Created by Jitesh Khurkhuriya, Python, Data Science & Machine Learning A-Z TeamPreview this Course - GET COUPON CODE This python programming masterclass is a practical course for complete beginners as well as existing programmers. This course has more than 100 hands on, assignments and projects. We have designed this course in such a manner that at the end of this Python Bootcamp, you can apply for jobs as Python Developer, ace your Python interviews and start working on a real-life Python projects. As part of working on various projects, you will create your own, Interactive phonebook using Python Lists Do Financial File reconciliation with Python Files Employee Salary Processing for Bonus Calculation Tracker by monitoring the Timekeeping and Logging using decorators Weather Forecasting Web app Payment gateway Integration using Razorpay Python SDK As of now, no Python programming course on Udemy teach payment gateway integration during a Python Programming Masterclass. Python is one of the hottest skills in demand. According to the Stackoverflow developer survey 2020, python is the most wanted programming language by the professional developers. Python is also ranked way above the other traditional programming languages as on July 2020. Top employers such as google where Python is one of the official server side languages and Instagram which runs the largest deployment of python web framework and many other top employers hire python developers No wonder then, python developers earn average $119,053 per year as per a report by Indeed. Today, python is used for Web Development, Mobile app development, Data Science and Machine learning, Game Development, Cloud and various other automations.

Transfer learning (TL) tries to utilize data or knowledge from one or more source domains to facilitate the learning in a target domain. It is particularly useful when the target domain has few or no labeled data, due to annotation expense, privacy concerns, etc. Unfortunately, the effectiveness of TL is not always guaranteed. Negative transfer (NT), i.e., the source domain data/knowledge cause reduced learning performance in the target domain, has been a long-standing and challenging problem in TL. Various approaches to overcome NT have been proposed in the literature. However, there has not been a systematic survey on overcoming NT. This paper fills the gap, by categorizing and reviewing near 100 approaches for combating NT, from four perspectives: source data quality, target data quality, domain divergence, and integrated algorithms. NT in related fields, e.g., multi-task learning, multilingual models, and lifelong learning, is also discussed.

Deep Neural Networks (DNNs) have achieved remarkable success in many computer vision tasks recently, but the huge number of parameters and the high computation overhead hinder their deployments on resource-constrained edge devices. It is worth noting that channel pruning is an effective approach for compressing DNN models. A critical challenge is to determine which channels are to be removed, so that the model accuracy will not be negatively affected. In this paper, we first propose Spatial and Channel Attention (SCA), a new attention module combining both spatial and channel attention that respectively focuses on "where" and "what" are the most informative parts. Guided by the scale values generated by SCA for measuring channel importance, we further propose a new channel pruning approach called Channel Pruning guided by Spatial and Channel Attention (CPSCA). Experimental results indicate that SCA achieves the best inference accuracy, while incurring negligibly extra resource consumption, compared to other state-of-the-art attention modules. Our evaluation on two benchmark datasets shows that, with the guidance of SCA, our CPSCA approach achieves higher inference accuracy than other state-of-the-art pruning methods under the same pruning ratios.

Artificial Intelligence (AI) or Machine Learning (ML) systems have been widely adopted as value propositions by companies in all industries in order to create or extend the services and products they offer. However, developing AI/ML systems has presented several engineering problems that are different from those that arise in, non-AI/ML software development. This study aims to investigate how software engineering (SE) has been applied in the development of AI/ML systems and identify challenges and practices that are applicable and determine whether they meet the needs of professionals. Also, we assessed whether these SE practices apply to different contexts, and in which areas they may be applicable. We conducted a systematic review of literature from 1990 to 2019 to (i) understand and summarize the current state of the art in this field and (ii) analyze its limitations and open challenges that will drive future research. Our results show these systems are developed on a lab context or a large company and followed a research-driven development process. The main challenges faced by professionals are in areas of testing, AI software quality, and data management. The contribution types of most of the proposed SE practices are guidelines, lessons learned, and tools.

