday está a la vuelta de la esquina, esa celebración del consumismo en la que se pueden encontrar grandes chollos si uno sabe explorar bien la multitud de ofertas que lanzan los diferentes comercios.
Será el 29 de noviembre cuando tenga lugar de forma oficial, pero muchas tiendas empiezan a lanzar ofertas antes y Amazon ha estado muy ágil este año en ese punto. Por ello, la nueva entrega de nuestro Cazando Gangas se va a centrar en algunas de las mejores ofertas que podréis encontrar allí en cuestión de televisores, barras de sonido, altavoces y proyectores.
Audio y vídeo
Ojo con estas ofertas, porque solamente duran 24 horas -al acabar aparecerán otros proyectos en oferta, tenedlo en cuenta por si ninguno se ajusta del todo a lo que buscáis-, no como el resto incluidas en este artículo:
Barras de sonido y altavoces
Otras ofertas del Black Friday
En otras publicaciones de Webedia también encontraréis un repaso a las ofertas más jugosas en sus áreas:
Puedes estar al día y en cada momento informado de las principales ofertas y novedades de Xataka Selección en su canal de Telegram o en sus perfiles de Twitter , Facebook y la revista Flipboard. Puedes echar un vistazo también a las ofertas de Xataka Móvil, Xataka Android, Xataka Foto, Vida Extra, Applesfera y a las nuestras, así como con nuestros compañeros de Compradicción. Puedes ver todas las gangas que publican en Twitter y Facebook, e incluso suscribirte a sus avisos vía Telegram.
También puedes encontrar aquí las mejores ofertas del Black Friday 2019
Imagen | Max Pixels
|Cache||Diese Aufgaben erwarten Sie:
- Erstellen und Evaluieren innovativer und effizienter Bildverarbeitungsalgorithmen
- Beobachtung des aktuellen Stands der Wissenschaft zum Thema Bildverarbeitung
- Entwicklung automatisierter Tests
- Identifikation und Ableitung von Lösungsansätzen sowie Mitarbeit in der Neugestaltung der Softwarearchitektur
- Eigenverantwortliches Arbeiten in einem agilen Entwicklungsumfeld
- Enge Zusammenarbeit mit anderen Entwicklungsabteilungen
Unsere Anforderungen an Sie sind:
- Erfolgreich abgeschlossenes Studium der Informatik, Physik oder Ingenieurwissenschaften
- Mehrjährige praktische Erfahrung oder Promotion in der Bildverarbeitung
- Erfahrung in der Algorithmenentwicklung und idealerweise in Machine Learning bzw. Deep Learning
- Sehr gute Programmierkenntnisse in C oder C++
- Know-How im Umgang mit git, Bitbucker oder JIRA wünschenswert
- Teamgeist und Engagement
- Problemlösungsorientierte sowie zielgerichtete Arbeitsweise
- Sehr gute Deutsch- und Englischkenntnisse|
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.
Context, as they say, is king.
The age-old question of exactly what a software developer should focus on learning has been crossing my mind a lot lately. More than ever, our technology is evolving at a furious pace - and the coding world is definitely feeling the pressure. It can be overwhelming to choose where to pay attention and what to dismiss as a passing fad.
So what are you to do? Let’s look at what the next decade has in store for the development world.
Cory House spoke convincingly on the merits of specializing in one area to become a known and trusted voice. A few years ago, Forbes came out with a high level article proclaiming the opposite. More recently, I stumbled upon this post on Hacker Noon embracing the notion of both specialist and generalist. Which way is a developer supposed to go? The answer to this question can feel largely opinion-based but there are some logical ways to examine it. Let’s get started.
How Do I Choose the Right Tech to Focus On
This is the question for a specialist: how to leap frog from one framework lilypad to another. It’s easy to fall in love with a specific area of coding and become obsessed - I’ve certainly done it. However, it can truly lead to a head-in-the-sand position when the world moves on without you (my condolences to Windows Phone developer friends, for example).
Are You Saying I Should be a Full Stack Developer
Great question! “Generalist” doesn’t always mean “Full Stack”; they aren’t interchangeable. The traditional view of a full stack programmer referred to the web (back end and front end) but there are many different places where code plays a role!
