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98-381 Introduction to Programming Using Python

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    TECH giant Microsoft’s protection chief has stated its information superhighway Explorer should quiet now not subsist used as a primary internet browser.

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    Microsoft is revamping its workplace home exhaust program to role discounted office 365 subscriptions | real Questions and Pass4sure dumps

    microsoftofficehomeuse.jpg credit score: Microsoft

    Microsoft industry purchasers who pay for Microsoft's utility Assurance plans occupy had the selection to obtain obtainable discounted copies of workplace for his or her personnel to exhaust at domestic. They've accomplished so by course of a software Assurance advantage called the domestic exhaust program (HUP). HUP is carrying on with, but it's in the middle of some massive adjustments.

    thanks to a heads-up from the licensing specialists at the Licensing faculty U.okay., I currently discovered about those coming HUP alterations.

    The current HUP office improvement enables utility Assurance purchasers to buy office 365 ProPlus (which is the the core set of office apps for home windows, made attainable by course of an workplace 365 subscription) or workplace skilled Plus 2019/workplace home and enterprise 2019 (the non-subscription models of office for windows/Mac for small-enterprise clients) for a discount. The discounted rate for U.S. users is $14.ninety nine for either of these options.

    The up-to-date HUP for workplace alternative will enable company users to purchase office 365 domestic or workplace 365 personal subscriptions at a 30 percent bargain. To qualify, customers deserve to either occupy utility Assurance or a certain variety of workplace 365/Microsoft 365 E3 or E5 licences (2,000 for industrial agencies and educational and nonprofit consumers). there is a (buried) mention of an selection to purchase the non-subscription edition of office 2019 for a $14.99 on Microsoft's licensing web page.)

    There are a brace of other alterations coming to the HUP workplace program. however personnel depart the agencies that has the volume licensing/utility Assurance relationship with Microsoft, they can subsist in a position to continue to exhaust their discounted workplace 365 home/very own subscription as long as they maintain paying for it.

    IT directors managing the HUP software additionally now not achieve this by the exhaust of the quantity License service seat (VLSC) or enterprise core Portal (BCP). as a substitute, Microsoft is directing them to the Microsoft shop for industry for this assignment. The HUP admin web site on VLSC and both ceased operation on the conclusion of January in nations the residence the brand original HUP office application already is available.

    Microsoft has no longer yet launched the original HUP office application in the U.S. A message on the U.S. web site says "the brand original Microsoft domestic exhaust program may subsist launching in the U.S. quickly. you might subsist invited to recrudesce back in early 2019 and raise capabilities of this unique application." it subsist additionally not yet attainable in China, Japan, Russia, Hong Kong and a brace of different nations.

    I asked Microsoft about the factors in the back of its quite a lot of HUP workplace software changes. A spokesperson talked about: "consumers occupy been requesting an workplace HUP providing and these updates are supposititious to aid those requests."

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    Introduction to Programming Using Python

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    5 Free Courses to Learn Python in 2019 | real questions and Pass4sure dumps

    Free Python Courses for Programmers and Developers

    Hello guys, If you are a beginner looking for some Free Python resources to start your programming journey in 2019 then you occupy approach to the prerogative place.

    Earlier, I occupy shared a brace of free Python Programming eBooks sand today I’ll partake a brace of sociable Python programming courses which are absolutely FREE!! You can raise these online courses to learn Python at your own pace, at your own time, and at your place.

    This is a mighty advantage of online learning, the flexibility it provides is just awesome. You just requisite a laptop or a smartphone with an internet connection and you can learn anything.

    Btw, before to start with the list of courses to learn Python programming I want to congratulate you on making the prerogative decision to start your programming journey with Python. Many beginners, students and people starting with programming put a question to this question to me every day. Should I start with Python or Java? Even though I am a Java developer, I put a question to people to start with Python because of its awesome and multi-purpose features.

    Python is effortless to learn, easier compared to even Java. You can besides write petite Python scripts to quickly automate things you normally achieve manually and that provides a mighty value to beginners.

    Python is besides powerful, feature affluent and multi-purpose. For example, you can exhaust Python for web development, you can exhaust it to create scripts, and you can even exhaust it in the space of Data Science and Machine learning.

    This seriously makes learning Python an Important skill which will pay you throughout your career. I occupy always advised all my readers and students run along with SQL and UNIX, you should besides learn Python. One of the mighty programming skill every programmer should have. That’s the judgement I occupy listed is one of the essential tools for programmers.

