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Proofreader
Kewal

Proofreader  


Mac MS Office Proofreading 
$4 /hr
India
Senior Computer Service Engineer and Technical Adviser
Anzar Kunhi

Senior Computer Service Engineer and Technical Adviser  


Mac Apple macOS IT Management 
$15 /hr
India
Sr. System analyst
Kumar Kashyap

Sr. System analyst  


Mac Android iOS 
$99 /hr
India
Business Analyst
Shishir Raj

Business Analyst  


Mac iOS Performance Testing 
$94 /hr
India
Software Developer
Shubham

Software Developer  


Mac CSS Machine Learning 
$4 /hr
India
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Python is one of the fastest growing programming languages. It has undergone more than 28 years of the successful span. Python itself reveals its success story and a promising future ahead. Python programming language is presently being used by a number of high traffic websites including Google, Yahoo Groups, Yahoo Maps, Shopzilla, Web Therapy, Facebook, NASA, Nokia, IBM, SGI Inc, Quora, Dropbox, Instagram and Youtube. Similarly, Python also discovers a countless use for creating gaming, financial, scientific and instructive applications.

 

Python is a fast, flexible, and powerful programing language that's freely available and used in many application domains. Python is known for its clear syntax, concise code, fast process, and cross-platform compatibility.

 

Python is considered to be in the first place in the list of all AI and machine learning development languages due to the simplicity. The syntaxes belonging to python are terribly easy and can be easily learn. Therefore, several AI algorithms will be easily implemented in it. Python takes short development time as compared to different languages like Java, C++ or Ruby. Python supports object oriented, functional as well as procedure oriented styles of programming. There are lots of libraries in python that make our tasks easier.

 

Some technologies relying on python:

Python has become the core language as far as the success of following technologies is concerned. Let’s dive into the technologies which use python as a core element for research, production and further developments.

 

  1. Networking: Networking is another field in which python has a brighter scope in the future. Python programming language is used to read, write and configure routers and switches and perform other networking automation tasks in a cost-effective and secure manner.
  2. Big Data: The future scope of python programming language can also be predicted by the way it has helped big data technology to grow. Python has been successfully contributing in analyzing a large number of data sets across computer clusters through its high-performance toolkits and libraries.
  3. Artificial Intelligence (AI): There are plenty of python frameworks, libraries, and tools that are specifically developed to direct Artificial Intelligence to reduce human efforts with increased accuracy and efficiency for various development purposes. It is only the Artificial Intelligence that has made it possible to develop speech recognition system, interpreting data like images, videos etc.

 

Why Choose Python for Artificial Intelligence and Machine Learning?

Whether a startup or associate MNC, Python provides a large list of benefits to all. The usage of Python is specified it cannot be restricted to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complicated processes like artificial intelligence (AI), Machine Learning (ML), natural language process, data science etc. The question is why Python is gaining such momentum in AI? And therefore the answer lies below:

 

Flexibility: Flexibility is one of the core advantages of Python. With the option to choose between OOPs approach and scripting, Python is suitable for every purpose. It works as a perfect backend and it also suitable for linking different data structures together.

 

Platform agnostic: Python provides developer with the flexibility to provide an API from the existing programming language. Python is also platform independent, with just minor changes in the source codes, you can get your project or application up and running on different operating systems.

 

Support: Python is a completely open source with a great community. There is a host of resources available which can get any developer up to speed in no time. Not to forget, there is a huge community of active coders willing to help programmers in every stage of developing cycle.

 

Prebuilt Libraries: Python has a lot of libraries for every need of your AI project. Few names include Numpy for scientific computation, Scipy for advanced computing and Pybrain for machine learning.

 

Less Code: Python provides ease of testing - one of the best among competitors. Python helps in easy writing and execution of codes. Python can implement the same logic with as much as 1/5th code as compared to other OOPs languages.

 

Applications of Python:

There are so many applications of Python in the real world. But over time we’ve seen that there are three main applications for Python

Web Development: Web frameworks that are based on Python like Django and Flask have recently become very popular for web development.

Data Science (including Machine Learning): Machine Learning with Python has made it possible to recognize images, videos, speech recognition and much more.

Data Analysis/Visualization: Python is also better for data manipulation and repeated tasks. Python helps in the analysis of a large amount of data through its high-performance libraries and tools. One of the most popular Python libraries for the data visualization is Matplotlib.

 

Nowadays, Android is not just on tens of thousands of different phones and tablets. It’s on your wrist, in your living room, in your car, and as soon we start assigning IP addresses to inanimate objects, it is going to be pretty much everywhere around us. A lot of ground for even an experienced Android developer to cover!

 

Also there are over one million apps on Google Play, not counting Amazon AppStore or markets we are not generally interested in.

