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    Microsoft is revamping its workplace home Use program to function discounted office 365 subscriptions | Real Questions and Pass4sure dumps

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

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

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    Introduction to Programming Using Python

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    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 have come to the right place.

    Earlier, I have shared a couple of free Python Programming eBooks sand today I’ll share a couple of good Python programming courses which are absolutely FREE!! You can take these online courses to learn Python at your own pace, at your own time, and at your place.

    This is a great advantage of online learning, the flexibility it provides is just awesome. You just need 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 right decision to start your programming journey with Python. Many beginners, students and people starting with programming ask this question to me every day. Should I start with Python or Java? Even though I am a Java developer, I ask people to start with Python because of its awesome and multi-purpose features.

    Python is easy to learn, easier compared to even Java. You can also write small Python scripts to quickly automate things you normally do manually and that provides a great value to beginners.

    Python is also powerful, feature rich and multi-purpose. For example, you can use Python for web development, you can use it to create scripts, and you can even use 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 have always advised all my readers and students go along with SQL and UNIX, you should also learn Python. One of the great programming skill every programmer should have. That’s the reason I have listed is one of the essential tools for programmers.

    5 Free Courses to Learn Python Programming

    Now that you know that learning Python is great for your programming career its time to actually learn Python. Whenever I start with a new technology I usually follow my 3 point model like join an online course, buy a book, and do a project. This way I have mastered several new 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 have shared free books to learn Python and today I will share some of the best online courses you take to learn Python Programming for free. The project part is something which you can do yourself once you learn Python by going through these courses and books.

    1. Introduction To Python Programming

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

    This is quite amazing that the instructor himself is a 17-year-old student and this Python course have more than 130K students enrolled in Udemy, which speaks volume about the course.  Here is the link to join the course: Introduction To Python Programming This course is a one-stop-shop for everything you’ll need to know to get 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 also teaches you file manipulation and functions. In short, a Quick and Easy Intro to Python Programming.

    Introduction To Python Programming 2. Python for Absolute Beginners

    This is another great 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 experience whatsoever. This course starts Python programming from scratch, showing you everything from how to set up a Python IDE to how to use list comprehensions.

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

    The best part of the course is that every new concept is taught with source code slides and practice problems for you to work through. You will also be provided with downloadable solutions to the practice problems. This learn-by-doing approach is great 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 have been looking to obtain a hands-on learning experience with Python. It teaches you programming concepts right from the basics of Python. Since Python is easy to learn, it also makes it easy to learn to programme.

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

    The course also provides a lot of exercise problems, programming lectures, PDF notes for a complete experience and everything are free of cost. In short, a great 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 great 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 ideal for those who haven’t programmed before, but it should also work for experienced programmers who want to learn Python as long as they don’t get offended by a few extra explanations.

    Here is the link to join the course for FREE: Python Programming for Absolute Beginners You will start from scratch and by the end 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 also learn about functions, modules and lambda expression in Python.

    Overall a nice beginner course with 2.5 hours of content for free. Talking about social proof the course has got on average 4.3 ratings from close 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 perfect course for you.

    This course will teach you how to structure your Python code by making use of Classes and Objects. You will also 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 join the course: Learn Python 3 From Scratch

    At the end of this course, you will be 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 have 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 do 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 Big Data and Apache Spark 5 Free Courses to Learn Git and Github Thanks for reading this article so far. If you like these free Python courses then please share with your friends and colleagues. If you have any questions or feedback then please drop a note.

    P.S.- Though these courses are good 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 suggest you join The Complete Python 3 Bootcamp. It’s not free but it’s completely worth your money.

    This story 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 get an introduction to world of machine learning, neural networks | real questions and Pass4sure dumps

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

    Machine learning scientist Brenda Ng taught a “Deep Learning 101” course, which introduced the basics of neural networks and deep learning to anyone with a basic knowledge 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 knowledge of programming in Python. The five-part series, which concluded Jan. 31, included lectures, demonstrations and labs, where attendees performed hands-on work 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 support from teaching assistant and CED machine learning engineer Sam Nguyen.