Recently, a standardized framework was proposed for introducing quantum-inspired moves in mathematical games with perfect information and no chance. The beauty of quantum games-succinct in representation, rich in structures, explosive in complexity, dazzling for visualization, and sophisticated for strategic reasoning-has drawn us to play concrete games full of subtleties and to characterize abstract properties pertinent to complexity consequence. Going beyond individual games, we explore the tractability of quantum combinatorial games as whole, and address fundamental questions including: Quantum Leap in Complexity: Are there polynomial-time solvable games whose quantum extensions are intractable? Quantum Collapses in Complexity: Are there PSPACE-complete games whose quantum extensions fall to the lower levels of the polynomial-time hierarchy? Quantumness Matters: How do outcome classes and strategies change under quantum moves? Under what conditions doesn't quantumness matter? PSPACE Barrier for Quantum Leap: Can quantum moves launch PSPACE games into outer polynomial space We show that quantum moves not only enrich the game structure, but also impact their computational complexity. In settling some of these basic questions, we characterize both the powers and limitations of quantum moves as well as the superposition of game configurations that they create. Our constructive proofs-both on the leap of complexity in concrete Quantum Nim and Quantum Undirected Geography and on the continuous collapses, in the quantum setting, of complexity in abstract PSPACE-complete games to each level of the polynomial-time hierarchy-illustrate the striking computational landscape over quantum games and highlight surprising turns with unexpected quantum impact. Our studies also enable us to identify several elegant open questions fundamental to quantum combinatorial game theory (QCGT).

Enterprises now acknowledge the value of having a highly capable workforce. The ever-changing business landscape demands workers to continually upskill and reskill, giving rise to employee training utilization. Most organizations are now reinforcing their human resources and training and development departments to help them address the need. Large US companies on the average spent $17.7 million on such efforts in 2019. Managing employee training, however, has its own set of challenges.

Develop Your Career in Data Science With Complete Data Science Roadmap 2020 for Data Scientist Learning Path. Start your career in Data Science with Complete Data Science Roadmap 2020 Learning Path which includes online courses and an E-book on Data Science. Learning Step by step guide to understand and build your skills in Data Science. This Complete Roadmap for Data Scientist Courses Path includes hand-picked courses which will help you to learn Data Science without any difficulties. It includes Python and Data Science, a Premium E-Book on Data Science, and Projects in Data Science.

"It should be fun to work with artificial intelligence tools," says Nancye Blair Black, Project Lead for AI Explorations and Their Practical Use in School Environments. It's a simple credo but one that belies both a deep passion for computational thinking and an urgent desire to see this project succeed in its primary objective: to cultivate a broad and diverse group of future AI users and developers. Black sees endless opportunities to introduce AI across the curriculum to students of all ages and backgrounds. As the program--a collaboration between ISTE and General Motors--wraps up its third year of professional learning opportunities and support for educators, she says, "this is really the end of the beginning of the work." We spoke with Black recently about the work that she and her colleagues are doing, what lies ahead and why she's so excited about the potential of artificial intelligence in the classroom. EdSurge: What do you hope to achieve with the AI Explorations project?

Many of us may not even understand exactly where or what the Cloud is. Yet, much of the data and programs that control our lives live on this Cloud of distant computer servers with the directions to run our devices coming over the Internet. As the prevalence of artificial intelligence (AI)-driven devices grows, researchers would like to bring some of that decision-making back to our own devices. WSU researchers have developed a novel framework to more efficiently use AI algorithms on mobile platforms and other portable devices. They presented their most recent work at the 2020 Design Automation Conference and the 2020 International Conference on Computer Aided Design.

You've probably heard us say this countless times: GPT-3, the gargantuan AI that spews uncannily human-like language, is a marvel. You can tell with a simple trick: Ask it the color of sheep, and it will suggest "black" as often as "white"--reflecting the phrase "black sheep" in our vernacular. That's the problem with language models: because they're only trained on text, they lack common sense. Now researchers from the University of North Carolina, Chapel Hill, have designed a new technique to change that. They call it "vokenization," and it gives language models like GPT-3 the ability to "see."