 

So, how can an independent developer create a successful app in this huge market with big players? I have no idea, I haven’t made a successful app! But, I have made a cute one, and I’d like to share my story with you.

Few Android Studio Tips, Tricks & Resources you should be familiar with, as an Android Developer

The reason of this article, is to list helpful tips, tricks, shortcuts and resources for Android Studio that will improve your overall efficiency and performance.

 

1.Material Colors theme for Android Logcat

To change Android Studio Logcat you need to go to:Preferences (Settings on Windows / Linux machines) → Editor → Colors & Fonts → Android Logcat and change the foreground color for every type of log.

 

2. Prevent Android Studio Logcat from clearing the log for the current application when it crashes. 

To do that you need to go to theAndroid Monitorpanel and chooseEdit filter configurationon the right side dropdown.

 

3. Apply a proper code style to your IDE (IntelliJ / Android Studio). 

Go toPreferences → Code Style → Javaand in aSchemedropdown you can choose your code style (or set up a new one).

2 styles which are especially worth to mention:

Square Java Code Styles with Android

Google Java Code Style.

 

4. Use split screen for increasing efficiency.

To turn this feature on, you need to right mouse click on the tab of your main screen and choose Split Vertically / Horizontally feature.

But to be as efficient as possible we need to set up a custom keyboard’s shortcut. To do that go to Preferences → Keymap and search for Split Vertically. Then open a context menu and click Add Keyboard Shortcut

It is shown on a gif below. You can define the shortcut for a horizontal split view as well.

 

5. Distraction Free Mode. 

You can enable it by going to: View → Enter Distraction Free Mode

 

6. Use Live Templates

you can use many already defined templates, like for Toasts or if conditions.

you can use your own custom templates. Here is a great reference article by Reto Meier. You can also refer to the IntelliJ IDEA documentation.

 

Learn XML And Java

Extensible markup language (XML) is a markup language that’s the foundation for the Android user interface (UI) and elements. Java is the back-end coding language of the Android platform, and it’s used with binding functionality with your XML layouts. Let’s take a look at a small example.

 

<?xml version="1.0" encoding="utf-8"?> <layout xmlns:android=http://schemas.android.com/apk/res/android"> <data> <variable name="user" type="com.example.User"/> </data> <LinearLayout android:orientation="vertical" android:layout_width="match_parent" android:layout_height="match_parent"> <TextView android:layout_width="wrap_content" android:layout_height="wrap_content" android:text="@{user.firstName}"/> <TextView android:layout_width="wrap_content" android:layout_height="wrap_content" android:text="@{user.lastName}"/> </LinearLayout> </layout>

 

This small layout has one data object defined as “User.” For developers used to binding elements in even web design, this should be intuitive for you. The data element is defined in Java, and then the object data is bound to the front-end layout element. In this example, we have a first and last name. The following Java code is an example of a User class used for the binding.

 

public class User { public final String firstName; public final String lastName; public User (String firstName, String lastName){ this.firstName = firstName; this.lastName = lastName; } }

Notice that the constructor takes a first and last name variable and binds the data to the public class properties. When the screen opens on the device, this data is then bound to the front-end elements.

 

Before you get started in Android Application development, you should be expert in both of these technologies. Understanding the fundamentals of XML formats is relatively easy, but Java is an object-oriented language and object-oriented programming (OOP) can be challenging if you’re not familiar with the concept. A newer language and framework, Kotlin, has been introduced to help advance Android app development. It can also be used with legacy Java apps as Kotlin co-operates with Java code.

 

The best way to learn Java is to dive into someone else’s code. GitHub is filled with open-source projects that help you understand Java and OOP concepts. The better your OOP skills are, the easier it will be to create a better coded, less buggy Android app.

 

Understand Memory Limitations

Mobile devices don’t have the high memory limitations that desktops have. In mobile programming, you must learn to code with smaller memory limitations in mind. If you carelessly use memory on an Android device, you run the risk of your app crashing due to “out of memory” errors.

To work around this, use services on the device sparingly and release memory resources when your app is hidden. You can release memory resources by overriding the onTrimMemory event although you should know that this isn’t a magic solution that allows you to ignore other performance optimization throughout your code. The following is a small sample of pseudocode that shows you how to override the event.

@Override public void onTrimMemory(int level) { if (level >= TRIM_MEMORY_MODERATE) {

//release resources here } }

Google also recommends that you avoid or at least reduce the use of bitmaps to lower the memory use when your app is running on the device. 

 

Don’t Use The Emulator For Real-World Testing

The Emulator in Android Studio is good for development and even debugging, but it doesn’t fully simulate a working Android device. You can consider basic environment features with the Emulator, but when it comes to testing, you should always use actual devices.

Of course, this is a huge investment for an Android developer, but it will make the entire development and testing life cycle much more efficient. It also ensures that you’re able to test your applications in various conditions as the user changes settings on the actual device. You can, however, use automated cloud solutions such as Amazon’s Testing platform.