    5 Free Courses to Learn Python Programming

    Now that you know that learning Python is mighty for your programming career its time to actually learn Python. Whenever I start with a original technology I usually ensue my 3 point model relish combine an online course, buy a book, and achieve a project. This course I occupy mastered several original technologies all by myself without going into expensive coaching classes or boot camps and nothing beats free resources to start with. In the past I occupy shared free books to learn Python and today I will partake some of the best online courses you raise to learn Python Programming for free. The project fraction is something which you can achieve yourself once you learn Python by going through these courses and books.

    1. Introduction To Python Programming

    If you requisite a quick brush-up or learning Python for the first time then this is the faultless course for you.

    This is quite astounding that the instructor himself is a 17-year-old student and this Python course occupy more than 130K students enrolled in Udemy, which speaks volume about the course.  Here is the link to combine the course: Introduction To Python Programming This course is a one-stop-shop for everything you’ll requisite to know to win started with Python, along with a few incentives.

    You will start with the basics of Python, learning about strings, variables, and getting to know the data types. You will then learn other essential programming constructs e.g. loops and conditions in Python.

    The course besides teaches you file manipulation and functions. In short, a Quick and effortless Intro to Python Programming.

    Introduction To Python Programming 2. Python for Absolute Beginners

    This is another mighty introductory course on Python for people who want to learn Python as well as Programming. It doesn’t matter if you’ve had no programming sustain whatsoever. This course starts Python programming from scratch, showing you everything from how to set up a Python IDE to how to exhaust list comprehensions.

    Here is the link to combine the course for FREE: Python for Absolute Beginners

    The best fraction of the course is that every original concept is taught with source code slides and drill problems for you to labor through. You will besides subsist provided with downloadable solutions to the drill problems. This learn-by-doing approach is mighty for beginners who can quickly learn both Programming and Python by following this course.

    Python for Absolute Beginners 3. Programming with Python: Hands-On Introduction for Beginners

    This is another awesome course to learn Python online. The course has been specifically designed for beginners who occupy been looking to obtain a hands-on learning sustain with Python. It teaches you programming concepts prerogative from the basics of Python. Since Python is effortless to learn, it besides makes it effortless to learn to programme.

    Here is the link to combine the course: Programming with Python: Hands-On Introduction for Beginners

    The course besides provides a lot of exercise problems, programming lectures, PDF notes for a complete sustain and everything are free of cost. In short, a mighty Python course for beginners who are passionate to learn to programme even if they haven’t written a single line of code in their entire life and wants to start with Python.

    Programming with Python: Hands-On Introduction for Beginners 4. Python Programming for Absolute Beginners

    This is a mighty practical Python course for beginners with full of examples and exercises, particularly useful for people coming from a different background with no programming experience. This course is exemplar for those who haven’t programmed before, but it should besides labor for experienced programmers who want to learn Python as long as they don’t win offended by a few extra explanations.

    Here is the link to combine the course for FREE: Python Programming for Absolute Beginners You will start from scratch and by the halt of the course you will know how to write Python programs and learn basics about lists, dictionary, and other essential Python data structures.

    You will besides learn about functions, modules and lambda expression in Python.

    Overall a nice beginner course with 2.5 hours of content for free. Talking about gregarious proof the course has got on tolerable 4.3 ratings from nigh to 7000 students.

    Python Programming for Absolute Beginners 5. Learn Python 3 From Scratch

    If you want to learn Object-oriented programming with Python then this is the faultless course for you.

    This course will educate you how to structure your Python code by making exhaust of Classes and Objects. You will besides learn how to achieve object-oriented programming in Python by learning how to bundle attributes and methods within a class and instantiating them through an object.

    Here is the link to combine the course: Learn Python 3 From Scratch

    At the halt of this course, you will subsist able to write your own Python 3 programs in Python!

    Learn Python 3 From Scratch

    That’s on this list of free Python Programming courses for beginners. As I occupy said before, Python is an awesome, multipurpose programming language and every programmer should learn it.

    You can automate things using Python by writing scripts, can achieve object-oriented programming and can even explore the world of web development, data science and machine learning using awesome Python libraries and modules.  Other Free Books and Courses for Programmers 5 Online Courses to learn Core Java for Free 5 Free Courses to Learn Maven and Jenkins 5 Courses to learn Android for Java Developers 5 Free Courses to Learn Data Structures and Algorithms 5 Free Spring Framework Courses for Java Developers 5 Online training courses to learn Angular for Free 5 Node JS courses for Java and Web Developers 5 Courses to Learn majestic Data and Apache Spark 5 Free Courses to Learn Git and Github Thanks for reading this article so far. If you relish these free Python courses then please partake with your friends and colleagues. If you occupy any questions or feedback then please drop a note.

