How do I write a detailed yet concise summary of an article?

We have to take a practical approach to the matter. A summary is a summary is a précis. It's an abridgment of the full article. It's an expository type of writing -- the purpose is to describe or inform rather than explain. Typical lengths are less than 500 to 1,000 words, depending on the subject matter and the subject field.

The usual (often taught) way is to pick the article's most important details and then describe them in short form to produce your summary.

But picking details isn't exactly an easy thing to do. The summariser might not be knowledgeable or familiar with the topic. That makes it hard for the summariser to decide which are the important points to select.

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So the emergency measure to summarising boils down to rewriting the article. Several rounds of rewriting are often necessary to pare things down. Over time, that experience will generally help the summariser get the hang of things.

So in short:

A summary abridges the full article. It describes and inform rather than to explain. A summary is around 500 to 1,000 words, depending on the topic. Picking the important details is the usual way, but that can be problematic for the person not familiar with the topic. Therefore, continual rewriting of the full article into shorter and shorter form is a practical way to pare it down.

How should one begin to write a novel if he/she has the basic plot but stuck on the way of presentation?

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A writer needs more than a basic plot in order to write a book. A basic plot will allow the author to develop a detailed plot that takes all the necessary elements into the story. This takes planning. The author needs to have a clear understanding of the major and minor plots of the story and how they link together. This means looking at the protagonist and discerning the characters traits, both positive and negative in order to understand how the character’s personality will interact with the plot and move it forward. Then the same needs to be done with the antagonist and other characters vital to the story. Each character has an effect on the story and plot. They control how the plot unfolds through the characters personalities and abilities or lack there of.

In my experience, writers, especially new ones who don’t take the time to develop their plot and storyline end up with plot holes, story inconsistencies, and other major problems. The plot needs to be strong enough and powerful enough to carry the story. The minor plots are how to characters move from their initial problem step by step through the story to being able to overcome the base problem that started the story.

Good stories aren’t born from a basic plot, they are crafted by the writer through detail, forward thinking, planning, and powerful characters. It is the characters, their dialogue, their feeling and emotions, their personalities and more that bring a plot to life. Readers want to be drawn into what the characters are doing and experiencing in order to bind the reader from the first page to the last.

Also, each genre has its own rules of what readers expect from a story. If the writer isn’t well read in their genre, they won’t understand what the readers will be expecting from their book. Therefore, it comes down to writing a book readers want to read or writing a book the writer wants to write, the two are not the same thing. Reading some books on writing and crafting stories may help the writer to plan and plot out a story that captivates.

What are the best ways to start writing better?

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Now start writing you heart out. Write on the topics that you like, whatever you think write it down. Then see if it can be any better or not. Then you can add some more content and edit your writings.

Make a habit of Reading! The more you'll read,more you'll be able to understand the theme of the poem or any article and message of the writer or poet.Read your favourite novels and other books which you like. It will improve your vocabulary and also helps to develop your interest in writing.

Most importantly, Be Consistent and Write! No matter what but try to write on daily basis. Write daily journals, write your thoughts, your experience. More you'll write, you'll become a better writer.Write your heart out, try to reflect your emotions in form of poem or any story. You'll become a very good writer.

How do you write a synthesis paper in academic writing?

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A synthesis is a written discussion that draws on one or more sources.

• It follows that your ability to write syntheses depends on your ability to infer relationships among sources - essays, articles, fiction, and also nonwritten sources, such as lectures, interviews, observations.

There are several techniques for developing synthesis essays, among them: Summary, Example or Illustration, Two or more reasons, and Comparison and Contrast.

Here’s one technique in synthesizing (infering relationships) -

• COMPARISON AND CONTRAST: Comparison and contrast techniques enable you to examine two subjects (or sources) in terms of one another. When you compare, you consider similarities. When you contrast, you consider differences.

To organize a comparison/contrast analysis, you must carefully read sources in order to discover significant criteria for analysis. A criterion is a specific point to which both of your authors refer and about which they may agree or disagree. There are two basic formulas for comparison/contrast analysis.