 

Testing should never be taken lightly. It should be a major part when you design and deploy apps for your clients. Fewer bugs means happier users, and it can also mean a more successful app in Google Play. This doesn’t mean that you need to buy every device on the market to test your app on, but you should account for some of the more popular ones and the most common screen sizes.

Now a days, the popularity of scientific computing environments such as IDL, Maple, Mathematica, Matlab and R has increased considerably. Engineer simply feel more productive in such environments. One reason is the simple and clean syntax of command languages in these environments. Another factor is tight integration of simulation and visualization in Maple, R and similar environments you can quickly and conveniently visualize what you just have computed. One problem with the mentioned environments is that they do not work, at least not in an easy way, with other types of numerical software and visualization systems. Many of the environment specific programming languages are also quite simple or primitive. At this point scripting in Python comes in.

 

Python offers the clean and simple syntax of the popular scientific computing environments, the language is very powerful, and there are lots of tools for simulation, visualization, and data analysis programs. Python allows you to build your own Matlab like scientific computing environment, tailored to your specific needs and based on your favorite high performance FORTRAN, C, or C++ codes.

 

Scientific Computing Is More Than Number Crunching: Many computational scientists work with their own numerical software development and realize that much of the work is not only writing computationally intensive number-crunching loops. Very often programming is about shuffling data in and out of different tools, converting one data format to another, extracting numerical data from a text, and administering numerical experiments involving a large number of data files and directories. Such tasks are much faster to accomplish in a language like Python than in FORTRAN, C, C++, and C#.

 

Scripting is particularly attractive for building demos related to teaching or project presentations. Such demos benefit greatly from a GUI, which offers input data specification, calls up a simulation code, and visualizes the results. The simple and intuitive syntax of Python encourages users to modify and extend demos on their own, even if you are newcomers to Python.

 

Python has some clear advantageous over Matlab and similar environments:

  • The Python programming language is more powerful.
  • The Python environment is completely open and made for integration with external tools.
  • A complete toolbox/module with lots of functions and classes can be contained in a single file.
  • Transferring functions as arguments to functions is simpler.
  • Nested, heterogeneous data structures are simple to construct and use.
  • Object-oriented programming is more convenient.
  • Interfacing C, C++, and FORTRAN code is better supported and therefore simpler.
  • Scalar functions work with array arguments to a larger extent (without modifications of arithmetic operators).
  • The source is free and runs on more platforms.

 

How to run Python script

One of the most important skills you need to build as a Python developer is to be able to run Python scripts and code. This is going to be the only way for you to know if your code works as you planned. It’s even the only way of knowing if your code works at all!

 

A Python script is a reusable set of code which is essentially a Python program or a sequence of Python instructions contained in a file. You can run the program by specifying the name of the script file to the interpreter. 

 

This step-by-step will guide you through a series of ways to run Python scripts, depending on your environment, platform, needs, and skills as a programmer. When you try to run Python scripts, a multi-step process begins. 

 

  1. Run Python Scripts Using the Command-Line: A Python interactive session will allow you to write a lot of lines of code, but once you close the session, you lose everything you’ve written. That’s why the usual way of writing Python programs is by using plain text files. By convention, those files will use the .py extension. Open a command-line and type in the word ‘python’ followed by the path to script file and press enter. You’ll see output on your screen.
  2. Run Python Scripts Interactively: It is also possible to run Python scripts and modules from an interactive session. This option offers you a variety of possibilities.
    • Taking advantage of import
    • Use importlib and imp
    • Use runpy.run_module()
    • Hacking exec()
    • Use execfile()
  3. Run Python Scripts from an IDE or a Text Editor: IDE offer the possibility of running your scripts from inside the environment itself. It is common for them to include a Run or Build command, which is usually available from the tool bar or from the main menu.
  4. Run Python Scripts From a File Manager: Running a script by double-clicking on its icon in a file manager is another possible way to run your Python scripts. This option may not be widely used in the development stage, but it may be used when you release your code for production.

 

After you play around with Python on your own or in an online tutorial, I highly recommend to you to write small scripts to strengthen your knowledge. To stay motivated, choose a program that is in some way useful to you, so you can gain insight while figuring out Python. Below are a few ways you can begin to build your expert level in Python script:

 

  • Python Documentation
  • Google and stackoverflow
  • Ask an experience person

 

First, create a very basic version end-to-end. It is much less frustrating than trying to build a super-duper version from scratch. A big plus is that you will have something you can use very fast. Then iterate and add more complex functionality one by one.

 

Second, decompose large problems to smaller ones by introducing functions. Small, cohesive functions are easy to understand, test and debug.

 

Last, but probably the most important thing to keep in mind, is practice makes perfect. Start small, be patient and practice. Happy coding!

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