    P.S.- Though these courses are sociable they are not comprehensive enough to cover everything a Programmer needs to know about Python. If you feel so and interested to learn more, I insinuate you combine The Complete Python 3 Bootcamp. It’s not free but it’s completely worth your money.

    This record is published in The Startup, Medium’s largest entrepreneurship publication followed by +415,678 people. Subscribe to receive their top stories here.

    Going deep: Lab employees win an introduction to world of machine learning, neural networks | real questions and Pass4sure dumps

    Deep learning is one of the most approved and widely used machine learning methods due to its success with autonomous vehicles, speech recognition and image classification, to denomination a few emergent technologies. But what exactly is profound learning, and how can it best subsist applied to Lab projects?

    Machine learning scientist Brenda Ng taught a “Deep Learning 101” course, which introduced the basics of neural networks and profound learning to anyone with a basic scholarship of programming in Python. Photos by Julie Russell/LLNL

    Lawrence Livermore National Lab (LLNL) employees discovered the answers during a recent “Deep Learning 101” course, which introduced the basics of neural networks and machine learning to anyone with a basic scholarship of programming in Python. The five-part series, which concluded Jan. 31, included lectures, demonstrations and labs, where attendees performed hands-on labor in coding and training their own neural networks. The teaching staff included CED machine learning scientist Brenda Ng, GS-CAD data scientist Luke Jaffe and CED computer vision scientist Nathan Mundhenk, with uphold from teaching aide and CED machine learning engineer Sam Nguyen.

    “There are many efforts around the Lab that are doing profound learning, but they want to obtain it more accessible to everybody,” Ng said. “Not everybody has a machine learning background, and there’s just so much information out there, in terms of tutorials and blogs, and you’re not sure which ones to even contemplate at. The goal is to give more practical scholarship to their scientists here, so they’re not just reading about it, but they will feel empowered to try some of these models and tools in their work.”

    The learning sequence consisted of five 75-minute classes. Each class started with a lecture by Ng that covered the profound learning topic of the day. The lecture was followed by a programming lab taught by Jaffe that explained the actual code (in the Python machine learning library PyTorch) to implement the model covered in the lecture.

    “We wanted to expose how effortless profound learning can subsist if you exhaust the prerogative tools,” Jaffe said. “We wanted to remove the stigma that profound learning is a complicated black box that can only subsist done with supercomputers. The labs are designed to expose that employees can win tangible improvements to their work, running on a laptop, starting prerogative now.”

    The five-part series, which concluded Jan. 31, included lectures, demonstrations and labs, where attendees performed hands-on labor in coding and training their own neural networks.

    Session topics included convolutional neural networks, generative adversarial networks, autoencoders and recurrent networks. The final class covered a practical tutorial on using Livermore Computing resources to train profound learning models, taught by Mundhenk. The course material was adopted from profound learning workshops and open-source code examples, interweaving the staff’s personal profound learning experiences to afford Lab employees with a attribute learning experience.

    In all, 91 people signed up for the course, which was originally conceived as a machine learning sequence for the Engineering postdocs and was fully funded by the Engineering postdoc program. However, due to overwhelming demand, the sequence was opened to anyone who was interested and had some familiarity with Python programming and library installations, as well as basic calculus and linear algebra.

    “The Engineering postdoc program is pleased to subsist able to uphold this kind of skill expansion and mentorship at the Laboratory,” said Engineering postdoc program coordinator Nathan Barton. “Building abilities to labor across disciplines is a mighty course to invest in workforce development. And people’s sustained interest in expanding their scholarship helps to obtain the Lab a mighty residence to work.”

    Wenqin Li, a original postdoc in the energy utilization and delivery group in CED Engineering, attended the entire sequence and said it Inspired her to find original ways to animate unravel energy problems.

    “DL101 is a mighty class for researchers who are a tenacious believer of applying machine learning to animate unravel their engineering problems,” Li said. “Brenda did a mighty job by explaining the most knotty terms in the simplest way. Luke besides designed very absorbing lab demos too, which provides us mighty hands-on experience.”

    MED postdoc Eric Elton said he wanted to raise the course because had no scholarship of machine learning or AI but thought that getting a better grasp of the fundamentals would animate him subsist better able to labor with others who program machine learning methods.

    “I occupy a much better grasp of how ML works now, not just from a programming perspective but besides from a fundamental perspective,” Elton said. “The course has really taken the mystery out of what exactly the computers are doing when someone says, ‘we used AI to achieve x.’ I’m besides much more sensible of the limits of machine learning now than I was before the course, and why I can’t just put a question to the computer to unravel some equations in the context of this data, or something relish that.”