BY CRITERIA

I. Introduce essay, state thesis

II. Introduce Criterion 1

• Passage A's viewpoint
• Passage B's viewpoint

III. Introduce Criterion 2

• Passage A's viewpoint
• Passage B's viewpoint

IV. Discussion and conclusion

Or you can use this -

BY SOURCE

I. Introduce essay, state thesis

II. Summarize Passage A

• View on Criterion I
• View on Criterion 2

III. Summarize Passage B

• View on Criterion 1
• View on Criterion 2

IV. Discussion and conclusion

Formatting the Essay [Pro Assignment Help | Take Online Courses. Earn College Credit. Research Schools, Degrees & Careers]

A synthesis essay has an introduction, body, and conclusion.

The introduction provides an overview of the topic, thesis, and sources, with some background information for the texts to be summarized.

The body includes a topic sentence, information from more than one source, with in-text citations; it compares and contrasts sources in an objective (two-sided) interpretation, and informs the reader why the source argues a thesis.

The conclusion connects the ideas from the sources to your thesis, and describes how each supports your viewpoint. The conclusion also rewords your claim so it is clear you are offering a different perspective on the topic.

SUMMARY: Aside from comparison and contrast technique, other techniques in synthesizing are Summary, Example or Illustration, and Two or more reasons. To synthesize means to infer relationships between two or more sources. A synthesis is structured thus: Introduction, body, conclusion.

What do experienced writers understand that new writers don't?

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The first and most important thing you need to understand is writing takes practice.

Everyone starts out terrible at writing and each gets better only through practice. Your first story no matter how good it may seem to you is probably a steaming pile of dog turds. You don’t realize this now but years later, after you have written many, many things you will look back on it and go, ‘what a load of crap’. It may have great ideas and may be the seed for something amazing but technically and stylistically it will be crap. Practice makes perfect. Even Hemingway started out as someone nobody had ever heard of.

“When daylight comes the castle falls in ruins and O’Donahue returns to his grave,” Ernest Hemingway, Age 10 writing about a ghost that rebuilds an old castle every night only to be foiled each day.

Ok well maybe Hemingway was always Hemingway but even he got better through practice.

Very, very few people enjoy writing. Everyone finds it to be a chore.

There are basically two kinds of writers. One kind has to write for a job. This first kind could be for a magazine or online publication or novels but it is how they support themselves. They have to do it. The second kind of writer writes because they have no other choice. The words get clustered up in their heads and like autumn sap in a maple words begin to inexplicably flow out slowly but surely. Often writers are mixtures of type one and type two and they find ways to make money off the necessary chore of giving the words release.

The only way you will ever get a big writing project done is to sit yourself down and commit to writing on it regularly. Set a goal of X thousand words written per day. You will have to force yourself to go back to it again and again until it is done. You may need to seclude yourself from all other distractions to get it done. Then you have to force yourself to go back and rewrite it again and again until it is polished. Lastly you have to go back and make little tweaks and edits to punch up the quality. There are no shortcuts.

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Good writers read! They read a lot!

You will need to read as much as you write. No that is not true, you will need to read far more than you write! You will read for inspiration. You will read for research. You will read for the simple pleasure of enjoying someone else's work. What you read will influence your work, as with all writers. Pick great things to read and learn from them.

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Good writers borrow, but the truly great ones steal!

Don’t beat yourself down because your story reminds you of something else. There are no original stories. There is no story that doesn’t build on that which came before. Language is like an ocean and many ships sail the same ocean from many shores but they all do it in different ways.

Just look at the TV Tropes site. You can pick any trope and find dozens of examples of the same general plot playing out in various contexts. The key is not as much to make your story different but to make it something that is yours. You need to own your story and do it the way only you can do. It doesn’t have to be completely new to be a wonderful story told the way you feel the story should be.

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Keep trying even when it gets hard and find peers to bounce ideas off of.

Look to most any famous writer and you will find they were part of writing groups and societies that predate their fame. Often they stayed in touch with these people long after their fame too. They sat down with other would be writers and talked about plots and devices and writing. They collaborated, critiqued and got laughed at. You need to embrace this. These friends (and frenemies) will help you improve and will help shape your writing.

How do you write a character who is incredibly gifted at sword-play, but you don't want them to be a 'Mary Sue' either?

The story should be about something else than sword play.

