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Setup Transfer Learning Toolkit with Docker on Ubuntu?

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When we talk about Computer vision products, most of them have required the configuration of multiple things including the configuration of GPU and Operating System for the implementation of different problems. This sometimes causes issues for customers and even for the development team. Keeping these things in mind, Nvidia released Jetson Nano, which has its own GPU, CPU, and SDKs, that help to overcome problems like multiple framework development, and multiple configurations. Jetson Nano is good in all perspectives, except memory, because it has limited memory of 2GB/4GB, which is shared between GPU and CPU. Due to this, training of custom Computer Vision models on Jetson Nano is not possible.


BlackBerry, Tesla and Autonomous Car Safety

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At BlackBerry's analyst summit this week, a great deal of time was spent on the company's secure QNX operating system, its IVY platform for software management on cars, and other tools and utilities designed for the next generation of personal transportation. This conversation can't happen soon enough. A growing concern of mine is that automobile companies don't yet seem to fully understand the risk they are taking with platforms that aren't secure enough for products tied to human transportation and safety. Having someone hack your phone or PC is bad, but having someone hack your car could be deadly. So when the industry is talking about putting apps in cars, safety and security should be a far higher priority for many of the automotive OEMs than it seems to be.


3 + 1 ways of running R on Amazon SageMaker

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The R programming language is one of the most commonly used languages in the scientific space, being one of the most commonly used languages for machine learning (probably second following python) and arguably the most popular language amongst mathematicians and statisticians. It is easy to get started with, free to use, with support for many scientific and visualisation libraries. While R can help you analyse your data, the more data you have the more compute power you require and the more impactful your analysis is, the more repeatability and reproducibility is required. Analysts and Data Scientists need to find ways to fulfil such requirements. In this post we briefly describe the main ways of running your R workloads on the cloud, making use of Amazon SageMaker, the end-to-end Machine Learning cloud offering of AWS.


Learning New Things and Avoiding Obstacles

Communications of the ACM

ACM A.M. Turing Award recipient Jack Dongarra never intended to work with computers. Initially, the Distinguished Professor at the University of Tennessee and founder of the Innovative Computing Laboratory (ICL) thought he would be a high school science teacher. A chance internship at the Argonne National Laboratory kindled a lifelong interest in numerical methods and software--and, in particular, in linear algebra, which powered the development of Dongarra's groundbreaking techniques for optimizing operations on increasingly complex computer architectures. Your career in computing began serendipitously, with a semester-long internship at Argonne National Laboratory. As an undergraduate, I worked on EISPACK, a software package designed to solve eigenvalue problems.


Responsible Data Management

Communications of the ACM

Incorporating ethics and legal compliance into data-driven algorithmic systems has been attracting significant attention from the computing research community, most notably under the umbrella of fair8 and interpretable16 machine learning. While important, much of this work has been limited in scope to the "last mile" of data analysis and has disregarded both the system's design, development, and use life cycle (What are we automating and why? Is the system working as intended? Are there any unforeseen consequences post-deployment?) and the data life cycle (Where did the data come from? How long is it valid and appropriate?). In this article, we argue two points. First, the decisions we make during data collection and preparation profoundly impact the robustness, fairness, and interpretability of the systems we build. Second, our responsibility for the operation of these systems does not stop when they are deployed. To make our discussion concrete, consider the use of predictive analytics in hiring. Automated hiring systems are seeing ever broader use and are as varied as the hiring practices themselves, ranging from resume screeners that claim to identify promising applicantsa to video and voice analysis tools that facilitate the interview processb and game-based assessments that promise to surface personality traits indicative of future success.c Bogen and Rieke5 describe the hiring process from the employer's point of view as a series of decisions that forms a funnel, with stages corresponding to sourcing, screening, interviewing, and selection. The hiring funnel is an example of an automated decision system--a data-driven, algorithm-assisted process that culminates in job offers to some candidates and rejections to others. The popularity of automated hiring systems is due in no small part to our collective quest for efficiency.


Methods Included

Communications of the ACM

Although workflows are very popular, prior to the CWL standards, all workflow systems were incompatible with each other. This means that users who do not use the CWL standards are required to express their computational workflows in a different way each time they use another workflow system, leading to local success but global unportability. The success of workflows is now their biggest drawback. Users are locked into a particular vendor, project, and often a specific hardware setup, hampering sharing and reuse. Even non-academics suffer from this situation, as the lack of standards, or their adoption, hinders effective collaboration on computational methods within and between companies.


New Programming Language Gen by MIT Makes AI More Accessible

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The future of technology is all about Artificial Intelligence. The rapidly growing trends of the industry would open doors to various opportunities and new developments. For instance, voice recognition and motion sensors have become built-in features in most devices and people expect to see more of what is coming. Being a major part of computer studies, Artificial Intelligence primarily focuses on the development of machines, which are programmed to work and think like humans. The technology consists of speech recognition, problem-solving, learning, and planning.


Python PCAP-31-03 Certified Associate in Python Programming

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The Practice Questions are dedicatedly designed from a certification exam perspective. The collection of these questions from our Study Guides are prepared to keep the exam blueprint in mind, covering not only important but necessary topics as well. It's an ideal Way to practice and revise your certification. PCAP – Certified Associate in Python Programming certification focuses on the Object-Oriented Programming approach to Python, and shows that the individual is familiar with the more advanced aspects of programming, including the essentials of OOP, the essentials of modules and packages, the exception handling mechanism in OOP, advanced operations on strings, list comprehensions, lambdas, generators, closures, and file processing. PCAP certification gives its holders confidence in their programming skills, helps them stand out in the job market, and gives them a head start on preparing for and advancing to the professional level.


My First Impression Trying Python on Browser

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Whenever we debate with other devs about the best programming language, we talk about JavaScript and Python for hours. Both are powerful, flexible languages that are dominating the world today. But a dead end to Python is its inability to run on browsers. JavaScript (JS), with the discovery of Node, runs on almost any platform. It even has modules to build machine learning algorithms.


Learn the World's Most Popular Programming Language

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In 2022, the world's most popular programming language is one that has been around for decades. Python has stood the test of time by remaining massively scalable, eminently user-friendly, and so general-purpose it's trusted to build everything from websites to machine learning algorithms. If you want to learn to code, Python is a great place to begin. And The Complete 2022 Python Programmer Bundle can help you get started. This eight-course bundle takes a broad approach to Python, starting you with the basics before delving into a number of specific use-cases.