    Another profound Learning 101 attendee, Farid Dowla, said: “This has been really an excellent class. It’s been a solid discussion on theory, techniques, tools and literature. It will benefit many at LLNL and certainly myself. I occupy been in this district for more than 20 years, and I can descry how things occupy changed recently. I highly recommend the staff in this class. They requisite to occupy more of these.”

    Source: LLNL

    Thread Carefully: An Introduction To Concurrent Python | real questions and Pass4sure dumps

    The skill to execute code in parallel is crucial in a wide variety of scenarios. Concurrent programming is a key asset for web servers, producer/consumer models, batch number-crunching and pretty much any time an application is bottlenecked by a resource.

    It’s sadly the case that writing attribute concurrent code can subsist a real headache, but this article aims to demonstrate how effortless it is to win started writing threaded programs in Python. Due to the large number of modules available in the criterion library which are there to animate out with this kind of thing, it’s often the case that simple concurrent tasks are surprisingly quick to implement.

    We’ll walk through the inequity between threads and processes in a Python context, before reviewing some of the different approaches you can raise and what they’re best suited for.

    (Python 3 is used for the duration of the article.)

    The Global Interpreter Lock

    It’s impossible to talk about concurrent programming in Python without mentioning the Global Interpreter Lock, or GIL. This is because of the large repercussion it has on which approach you select when writing asynchronous Python. The most Important thing to note is that it is only a feature of CPython (the widely used “reference” Python implementation), it’s not a feature of the language. Jython and IronPython, among other implementations, occupy no GIL.

    The GIL is controversial because it only allows one thread at a time to access the Python interpreter. This means that it’s often not feasible for threads to raise advantage of multi-core systems. Note that if there are blocking operations which betide outside Python, long-wait tasks relish I/O for instance, then the GIL is not a bottleneck and writing a threaded program will quiet subsist a benefit. However, if the blocking operations are largely crunching through CPython bytecode, then the GIL becomes a bottleneck.

    Why was the GIL introduced at all? It makes memory management much simpler with no possibility of simultaneous access or race conditions, and it makes C extensions easier to write and easier to wrap.

    The upshot of all this is that if you requisite trusty parallelism and requisite to leverage multi-core CPUs, threads won’t nick it and you requisite to exhaust processes. A separate process means a separate interpreter with separate memory, its own GIL, and trusty parallelism. This pilot will give examples of both thread and process architectures.

    The concurrent.futures module

    The concurrent.futures module is a well-kept secret in Python, but provides a uniquely simple course to implement threads and processes. For many basic applications, the effortless to exhaust Pool interface offered here is sufficient.

    Here’s an case where they want to download some webpages, which will subsist much quicker if done in parallel.

    """Download webpages in threads.""" import requests from concurrent.futures import ThreadPoolExecutor download_list = [ {'name': 'google', 'url': ""}, {'name': 'reddit', 'url': ""}, {'name': 'ebay', 'url': ""}, {'name': 'bbc', 'url': ""} ] def download_page(page_info): """Download and rescue webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': pool = ThreadPoolExecutor(max_workers=10) for download in download_list: pool.submit(download_page, download)

    Most of the code is just setting up their downloader example; it’s only the final obscure which contains the threading-specific code. Note how effortless it is to create a dynamic pool of workers using ThreadPoolExecutor and submit a task. They could even simplify the final two lines to one using map:, download_list)

    Using threads works well in this case since the blocking operation that benefits from concurrency is the act of fetching the webpage. This means that the GIL is not an issue and threading is an exemplar solution. However, if the operation in question was something which was CPU intensive within Python, processes would likely subsist more commandeer because of the restrictions of the GIL. In that case, they could occupy simply switched out ThreadPoolExecutor with  ProcessPoolExecutor.

    The threading module

    Whilst the concurrent.futures module offers a mighty course to win off the ground quickly, sometimes more control is needed over different threads, which is where the ubiquitous threading module comes in.

    Let’s re-implement the website downloader they made above, this time using the threading module.

    """Download webpages in threads, using `threading`.""" import requests import time import threading download_list = [ {'name': 'google', 'url': ""}, {'name': 'reddit', 'url': ""}, {'name': 'ebay', 'url': ""}, {'name': 'bbc', 'url': ""} ] def status_update(): """Print 'Still downloading' at regular intervals.""" while True: print("Still downloading") time.sleep(0.1) def download_page(page_info): """Download and rescue webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': for download in download_list: downloader = threading.Thread(target=download_page, args=(download,)) downloader.start() status = threading.Thread(target=status_update) status.start()

    For each thread they want to create, they obtain an instance of the threading.Thread class, specifying what they would relish their worker role to be, and the arguments required.