    “There are many efforts around the Lab that are doing deep learning, but they want to make 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 look at. The goal is to give more practical knowledge 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 series consisted of five 75-minute classes. Each class started with a lecture by Ng that covered the deep 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 show how easy deep learning can be if you use the right tools,” Jaffe said. “We wanted to remove the stigma that deep learning is a complicated black box that can only be done with supercomputers. The labs are designed to show that employees can get tangible improvements to their work, running on a laptop, starting right now.”

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

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

    In all, 91 people signed up for the course, which was originally conceived as a machine learning series for the Engineering postdocs and was fully funded by the Engineering postdoc program. However, due to overwhelming demand, the series 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 be able to support this kind of skill expansion and mentorship at the Laboratory,” said Engineering postdoc program coordinator Nathan Barton. “Building abilities to work across disciplines is a great way to invest in workforce development. And people’s sustained interest in expanding their knowledge helps to make the Lab a great place to work.”

    Wenqin Li, a new postdoc in the energy utilization and delivery group in CED Engineering, attended the entire series and said it Inspired her to find new ways to help solve energy problems.

    “DL101 is a great class for researchers who are a strong believer of applying machine learning to help solve their engineering problems,” Li said. “Brenda did a great job by explaining the most complex terms in the simplest way. Luke also designed very interesting lab demos too, which provides us great hands-on experience.”

    MED postdoc Eric Elton said he wanted to take the course because had no knowledge of machine learning or AI but thought that getting a better grasp of the fundamentals would help him be better able to work with others who program machine learning methods.

    “I have a much better grasp of how ML works now, not just from a programming perspective but also 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 do x.’ I’m also much more aware of the limits of machine learning now than I was before the course, and why I can’t just ask the computer to solve some equations in the context of this data, or something like that.”

    Another Deep 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 have been in this area for more than 20 years, and I can see how things have changed recently. I highly recommend the staff in this class. They need to have more of these.”

    Source: LLNL

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

    The ability 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 quality concurrent code can be a real headache, but this article aims to demonstrate how easy it is to get started writing threaded programs in Python. Due to the large number of modules available in the standard library which are there to help 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 difference between threads and processes in a Python context, before reviewing some of the different approaches you can take 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 impact 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, have 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 possible for threads to take advantage of multi-core systems. Note that if there are blocking operations which happen outside Python, long-wait tasks like I/O for instance, then the GIL is not a bottleneck and writing a threaded program will still be 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 need true parallelism and need to leverage multi-core CPUs, threads won’t cut it and you need to use processes. A separate process means a separate interpreter with separate memory, its own GIL, and true parallelism. This guide 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 way to implement threads and processes. For many basic applications, the easy to use Pool interface offered here is sufficient.

    Here’s an example where they want to download some webpages, which will be 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 save 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 last block which contains the threading-specific code. Note how easy it is to create a dynamic pool of workers using ThreadPoolExecutor and submit a task. They could even simplify the last 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 ideal solution. However, if the operation in question was something which was CPU intensive within Python, processes would likely be more appropriate because of the restrictions of the GIL. In that case, they could have simply switched out ThreadPoolExecutor with  ProcessPoolExecutor.

    The threading module

    Whilst the concurrent.futures module offers a great way to get 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 save 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 make an instance of the threading.Thread class, specifying what they would like their worker function to be, and the arguments required.

    Note that we’ve also 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 example of when the threading module’s multitude of options could be useful: they can mark 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 be 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 be working on when they start them. However, it’s often the case that they need 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 mark tasks as done is as simple as this:

    from queue import Queue # maxsize=0 means infinite 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 use 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 come 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 save 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 example all the tasks were added in one go 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 great for detailed control of threads, but what if they want this finer level of control for processes? You might think that this would be more challenging since once a process is launched, it’s completely separate and independent – harder to control than a new 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 same 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 save 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 think it’s awesome that Python manages to keep the same syntax between the threading and multiprocessing modules, when the action taking place under the hood is so different.

    When it comes to distributing data between processes, the queue.Queue that they used for threading will not work 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 be a lot of fun due to the inbuilt language features that abstract away a lot of problems. This doesn’t mean that a detailed level of control cannot be 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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