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Data Science Student Success

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Berkeley Coding Academy teaches Python Programming, Data Analytics, and Machine Learning to teenagers. Our Medium publication includes articles related to data science for parents of teens, teens, and a general audience.


Global Big Data Conference

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Researchers at Duke University have demonstrated that incorporating known physics into machine learning algorithms can help the inscrutable black boxes attain new levels of transparency and insight into material properties. In one of the first projects of its kind, researchers constructed a modern machine learning algorithm to determine the properties of a class of engineered materials known as metamaterials and to predict how they interact with electromagnetic fields. Because it first had to consider the metamaterial's known physical constraints, the program was essentially forced to show its work. Not only did the approach allow the algorithm to accurately predict the metamaterial's properties, it did so more efficiently than previous methods while providing new insights. The results appear online the week of May 9 in the journal Advanced Optical Materials.


13 Free Model Zoos for Deep Learning and Computer Vision Models

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Computer vision is a fast-growing subfield of AI and deep learning. From cashierless stores in retail to crop detection in agriculture, there's an increasing interest in CV applications. This has created a vibrant community that gladly shares architectures, codes, pre-trained models, and even tips for every stage of the development cycle. Starting a CV project from scratch takes time. So, the usual process is, given a problem or a use case, you look for models that partially solve it.


Machine learning engineering: The science of building reliable AI systems

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Machine learning engineering aims to apply software engineering and data science methods to turn machine learning models into usable functions for products and consumers. Artificial intelligence technology is created using machine learning engineering with massive data sets. Machine learning engineering develops AI systems and algorithms to learn and ultimately make predictions. Machine learning engineers are competent software developers who research, design, and implement autonomous programs to create predictive models. Engineers must evaluate, analyze, and organize data, execute experiments, and optimize the training procedure to construct high-performance machine learning models.


Manipulating the future

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As robots evolve, society's collective imagination forever ponders what else robots can do, with recent fascinations coming to life as self-driving cars or robots that can walk and interact with objects as humans do. These sophisticated systems are powered by advances in deep learning that triggered breakthroughs in robotic perception, so that robots today have greater potential for better decision-making and improved functioning in real-world environments. But tomorrow's roboticists need to understand how to combine deep learning with dynamics, controls, and long-term planning. To keep this momentum in robotic manipulation going forward, engineers today must learn to hover above the whole field, connecting an increasingly diverse set of ideas with an interdisciplinary focus needed to design increasingly complex robotic systems. Last fall, MIT's Department of Electrical Engineering and Computer Science launched a new course, 6.800 (Robotic Manipulation) to help engineering students broadly survey the latest advancements in robotics while troubleshooting real industry problems.


Deep Learning Code Generation from Simulink Applications - MATLAB & Simulink

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You can accelerate the simulation of your algorithms in Simulink by using different execution environments. By using support packages, you can also generate and deploy C/C and CUDA code on target hardware. Simulate and generate code for deep learning models in Simulink using MATLAB function blocks. Simulate and generate code for deep learning models in Simulink using library blocks. This example shows how to develop a CUDA application from a Simulink model that performs lane and vehicle detection using convolutional neural networks (CNN).


8 Free MIT Courses to Learn Data Science Online - KDnuggets

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I enrolled into an undergraduate computer science program and decided to major in data science. I spent over $25K in tuition fees over the span of three years, only to graduate and realize that I wasn't equipped with the skills necessary to land a job in the field. I barely knew how to code, and was unclear about the most basic machine learning concepts. I took some time out to try and learn data science myself -- with the help of YouTube videos, online courses, and tutorials. I realized that all of this knowledge was publicly available on the Internet and could be accessed for free.


Traditional Programming And Machine Learning Programming

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Firstly, you need to understand little bit about what is Traditional Programming. Traditional Programming is one of the simplest programming system. You are one who creates the program which then process information according to condition/rules defined by you and you get the output. Let's take an example of traditional programming that you can understand clearly. In this diagram we can see Traditional Programming.


Building Intelligent Systems: A Guide to Machine Learning Engineering: Hulten, Geoff: 9781484234310: Amazon.com: Books

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This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems. Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world. Software engineers, machine learning practitioners, and technical managers who want to build effective intelligent systems.


Why Every Final Year Computer Science Student Should Know About Tmux

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So, back in 2021, we were in the development phase of our final year project. I will start with a little bit of background on the project. It was a classic sentiment analysis study on political parties in the country. There were 3 group members in total, and neither of us had foreseen the kind of hardware difficulties we might face at this stage of development. The thing is, we had no money to invest in those high-end machines to do extensive machine learning tasks.