Personally, I’ve coded for voice, IoT, APIs, timer jobs, mobile apps, intranet sites, external websites, ETLs, and the list goes on. Is any of that knowledge evergreen? Some of it! Mostly the ways in which I interacted with my team and product owner - not how I specifically customized a Sharepoint page.
You can carry a cross-section of evergreen knowledge with you as a software engineer. A specific part of those “years of experience” is still applicable to whatever you need to work on now. And that part fits neatly into being a generalist developer.
Why Does Future Tech Require a Generalist Approach
Remember when AI was just another fad? With advancements in Machine Learning, Deep Learning and Big Data analytics - it doesn’t look so dismissable anymore.
Even if your main gig is maintaining a legacy code base, you owe it to your future self to know what tools are out there, different than what you use today. This knowledge doesn’t have to be super deep to be powerful, but you do need to know enough about current and future industry techniques to understand where your experience can fit.
What Should All Developers Learn Right Now
I’m glad you asked! There are a few areas in particular that developers really can’t afford to ignore anymore.
Naturally, the developer relations team here at Okta cares a lot about this topic! Often, developers are content to make a system ‘just work’ well enough to get out the door for a deadline. The result is company after company coming forward and admitting to their users that their data was not securely stored or collected. This is an area you HAVE to educate yourself on.
Next, make sure you are coding securely from the very beginning, from how you store API keys to the way you deploy your code. This is one area you cannot afford to cut corners. We’ve got lots of blog posts here at Okta to get you up to speed on user security specifically.
2. Machine Learning
Automation will come in many forms and affect all areas of technology. You should have at least a cursory understanding of how your data is fed into various algorithms and the decisions those algorithms can make.
You’ll need to use coding languages like Python and/or R to get started in this area and there are great tutorials on using Jupyter Notebooks to help. However, if you are interested in using machine learning as a service, Microsoft has come a long way with Cognitive Services, which will allow you to use REST APIs to do basic machine learning tasks like image recognition or text analysis. No matter what business you are part of, AI is here to stay in some capacity and you will probably need to interact with it in some way.
Even if you aren’t the keeper of the big red deployment button, it’s crucial for all developers to understand how code gets to production. From mastering pull-request procedures to knowing how your application architecture impacts hosting costs, development work is intrinsically tied to ops work. This is especially true with microservices architecture, which often impact the bottom line.
Reach out to your local DevOps or TechOps meetup or user group and get acquainted with a few people who really know the practice. If that’s not an option in your area, watch a few video courses on the subject. Look into scripting tools for DevOps automation like Terraform or Pulumi. Playing with infrastructure as code can actually be quite enjoyable and a nice change of pace coming from application development. Whether you are the only technical person both building and deploying code, or you are one part of a large department with a separate DevOps team, take the time to become educated on this flow.
What Do You Think Developers Should Learn
Being a specialist can be rewarding, but being a generalist is a necessity. You truly do need a bit of both; just remember not to sacrifice general knowledge in order to focus on your preference. Security, Machine Learning, and Dev/Tech Ops are the top 3 topics I believe have strong merit at the moment, but that list is certainly not exhaustive. Comment below with what you believe every coder needs to add to their ever-growing toolbox!
Learn more about Developer Careers, Tools, and Security
If you’d like to continue reading our thoughts on developer careers, we’ve published a number of posts that might interest you:
A comparative study of fault diagnosis for train door system: traditional versus deep learning approaches - Ham S, Han SY, Kim S, Park HJ, Park KJ, Choi JH.Cache
|A fault diagnosis of a train door system is carried out using the motor current signal that operates the door. A test rig is prepared, in which various fault modes are examined by applying extreme conditions, as well as the natural and artificial wears of ...|
|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: ()|
CoinDesk • December 2, 2019, 9:00 am
Technical analysis, or the art of divining future price movements from historical data, divides opinion in the crypto world. So does does it offer real insight into the markets?