I’ll take David Gemmel’s books as an example

We have 3 characters

• Skilgannon the damned, the most skilled swordmaster in his time
• Druss the legend, the most powerful warrior
• Tenaka Khan the shadow prince, who at 15 was already better than pretty much everyone else in every weapon

But the three of them have flaws, and huge flaws.

• Skilgannon loved a woman and helped her take back her throne, and she sent him to conquer cities. He went to a city called Perapolis and killed everyone there (including women and children). Since then, his nickname is “the damned” and he is hated by everyone for his atrocities. Also, his queen doesn’t want him in her bed anymore. He is in exile and considered a traitor.
• Druss is old. In Legend, he’s 60 years old. So he’s still the best fighter around, but his knees are weak, his shoulder hurts and he gets tired.
• Tenaka Khan is a mixed race son of two tribes that hates one another. He’s hated by both. He doesn’t have a place where he is welcome. He bought a woman just to have someone at home so he can pretend he is loved. And then she died. Also, he’s approaching 40 years old and all his friends are dead.

Those heroes have each a different arc, but you like them not because they are the best, but almost despite this.

How do you write a resume letter with no experience?

First, consider this letter you are writing will represent you. It’s the only thing about you your potential employer can see.

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Hold on to this thought. We will come back to it.

Set aside all the reasons why you want, need or deserve a job. The place where you are interviewing is wondering how you can help them, not how they can help you. Focus on that instead.

Think about the things you have done that might be considered a transferable skill. Some examples: writing, places where you have shown you are responsible, places where you have demonstrated you can handle multiple priorities, places where you have exhibited common sense.

Keep in mind that you don’t really know what can count as a transferable skill until you know what kind of job you are applying for. In other words: your resume should be tailored on the job you are interested in getting.

Consider anything you’ve done that gives you a boost - by this I mean “you will not have to spend too much time training me, because I’ve done a version of this before.” This is why internships are such a plus.

Is there anything your future employer can see that might give them the wrong impression? Ask yourself this as you reconsider things you’ve posted on social media. Employers can and often do look through everything.

Finally, remember when I said your resume will represent you? Be extra careful with how it’s written, with grammar, punctuation, format. It says a lot to have in your hands something clean, light, simple, easy to read.

Which tools and libraries are recommended for machine learning in Python?

Machine learning is the most algorithm-intense field in computer science. Gone are those days when people had to code all algorithms for machine learning. Thanks to Python and it’s libraries, modules, and frameworks.

Python machine learning libraries have grown to become the most preferred language for machine learning algorithm implementations. Let’s have a look at the main Python libraries used for machine learning.

Top Python Machine Learning Libraries

1) NumPy

NumPy is a well known general-purpose array-processing package. An extensive collection of high complexity mathematical functions make NumPy powerful to process large multi-dimensional arrays and matrices. NumPy is very useful for handling linear algebra, Fourier transforms, and random numbers. Other libraries like TensorFlow uses NumPy at the backend for manipulating tensors.

With NumPy, you can define arbitrary data types and easily integrate with most databases. NumPy can also serve as an efficient multi-dimensional container for any generic data that is in any datatype. The key features of NumPy include powerful N-dimensional array object, broadcasting functions, and out-of-box tools to integrate C/C++ and Fortran code.

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2) SciPy

With machine learning growing at supersonic speed, many Python developers were creating python libraries for machine learning, especially for scientific and analytical computing. Travis Oliphant, Eric Jones, and Pearu Peterson in 2001 decided to merge most of these bits and pieces codes and standardize it. The resulting library was then named as SciPy library.

The current development of the SciPy library is supported and sponsored by an open community of developers and distributed under the free BSD license.

The SciPy library offers modules for linear algebra, image optimization, integration interpolation, special functions, Fast Fourier transform, signal and image processing, Ordinary Differential Equation (ODE) solving, and other computational tasks in science and analytics.

The underlying data structure used by SciPy is a multi-dimensional array provided by the NumPy module. SciPy depends on NumPy for the array manipulation subroutines. The SciPy library was built to work with NumPy arrays along with providing user-friendly and efficient numerical functions.

3) Scikit-learn

In 2007, David Cournapeau developed the Scikit-learn library as part of the Google Summer of Code project. In 2010 INRIA involved and did the public release in January 2010. Skikit-learn was built on top of two Python libraries – NumPy and SciPy and has become the most popular Python machine learning library for developing machine learning algorithms.

