Python Programming Leftovers
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Python Modules and Packages: An Introduction
In this course, you’ll learn about Python modules and Python packages, two mechanisms that facilitate modular programming.
Modular programming is the process of breaking a large, unwieldy programming task into separate, smaller, more manageable subtasks or modules. Individual modules can then be put together like building blocks to create a larger application.
Learn how to write and import modules so you can optimize the structure of your own programs and make them easier to maintain and grow.
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PyCoder’s Weekly: Issue #405 (Jan. 28, 2020)
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Why Should You Use typing In Python?
I am using typing at my work project and now trying to use it for my personal or freelance projects only if it is not a one-time script. No sense to invest time in the 15-minute thing. And why you should use it too.
Lots of people like Python because it provides a fast way to build software. But on the other hand, after some time of the team development, it can be hard to understand the code for the team members. Especially, for the new ones.
I do personally believe that it increases the readability of the code. Seems to me that it is even more about self-documenting the code, not about the type checking. However, yes, it helps to find some obvious bugs.
My favorite example is a variable called data. Is it a list? Or maybe a dict? Or maybe something custom? Looks like that you need to invest some time in figuring this out. And probably repeat it one more time in a month or two. -
Text Classification with BERT Tokenizer and TF 2.0 in Python
This is the 23rd article in my series of articles on Python for NLP. In the previous article of this series, I explained how to perform neural machine translation using seq2seq architecture with Python's Keras library for deep learning.
In this article we will study BERT, which stands for Bidirectional Encoder Representations from Transformers and its application to text classification. BERT is a text representation technique like Word Embeddings. If you have no idea of how word embeddings work, take a look at my article on word embeddings.
Like word embeddings, BERT is also a text representation technique which is a fusion of variety of state-of-the-art deep learning algorithms, such as bidirectional encoder LSTM and Transformers. BERT was developed by researchers at Google in 2018 and has been proven to be state-of-the-art for a variety of natural language processing tasks such text classification, text summarization, text generation, etc. Just recently, Google announced that BERT is being used as a core part of their search algorithm to better understand queries.
In this article we will not go into the mathematical details of how BERT is implemented, as there are plenty of resources already available online. Rather we will see how to perform text classification using the BERT Tokenizer. In this article you will see how the BERT Tokenizer can be used to create text classification model. In the next article I will explain how the BERT Tokenizer, along with BERT embedding layer, can be used to create even more efficient NLP models.
Note: All the scripts in this article have been tested using Google Colab environment, with Python runtime set to GPU.
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PyCharm 2020.1 EAP starts now
There are two types of people in the world: those who can wait to open a package they’ve received, and people like me, who need to see what’s inside this very second.
PyCharm isn’t delivered in the mail though, and that’s why we have something even better for impatient people. The early access program (EAP) shows you what’s in the package a couple months before you get it. Take a sneak peek, and get PyCharm’s first EAP now!
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Webinar Recording: “Advanced Debugging in PyCharm”
Last week we held a special webinar for “Advanced Debugging in PyCharm”. Special how? In person, in the St. Petersburg office, with the two PyCharm team members in charge of the debugger, and a huge webinar audience. The recording is now available.
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