    Note that we’ve besides added a status update thread. The purpose of this is to repeatedly print “Still downloading” until we’ve finished fetching all the web pages. Unfortunately, since Python waits for all threads to finish executing before it exits, the program will never exit and the status updater thread will never stop printing.

    This is an case of when the threading module’s legion of options could subsist useful: they can label the updater thread as a daemon thread, which means that Python will exit when only daemon threads are left running.

    status = threading.Thread(target=status_update) status.daemon = True status.start()

    The program now successfully stops printing and exits when all downloader threads are finished.

    Daemon threads are generally most useful for background tasks and repetitive functions which are only required when the main program is running, since a daemon can subsist killed at any moment, causing data loss.

    The combination of threading and queues

    So far we’ve only looked at cases where they know exactly what they want the threads to subsist working on when they start them. However, it’s often the case that they requisite to start a group of worker threads, then feed them tasks as they arrive.

    The best data structure for dealing with these tasks is, of course, a queue, and Python provides a queue module which is especially geared towards threading applications. FIFO, LIFO and priority queues are available.

    Using a queue.Queue object to add, get, and label tasks as done is as simple as this:

    from queue import Queue # maxsize=0 means boundless size limit tasks = Queue(maxsize=0) tasks.put("a task") tasks.put("another task") while not tasks.empty(): print(tasks.get()) # execute task tasks.task_done()

    Ok, that’s pretty basic so far. Now let’s exhaust it to create a tasks queue for their website downloader. We’ll create a group of worker threads which can all access the queue and wait for tasks to approach in.

    """Download webpages in threads, using `threading` and `queue`.""" import requests import threading from queue import Queue NUM_WORKER_THREADS = 3 def download_page(page_info): """Download and rescue webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) def handle_tasks(tasks_queue): """Monitor tasks queue and execute tasks as appropriate.""" while True: download_page(tasks_queue.get()) tasks_queue.task_done() if __name__ == '__main__': tasks = Queue(maxsize=0) # Create and start worker threads for i in range(NUM_WORKER_THREADS): worker = threading.Thread(target=handle_tasks, args=(tasks,)) worker.daemon = True worker.start() # Add some tasks to the queue tasks.put({'name': 'google', 'url': ""}) tasks.put({'name': 'reddit', 'url': ""}) tasks.put({'name': 'ebay', 'url': ""}) tasks.put({'name': 'bbc', 'url': ""}) tasks.join()

    Note that in this case all the tasks were added in one run for the sake of brevity, but in a real application the tasks could trickle in at any rate. Here they exit the program when the tasks queue has been fully completed, using the .join() method.

    The multiprocessing module

    The threading module is mighty for minute control of threads, but what if they want this finer even of control for processes? You might contemplate that this would subsist more challenging since once a process is launched, it’s completely separate and independent – harder to control than a original thread which remains within the current interpreter and memory space.

    Fortunately for us, the Python developers worked hard to create a multiprocessing module which has an interface that is almost identical to the threading module. This means that launching processes follows the exact very syntax as their examples above. Their simple downloader would become this:

    """Download webpages in threads, using `multiprocessing`.""" import requests import time import multiprocessing download_list = [ {'name': 'google', 'url': ""}, {'name': 'reddit', 'url': ""}, {'name': 'ebay', 'url': ""}, {'name': 'bbc', 'url': ""} ] def status_update(): """Print 'Still downloading' at regular intervals.""" while True: print("Still downloading") time.sleep(0.1) def download_page(page_info): """Download and rescue webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': for download in download_list: downloader = multiprocessing.Process(target=download_page, args=(download,)) downloader.start() status = multiprocessing.Process(target=status_update) status.daemon = True status.start()

    We contemplate it’s awesome that Python manages to retain the same syntax between the threading and multiprocessing modules, when the action taking residence under the hood is so different.

    When it comes to distributing data between processes, the queue.Queue that they used for threading will not labor between processes. This is because a queue.Queue is fundamentally just a data structure within the current process – albeit one which is cleverly locked and mutexed. Thankfully there exists a multiprocessing.Queue, which is specifically designed for inter-process communication. Behind the scenes, this will serialize your data and send it through a pipe between processes – a very convenient abstraction.


    Writing concurrent code in Python can subsist a lot of fun due to the inbuilt language features that abstract away a lot of problems. This doesn’t spell that a minute even of control cannot subsist achieved either, but rather that the barrier to getting started with simple tasks is lowered. So when you’re stuck waiting for one process to finish before starting the next, give one of these techniques a try.

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