Scikit-learn has a wide range of supervised and unsupervised learning algorithms that works on a consistent interface in Python. The library can also be used for data-mining and data analysis. The main machine learning functions that the Scikit-learn library can handle are classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.

4) Theano

Theano is a python gadget getting to know library which could act as an optimizing compiler for evaluating and manipulating mathematical expressions and matrix calculations. Built on NumPy, Theano well-knownshows a decent integration with NumPy and has a very similar interface. Theano can work on Graphics Processing Unit (GPU) and CPU.

Working on GPU architecture yields faster results. Theano can carry out data-intensive computations up to 140x quicker on GPU than on a CPU. Theano can automatically keep away from mistakes and insects when managing logarithmic and exponential features. Theano has integrated gear for unit-testing and validation, thereby fending off bugs and problems.

5) TensorFlow

TensorFlow changed into evolved for Google’s internal use through the Google Brain team. Its first release came in November 2015 under Apache License 2.0. TensorFlow is a popular computational framework for growing gadget getting to know fashions. TensorFlow supports a ramification of different toolkits for building models at various stages of abstraction.

TensorFlow exposes a completely solid Python and C++ APIs. It can expose, backward compatible APIs for different languages too, however they might be unstable. TensorFlow has a bendy architecture with which it can run on a ramification of computational systems CPUs, GPUs, and TPUs. TPU stands for Tensor processing unit, a hardware chip constructed around TensorFlow for device gaining knowledge of and artificial intelligence.

6) Keras

Keras has over 200,000 users as of November 2017. Keras is an open-source library used for neural networks and machine learning. Keras can run on top of TensorFlow, Theano, Microsoft Cognitive Toolkit, R, or PlaidML. Keras also can run efficiently on CPU and GPU.

Keras works with neural-network building blocks like layers, objectives, activation functions, and optimizers. Keras also have a bunch of features to work on images and text images that comes handy when writing Deep Neural Network code.

Apart from the standard neural network, Keras supports convolutional and recurrent neural networks.

7) PyTorch

PyTorch has a range of tools and libraries that support computer vision, machine learning, and natural language processing. The PyTorch library is open-source and is based on the Torch library. The most significant advantage of PyTorch library is it’s ease of learning and using.

PyTorch can smoothly integrate with the python data science stack, including NumPy. You will hardly make out a difference between NumPy and PyTorch. PyTorch also allows developers to perform computations on Tensors. PyTorch has a robust framework to build computational graphs on the go and even change them in runtime. Other advantages of PyTorch include multi GPU support, simplified preprocessors, and custom data loaders.

8) Pandas

Pandas are turning up to be the most popular Python library that is used for data analysis with support for fast, flexible, and expressive data structures designed to work on both “relational” or “labeled” data. Pandas today is an inevitable library for solving practical, real-world data analysis in Python. Pandas is highly stable, providing highly optimized performance. The backend code is purely written in C or Python.

The two main types of data structures used by pandas are :

Series (1-dimensional)

DataFrame (2-dimensional)

• These two put together can handle a vast majority of data requirements and use cases from most sectors like science, statistics, social, finance, and of course, analytics and other areas of engineering.
• Pandas support and perform well with different kinds of data including the below :
• Tabular data with columns of heterogeneous data. For instance, consider the data coming from the SQL table or Excel spreadsheet.
• Ordered and unordered time series data. The frequency of time series need not be fixed, unlike other libraries and tools. Pandas is exceptionally robust in handling uneven time-series data
• Arbitrary matrix data with the homogeneous or heterogeneous type of data in the rows and columns
• Any other form of statistical or observational data sets. The data need not be labeled at all. Pandas data structure can process it even without labeling.

9) Matplotlib

Matplotlib is a data visualization library that is used for 2D plotting to produce publication-quality image plots and figures in a variety of formats. The library helps to generate histograms, plots, error charts, scatter plots, bar charts with just a few lines of code.

It provides a MATLAB-like interface and is exceptionally user-friendly. It works by using standard GUI toolkits like GTK+, wxPython, Tkinter, or Qt to provide an object-oriented API that helps programmers to embed graphs and plots into their applications.