Cointelegraph.com News • December 2, 2019, 8:44 am
LSTM neural networks can purportedly be used to predict crypto prices in real-time, demonstrates data scientist
Bitcoin - The Currency of the Internet • December 2, 2019, 9:46 am
submitted by /u/cryptoadventura [link] [comments]
Bitcoin - The Currency of the Internet • December 2, 2019, 7:50 am
submitted by /u/cointastical [link] [comments]
LSTM neural networks can purportedly be used to predict crypto prices in real-time, demonstrates data scientist
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.
Scopic Software is seeking a Remote Machine Learning Developer (Junior) to join our team of 250+ professionals across 40+ countries. The successful candidate will work with a team of talented developers, designers, and project managers to develop industry-leading applications with the latest technologies. Current projects include a hair-loss simulator, gender change simulator, and voice recognition processor.
This is a full-time, home-based position.
Compensation: Depending on skills and experience.
|Cache||Job SummaryThe Inside Sales representative) is an accomplished enterprise sales professional and demand generation specialist. The ISR works closely with Marketing team members, Regional Account Managers, and Solutions Architects to develop new business pipeline opportunities within a geographic territory and to support revenue growth objectives. Our ideal candidate is smart, analytical, and has experience in high-growth, early-stage technology organizations.We currently work with new and exciting technologies in cybersecurity, machine learning, artificial intelligence and DevOps, where we are building intelligent and deep learning data analytics as a service to change the world.Responsibilities - Leverage open internet and Fractal-sourced prospecting tools to identify new contacts and organizations in assigned geographic territory - Develop and maintain a comprehensive understanding of the technical and business value of QOMPLX's solutions to communicate most effectively to prospective customers - Develop and execute timely and effective campaigns to drive traffic to online and to in-person events and to successfully progress opportunity development objectives - Maintain professional and positive engagement with prospects through all aspects of targeting and qualification (BANT), with ultimate successful handoff to Regional Account Managers - Perform all job responsibilities in alignment with QOMPLX's core values, mission, and objectives - Achieve or exceed monthly, quarterly and annual demand generation and new opportunity performance objectives - Accurately and timely administer CRM for daily activities, new opportunity related information, and overall sales activity managementQualifications - Reside in the greater Washington D.C. area, with the ability to work daily in QOMPLX's, VA HQ - Bachelor's Degree preferred; a combination of relevant experience and education may be considered - 1-2 years of quota-carrying technology sales, focused on Cyber security, data services and/or IaaS preferred - Proven track record of consistently meeting and exceeding sales quota - Experience in establishing and maintaining relationships at VP and CXO level in a customer organization - Outstanding relationship building skills - High degree of integrity - Energetic, tenacious team player - Passion for helping customers solve complex problems with innovative technology - Strong attention to detail - Ability to work independently and collaborate with other team members - Proficient with Mac applications, MSFT Office, CRM, and social networking tools - Excellent communication, interpersonal and organizational skills.Desirable - Top-performer track record of successful lead generation and opportunity generation leveraging modern social media and outreach platforms - SaaS and on-premise delivery model experience - Career path and growth oriented - a strong desire to take-on additional responsibilities - Experience selling Cyber security, AI/ML, and/or data platform as-a-service solutions ()|
Amazon believes its latest Web Services tool will help doctors spend more time with their patients. The tool, called Amazon Transcribe Medical, allows doctors to easily transcribe patient conversations and add those interactions to someone's medical records with the help of deep learning software.
Funding powerhouse Cambridge Innovation Capital has now attracted £1 billion of investment into Cambridge companies and managing partner Andrew Williamson has hired more Silicon Valley talent to ensure the technology cluster maintains its upward trajectory on the global stage.
The £1bn funding landmark has come within the last five years of CIC’s six-year existence but Williamson tells Business Weekly that Cambridge can leverage even more international capital as it builds on AI, deep learning, life science and therapeutic market leads.
CIC’s leading position as a gateway to accessing world-leading innovation was underlined by its performance in the six months to the end of September.
CIC has simultaneously unveiled Vin Lingathoti, a 10-year Valley veteran in the deep technology sector, as a partner to focus on enterprise software. Williamson himself spent 20 years in the US technology heartland while investment director Michael Anstey excelled at The Boston Consulting Group’s office in Toronto and has advised multinational healthcare businesses across North America, Europe, India, and Japan.
