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Python NumPy Tutorial – Learn NumPy Arrays With Examples

In myprevious blog, you have learned about Arrays in Python and its various fundamentals like functions, lists vs arrays along with its creation.But, those were just the basics and with Python Certification being the most sought-after skill in the programming domain today, there’s obviously so much more to learn.

It is the core library for scientific computing, which contains a powerful n-dimensional array object, provide tools for integrating C, C++ etc.

In order to perform these numpy operations, the next question which will come in your mind is: To install Python NumPy, go to your command prompt and type “pip install numpy”.

We use python numpy array instead of a list because of the below three reasons: The very first reason to choose python numpy array is that it occupies less memory as compared to list.

Don’t worry, I am going to prove the above points one by one practically in PyCharm.Consider the below example: import numpy as np import

The above outputshows that the memory allocated by list (denoted by S) is 14000 whereas the memory allocated by the numpy array is just 4000.

From this, you can conclude that there is a major difference between the two and this makes python numpy array as the preferred choice over list.

Then, we have compared the time taken in order to find the sum of lists and sum of numpy arrays both.

loop for a list which returns the concatenation of both the lists whereas for numpy arrays, wehave just added the two array by simply printingA1+A2.

A step-by-step neural network tutorial for beginners

The neural network is a technology based on the structure of the neurons inside a human brain.

Each neuron will try to stimulate other neurons via its Axon terminals and tell which terminal should active and which ones remain inactive.

By doing that over and over across multiple neurons (FYI, we have around 100 billions of neuron in our brain), our brain can process complex things and solving problems.

But, it was Geoffrey Hinton makes this algorithm comes to the surface via his learning algorithm, called Backpropagation In simple term, a Neural network algorithm will try to create a function to map your input to your desired output.

As an example, you want the program output “cat” as an output, given an image of a cat.

The cat image is the input in the input layer, while “cat” will be on the output layer.

And FYI, solving MNIST with the very simple Neural network could get you to 95% accuracy without trying to do any fine tuning.

The image categories are: The purpose of the tutorial is to accurately assign each item into one of the ten categories.

Generally, a larger amount of training data quantity will make your Neural Network better understand your data distribution.

If the network performs well on the test data, you can bring the network to the production level.

70% of the data are split into training, 10% into validation, and 20% into the test set.

The data provided by Keras is already split between the training and testing sets, with 60K for training and 10k for testing.

Then do the set-up imports: The fashion MNIST dataset is already included inside Keras’ own collection.

Your training data x_train is transformed from 60,000 x 28 x 28 to 60,000 x 784.

Your testing data x_test follows suit, from 10,000 x 28 x 28 to 10,000 x 784.

While for the output layer, because we have ten categories to categorize, we need to set it to 10 output neurons.

In an example, if you have a Sandal image, then the output layer should have something like this [0 0 0 0 0 1 0 0 0 0].

Top 100 Python Interview Questions You Must Prepare In 2019

Some of the commonly used built-in modules are: Global Variables: Variables declared outside a function or in global space are called global variables.

But, arrays can hold only a single data type elements whereas lists can hold any data type elements.

Example: Output: array(‘i’, [1, 2, 3, 4]) [1, ‘abc’, 1.2] Ans: A function is a block of code which is executed only when it is called.

The self variable in the init method refers to the newly created object while in other methods, it refers to the object whose method was called.

Output: array(‘i’, [5, 4, 3, 2, 1]) Ans:Consider the example shown below: The output of the following code is as below.

The method is defined as: The statement random.random() method return the floating point number that is in the range of [0, 1).

This technique is used with a type of object known as generators.That means that if you have a really gigantic range you’d like to generate a list for, say one billion, xrange is the function to use.

This is especially true if you have a really memory sensitive system such as a cell phone that you are working with, as range will use as much memory as it can to create your array of integers, which can result in a Memory Error and crash your program.

Comments in Python start with a # Ans:Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling.

is ‘a’) not: returns the inverse of the boolean value in: checks if some element is present in some sequence Ans:Help() and dir() both functions are accessible from the Python interpreter and used for viewing a consolidated dump of built-in functions.

Syntax: The Ternary operator will be given as: [on_true] if [expression] else [on_false]x, y = 25, 50big = x if x <

y else y Example: The expression gets evaluated like if x<y else y, in this case if x<y is true then the value is returned as big=x and if it is incorrect then big=y will be sent as a result.

