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Physicist Analyst & ML Engineer — Writer because of his enthusiasm: Phython | R | Data Science | AI — ML — NLP

Learn to get appropriate answers to questions for complex texts thanks to the pre-trained model ( )

Very interesting projects for language studies have been developed in recent years. NLP technologies played an extremely impressive role in this. In fact, it had been a long time since Google had set out to understand searches better than ever.

However, after the advent of BERT, quite interesting developments took place. And we have quickly reached a place where the latest evolution of NLP technology is democratized. Today, more and more people want to develop their projects or brands by producing fine results.

Let’s look at the numbers to better understand the point we have reached.

BERT in Numbers

Pre-trained BERT offers NLP…

We continue to focus on Time Series Analysis. We will estimate the fertility rate for 2021 in Germany by all methods.

In the previous article, we tried to look at time series theoretically. You can continue here to get a general idea 👇

In this article, we will simply give some examples for a better understanding of time series. We will follow a sequence like the one below.

  • Simple Average Method
  • Moving Averages
  • Exponential smoothing
  • Trend Analysis
  • Seasonal Fluctuations and Proportioning Method in Trend

1) Simple Average Method

Creating machines as predictors with time series and a specific database

There is a fairly long list of things to do with machine learning. It is not possible to understand this all at once. However, it is possible to make some special applications more understandable thanks to the projects devoted to training.

It makes sense to create titles for this. So generalized concepts are always a better option to understand which tool is more useful in which project.

For example, to get colorful frames from black and white movies with machine learning:

You will need a model developed with deep learning. Again, for this, the model must be saturated with enough…

With Python — TensorFlow we can use facilitating paddings for sentence fragmentation and analysis in NLP stages. So how?

NLP tells us that we have to do a lot of similar things in order. These are very practical thanks to the TensorFlow and Keras libraries, which can be easily integrated into the system. So the first thing will be to import them. In fact, our job is to be able to use existing APIs. Because wheels are already invented!

NLP; is a set of operations designed to help the machine to encode a sentence structure meaningfully with the help of vectors. When we set out with these existing basics, we can see standards in small scale and similar jobs…

Understanding and applying compositions in a short time


Compositions; It is a programming technique used when establishing relationships between classes and objects. Understanding compositions is important in object-oriented programming. Compositions can be a good idea in Python and software language to make a particular structure dynamic. Because in this way, we fill the place of complex code blocks with more compact material.

Why Composition?

Through the compositions, we actually state the relationship between the two classes. And in this way, the code can be reused. This has a common similarity with the concept of inheritance. As a result, we obtain more complex structures by combining objects between different types.



We examine recursion functions and examples that can be used efficiently

Recursion functions are functions that reuse themselves. Its general goal is to be able to solve problems that are difficult and likely to take a long time more easily. Writing code as a recursive function is actually not too complicated. However, it is an important task of an IT professional to examine this tool closer to know where to use it.

Why Is It Important?

For those dealing with coding in general, the tasks to be automated should be performed in the best way possible. Simplicity and clarity can meet the best expectations at this stage. …

Isn’t it time to meet CatBoost?

1. Introduction


‘Category’ and ‘Boost’. Here is ‘CatBoost’. CatBoost is an algorithm for increasing gradients in decision trees. CatBoost, which came into existence thanks to Yandex engineers; recommendation systems are used in a wide range of areas such as personal assistants, driverless cars, weather prediction. And the good news: It is open source and available to everyone.

What does it do?

Catboost builds symmetrical trees. In this way, it catches a high estimate rate without setting up very deep trees. Here, it is necessary to understand the above part, i.e. “gradient enhancement in decision trees”.

Decision trees are simple diagrams in which we show the statistical…

What does it mean to read characters in Python?

In Python, even if we have important information such as arrays, lists, sets that we need to know in terms of data types, we are not considered sufficient in terms of coding. Because coding skills need more functional competencies to rise up the ramp in a better way. And sometimes they really are in the details.

One of the methods that exist in Python strings is encode (). In addition, it can be said that the files also have an ‘encoding’ parameter. …

Add additional functionality to one or more functions


As we dive deeper into Python, we see many interesting features. One of them is decorators. As it is known, the ability to use many features exactly when we need them takes us forward. So let’s take a closer look at decorators in this article.

1. What are Decorators?

Python has a concept called decorator that allows you to take advantage of the extra functionality quickly. Decorators are structures in Python that allow the use of other functions included in a function separately. This makes it easier to add additional functionality to one or more functions.

When we want to use a simple function…

It is important to understand polymorphism when we want to use the same methods in different classes

1. What is Polymorphism?

Polymorphism allows working with multiple data types in the same interface in computer science. Thanks to polymorphism, it is functional to work with different objects, to better explain their unique side in terms of features. This can be thanks to a function that applies to all subclasses mentioned. The picture below will be useful to understand a little more.

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