Williamson said the team was wired to help steer a new wave of growth for the Cambridge technology sector. He said: “The high quality of opportunities afforded to CIC as a result of our preferential access to IP from the University of Cambridge and our superior network through the Cambridge ecosystem ensures we are the gateway for accessing world-leading innovation.”
In the six months under review, CIC invested £22.8 million into three new and 11 existing portfolio companies; Riverlane, Sense Biodetection and PredictImmune joined CIC’s portfolio.
CMR Surgical closed a £195m Series C to commercialise its next-generation surgical robotic system, clinching unicorn status at the same time.
Gyroscope Therapeutics raised £50.4m of Series B funding round including from CIC and lead investor Syncona for the development of gene therapies and surgical delivery systems for retinal diseases.
Cytora closed a £25m Series B financing round to develop its artificial intelligence-powered insurance technology platform and PROWLER.io raised $24m to support product expansion and growth in artificial intelligence decision-making. CIC also figured in prominent deals for Storm Therapeutics, Audio Analytic and Bicycle Therapeutics.
Investing from its £275 million first fund, CIC has injected capital into 29 companies to date. Williamson stressed that international big hitters had invested alongside CIC to accumulate the landmark total.
Thrilled with what he called a show of confidence in Cambridge, Williamson said the local deep technology market was set for further unprecedented growth because the cluster was so uniquely endowed with IP-rich, transformative businesses.
In an exclusive interview with Business Weekly, Williamson said Cambridge gloried in companies creating differentiated technologies. These were prolific, thanks in no small measure to Cambridge University and its exponential success in nurturing and spinning out transformational life science and hi-tech companies.
CIC’s close relationship with the university and follow-up companies is evidenced by the fact that 18 of the 29 businesses in whom it has invested to date are Cambridge University spin-outs.
What is less well known is that the astute and highly forensic CIC team has seen around 1500 investment opportunities to date and engaged closely with around 1000 of those without committing investment. So the portfolio businesses are in an elite minority. The common denominator is that besides having great technology they also have the capability to scale rapidly on a global basis, says Williamson.
This Solomon-esque insight makes CIC more than just another investor and more like an anchor institution within the burgeoning cluster.
Williamson told me: “Not every investment opportunity converts immediately; sometimes we bide our time and look at investments over a number of years: 97 per cent of companies we see we don’t invest in but by engaging with them closely we are able to suggest how they can get themselves investment ready.
“So in addition to capital, we invest a lot of time building relationships. And the model works: The quality and number of high-potential companies is growing year by year. We have acknowledged the reality that some companies may be exceptionally IP rich but take a little bit longer to develop than ventures based on more traditional business models.”
This is where CIC’s globally experienced team comes in once more.
Williamson says: “CIC has a lot of PhDS on its teams with deep tech backgrounds. Every business we invest in is a global business and a significant amount of capital we have invested has been globally secured, so we are seen as a safe pair of hands by investing entrepreneurs and funds across the planet.
“A lot of promising deep tech businesses are too small to build into $1bn businesses as things stand which is why it is vital to incorporate a US, Asian and European growth strategy.
“While all our portfolio businesses tend to have started in Cambridge and developed disruptive Science & Technology within the cluster, many have opened up markets on the West Coast of the US or in Asia, for example.
“We see some ventures that are fantastic in terms of their own specific propositions but because of their business model they remain too small currently to build into a global business. For example, we don’t get involved in consumer related brands - it is just not our model. We and our co-investors require that businesses we back have the ability to scale as rapidly as possible.”
Williamson makes the point that the quality of talent emanating from Cambridge University – principally the calibre of its engineers – is second to none on the worldwide stage.
And the best of our companies – such as CMR Surgical and PROWLER.io – have learned how to attract and retain top global talent by offering stimulating work environments and employment packages that encourage good people to stay and grow with the business.
Williamson says: “In the US engineers can move to new roles almost on an annual basis depending on the packages on offer. In Cambridge, engineers are attracted by the quality of the work, they can become shareholders – personally and professionally they are in a very good place here and they tend to stay loyal to a progressive, switched on employer.