Ans:We use *args when we aren’t sure how many arguments are going to be passed to a function, or if we want to pass a stored list or tuple of arguments to a function.

**kwargs is used when we don’t know how many keyword arguments will be passed to a function, or it can be used to pass the values of a dictionary as keyword arguments.

The negative index is used to remove any new-line spaces from the string and allow the string to except the last character that is given as S[:-1].

Example: Output: array(‘d’, [1.1, 2.1, 3.1, 3.4]) array(‘d’, [1.1, 2.1, 3.1, 3.4, 4.5, 6.3, 6.8]) array(‘d’, [1.1, 2.1, 3.8, 3.1, 3.4, 4.5, 6.3, 6.8]) Ans: Array elements can be removed usingpop()orremove()method.

Ans:Shallow copy is used when a new instance type gets created and it keeps the values that are copied in the new instance.

These references point to the original objects and the changes made in any member of the class will also affect the original copy of it.Shallow copy allows faster execution of the program and it depends on the size of the data that is used.

The changes made in the original copy won’t affect any other copy that uses the object.Deep copy makes execution of the program slower due to making certain copies for each object that is been called.

Ans:The compiling and linking allows the new extensions to be compiled properly without any error and the linking can be done only when it passes the compiled procedure.

Consider the below example: We can then run the monkey-patch testing like this: The output will be as below: As we can see, we did make some changes in the behavior off()inMyClassusing the function we defined,monkey_f(), outside of the modulem.

Output: Enter the terms 5 0 1 1 2 3 Output: enter number 3 Prime Output: enter sequence 323 palindrome Ans:Let us first write a multiple line solution and then convert it to one-liner code.

Django consists of prewritten code, which the user will need to analyze whereas Flask gives the users to create their own code, therefore, making it simpler to understand the code.

We will add the following lines of code to the file: Ans:This is how we can use write a view in Django: Returns the current date and time, as an HTML document Ans:The template is a simple text file.

A template contains variables that get replaced with values when the template is evaluated and tags (% tag %) that control the logic of the template.

Django abstracts the process of sending and receiving cookies, by placing a session ID cookie on the client side, and storing all the related data on the server side.

Ans:In Django, there are three possible inheritance styles: Ans:We will use the following code to save an image locally from an URL address Ans:Use the following URL format: Be sure to replace “URLGOESHERE”

The above code will help scrap data from IMDb’s top 250 list Ans:map function executes the function given as the first argument on all the elements of the iterable given as the second argument.

Ans:We use python numpy array instead of a list because of the below three reasons: For more information on these parameters, you can refer to this section –

Ans:Like 2D plotting, 3D graphics is beyond the scope of NumPy and SciPy, but just as in the 2D case, packages exist that integrate with NumPy.Matplotlibprovides basic 3D plotting in themplot3dsubpackage, whereasMayaviprovides a wide range of high-quality 3D visualization features, utilizing the powerfulVTKengine.

Answer: b) // When both of the operands are integer then python chops out the fraction part and gives you the round off value, to get the accurate answer use floor division.

Answer: a) they are used to indicate a private variable of a class As Python has no concept of private variables, leading underscores are used to indicate variables that must not be accessed from outside the class.

If you wish to learn Python and gain expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers by transforming your career into Data Scientist role, check out our interactive, live-online Python Certification Training.

You will use libraries like Pandas, Numpy, Matplotlib, Scipy, Scikit, Pyspark and master the concepts like Python machine learning, scripts, sequence, web scraping and big data analytics leveraging Apache Spark.The trainingcomes with 24*7 support to guide you throughout your learning period.

Machine Learning, Data Science and Deep Learning with Python

New!Updated for Summer 2019 for the latest software versions, and over 5 hours of new &updated content!

If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitionersin the tech industry - and prepare you for a move into this hot career path.

It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference.You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them.

If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists.

Neural Networks: An Essential Beginners Guide to Artificial Neural Networks and Their Role in Machine Learning and Artificial Intelligence

There is a lot of coding and math behind neural networks, but the listener is presumed to have no prior knowledge or interest in either, so the concepts are broken down and elaborated on as such.  Each chapter is made as standalone as possible to allow the listener to skip back and forth without getting lost, with the glossary at the very end serving as a handy summary.  So if you want to learn about Neural Networks without having to go through heavy textbooks, listen to this audiobook now!

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