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Remote Sensing Scientist at Leidos in Arlington, VA

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Want to be a part of an elite team where our innovative technical solutions are delivered to customers that advance the state of the art while addressing long-term problems of importance to national security? At our Leidos' Multi-Spectrum Warfare Research and Analytics Systems (MSWRAS) Division, an organization in the Leidos Innovation Center (LInC), we are looking for you, our next Scientist who specializes in remote sensing data analytics. Join our team of Ph.D. level peers in designing and developing advanced technology-based solutions for contract research and development projects working in our Arlington, VA office. Fun roles you will have in this job: Describe instances of successful, proven, and demonstrable experience contributing to the technical work as part of cross-discipline teams in the development and integration of software-based solutions for competitive, contract-based applied research programs Work with teams composed of members from industry, small businesses, and academic-based researchers and should have experience working on projects focused on multiple technical fields such as machine learning, artificial intelligence, engineering, and software development and integration Describe how the work products to which they contributed had solved customers' problems in such domains as energy, health, and national security or in the commercial sector Work within the MSWRAS Division and across the LInC, performing basic and applied contract research and development projects both leading and working under the guidance of senior scientists and engineers. Processing, interpreting and analyzing large volumes of data collected by remote sensing platforms but may also include other types of phenomenological data such as field measurements, or weather data Independently design and undertake new research as well as partner in a team environment across organizations Contribute to the development of creative and innovative R&D approaches to solving major remote sensing analytics challenges and work with potential sponsors (customers or internal champions) to secure funding for new research efforts based on those topics Contribute to the productivity of teams composed of fellow researchers, data scientists, data engineers, and software engineers to execute complex R&D programs Under the guidance of a senior scientist or engineer, design and develop or integrate secure and scalable applications that are part of broader solutions, that are applicable across multiple domains.


Boltzmann machine learning with a variational quantum algorithm

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Boltzmann machine is a powerful tool for modeling probability distributions that govern the training data. A thermal equilibrium state is typically used for Boltzmann machine learning to obtain a suitable probability distribution. The Boltzmann machine learning consists of calculating the gradient of the loss function given in terms of the thermal average, which is the most time consuming procedure. Here, we propose a method to implement the Boltzmann machine learning by using Noisy Intermediate-Scale Quantum (NISQ) devices. We prepare an initial pure state that contains all possible computational basis states with the same amplitude, and apply a variational imaginary time simulation. Readout of the state after the evolution in the computational basis approximates the probability distribution of the thermal equilibrium state that is used for the Boltzmann machine learning. We actually perform the numerical simulations of our scheme and confirm that the Boltzmann machine learning works well by our scheme.


Deployment of Machine Learning Models

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Online Courses Udemy - Deployment of Machine Learning Models Build Machine Learning Model APIs Created by Soledad Galli, Christopher Samiullah English [Auto] Students also bought Data Science: Natural Language Processing (NLP) in Python Recommender Systems and Deep Learning in Python Artificial Intelligence: Reinforcement Learning in Python Unsupervised Machine Learning Hidden Markov Models in Python Deep Learning: Recurrent Neural Networks in Python Preview this course GET COUPON CODE Description Learn how to put your machine learning models into production. Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate.


Python for Beginners: Anyone Can Code

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Make your computer talk, draw graphics, and create an arcade game. Created by Matt Bohn Students also bought Unsupervised Machine Learning Hidden Markov Models in Python Data Science: Supervised Machine Learning in Python Python and Django Full Stack Web Developer Bootcamp The Python Bible Everything You Need to Program in Python Complete Python Developer in 2020: Zero to Mastery Preview this course GET COUPON CODE Description Learn to Code with Simple and Fun Hands On Videos Do you want to learn to code? Maybe you are interested in programming as a career or a hobbyist who wants to create code for your own projects? Or, maybe you're a parent with a student who would love to write code. If so then this is the course you're looking for.