“Swim.ai - which started with commercial operations in San Jose – came to Cambridge because of access to the high quality engineering talent and brainpower available through the university. Their model is sustainable: Swim.ai is already hiring big and filling all their slots.
“Hiring top talent to sustain scalability on an international basis is clearly a challenge for the cream of our technology companies locally but businesses like PROWLER.io, CMR Surgical and Swim.ai provide highly productive workplaces, challenging working environments and great incentives to be part of a business that can transform technology sectors globally.
“The ability of our top life science and technology companies to grow sustainably and consistently recruit top people is possibly the greatest cultural change in the Cambridge science & technology environment in the last 20 years.”
Similarly, while many tech entrepreneurs and investors continue to look to Silicon Valley as an exemplar, Cambridge is no longer a generation behind in terms of maturation compared to the West Coast ecosystem.
“Our own ecosystem now compares favourably,” says Williamson. He praised the energy and growing global influence of Cambridge Enterprise, the university’s commercialisation arm, and the prodigious input of financial and business building expertise from Cambridge Angels.
New executive recruit Vin Lingathoti is a software engineer by training and has held roles across multiple functions including engineering, product management, corporate strategy, private equity and corporate development.
Most recently he was regional head of Venture Investments and Acquisitions at Cisco Europe, where he led multiple direct and fund-of-fund investments and played a vital role in helping Cisco’s executive leadership team develop its European investment strategy.
He says: “The Cambridge cluster has many similarities to Silicon Valley. The University of Cambridge produces some of the best engineering talent in the world, on a par with Stanford and MIT.
“It has one of the most active angel and seed investor communities in the UK and is home to prominent deep tech companies such as PROWLER.io and Riverlane. Many of the global software giants such as Microsoft and Amazon have opened R & D centres in Cambridge in pursuit of hard-to-acquire engineering talent.
“CIC is uniquely positioned to leverage this Cambridge advantage. We have a close relationship with the University of Cambridge and deep connections to the local startup community.
“We have an exceptional team of investment professionals with deep domain expertise and global perspective. All of us have lived in multiple countries and held roles in large corporates and startups and understand the challenges faced by early-stage founders. We take a collaborative approach in helping founders navigate through their journey of building world-class companies.”
Stars in the CIC firmament
Riverlane’s software leverages the capabilities of quantum computers, which operate using the principles of quantum mechanics. In the same way that graphics processing units accelerate machine learning workloads, Riverlane uses quantum computers to accelerate the simulation of quantum systems.
Riverlane is working with leading academics and companies on critical early use cases for its software, such as developing new battery materials and drug treatments. The company will use its seed funding to demonstrate its technology across a range of quantum computing hardware platforms, focused on early adopters in materials design and drug discovery. It will also expand its team of quantum software researchers and computational physicists.
Sense Biodetection plans to invest the new funds in the development and manufacture of a range of tests utilising its novel and proprietary rapid molecular amplification technology, targeting in the first instance infectious disease applications such as influenza.
CIC was an early investor in CMR Surgical having first backed the company’s Series A round in 2016 and has continued to provide financial support and guidance, enabling the realisation of the potential of the Versius® system. CMR Surgical has launched initially in hospitals India with further expansion across the NHS and elsewhere globally expected in short order.
Cytora’s underwriting platform applies Machine Learning and Natural Language Processing techniques to public and proprietary data sets, including property construction features, company financials and local weather.
Ημερομηνία: Δευτέρα 25/11, Αίθουσα: Α56
Ομιλητής: Ηλίας Χαλκίδης, Υποψήφιος Διδάκτορας, Οικονομικό Πανεπιστήμιο Αθηνών
Τίτλος: Deep Neural Networks for Information Mining from Legal Texts
|Cache||Diffractive deep neural network is an optical machine learning framework that blends deep learning with optical diffraction and light-matter interaction to engineer diffractive surfaces that collectively perform optical computation at the speed of light. A diffractive neural network is first designed in a computer using deep learning techniques, followed by the physical fabrication of the designed layers of the neural network using e.g., 3-D printing or lithography. Since the connection between the input and output planes of a diffractive neural network is established via diffraction of light through passive layers, the inference process and the associated optical computation does not consume any power except the light used to illuminate the object of interest.|
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