Software development in Python: A practical approach

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Online Courses Udemy - Software development in Python: A practical approach Learn to build real apps with python NEW Created by Daniel IT English [Auto] Students also bought Data Science: Deep Learning in Python Advanced AI: Deep Reinforcement Learning in Python Deep Learning Prerequisites: Linear Regression in Python Unsupervised Machine Learning Hidden Markov Models in Python 2020 Complete Python Bootcamp: From zero to hero in Python Preview this course GET COUPON CODE Description The reason I got into python, I wanted to be a software engineer, I had just built a chat app in PHP and JQuery and a girl asked me if it could run on phone. I responded yes, but I knew that would only be possible using help using non-native means. I wanted native builds, not some complex framework which will only allow me to make a web app whiles I could use the time to study a full fledge programming language. There were others like making a web view app, I didn't like the Idea because there would definetely be setbacks. And I also wanted to be a software engineer or developer, I had built two almost identical CMSs with PHP and I felt I was ready to move into the software development space.


Deep Learning Prerequisites: Logistic Regression in Python

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Online Courses Udemy | Deep Learning Prerequisites: Logistic Regression in Python Data science techniques for professionals and students - learn the theory behind logistic regression and code in Python BESTSELLER Created by Lazy Programmer Inc.  English [Auto-generated], Portuguese [Auto-generated], 1 more Students also bought Natural Language Processing with Deep Learning in Python Data Science: Natural Language Processing (NLP) in Python Deep Learning: Advanced Computer Vision (GANs, SSD, +More!) Unsupervised Machine Learning Hidden Markov Models in Python Modern Deep Learning in Python Preview this course GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes


Advanced AI: Deep Reinforcement Learning in Python

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Online Courses Udemy Advanced AI: Deep Reinforcement Learning in Python, The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks Created by Lazy Programmer Team, Lazy Programmer Inc. English [Auto-generated], Indonesian [Auto-generated], 5 more Students also bought Deep Learning: Convolutional Neural Networks in Python Deep Learning: Recurrent Neural Networks in Python Unsupervised Machine Learning Hidden Markov Models in Python Bayesian Machine Learning in Python: A/B Testing Data Science: Supervised Machine Learning in Python Preview this course GET COUPON CODE Description This course is all about the application of deep learning and neural networks to reinforcement learning. If you've taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Reinforcement learning has been around since the 70s but none of this has been possible until now. The world is changing at a very fast pace.


Agent-Centered Search

AI Magazine

In this article, I describe agent-centered search (also called real-time search or local search) and illustrate this planning paradigm with examples. Agent-centered search methods interleave planning and plan execution and restrict planning to the part of the domain around the current state of the agent, for example, the current location of a mobile robot or the current board position of a game. These methods can execute actions in the presence of time constraints and often have a small sum of planning and execution cost, both because they trade off planning and execution cost and because they allow agents to gather information early in nondeterministic domains, which reduces the amount of planning they have to perform for unencountered situations. These advantages become important as more intelligent systems are interfaced with the world and have to operate autonomously in complex environments. Agent-centered search methods have been applied to a variety of domains, including traditional search, strips-type planning, moving-target search, planning with totally and partially observable Markov decision process models, reinforcement learning, constraint satisfaction, and robot navigation.


#111 Machine Learning with TensorFlow with Chris Mattmann – Author / Manager, Chief Technology and Innovation Officer -- DATA FUTUROLOGY PODCAST

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Chris Mattmann is the Deputy Chief Technology and Innovation Officer at NASA Jet Propulsion Lab, where he has been recognised as JPL's first Principal Scientist in the area of Data Science. Chris has applied TensorFlow to challenges he's faced at NASA, including building an implementation of Google's Show & Tell algorithm for image captioning using TensorFlow. He was involved in the Mars rover landing mission, where he was working in a planetary data system engineering node, helping to build a data management framework called object-oriented data technology to support capturing, processing and sharing of data for NASA's scientific archives. He contributes to open source as a former Director at the Apache Software Foundation, and teaches graduate courses at USC in Content Detection and Analysis, and in Search Engines and Information Retrieval. In this episode, Chris opens the show discussing his interest in data.


Improving Convolutional Neural Networks for Text Coherence Modelling using Class Balancing…

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Lately, Deep Learning is gaining huge popularity due to its supremacy in terms of accuracy when it comes to very complex problems. It proved efficiency in NLP and was widely adopted by many problems to address them opening new doors for more meaningful and accurate modeling approaches. While many problems in NLP involve text syntheses such as text generation and multi-document summarization, text quality measures became a core requirement, and modeling them is an active problem. Of these measures, the problem of text coherence is key and needs special handling. Text coherence, which means the degree of the logical consistency of text, is a problem that dates back to the 1980s, where several models were suggested.