Deep Learning, NLP, NMT, AI, ML
Python tutorials
Introduction Dictionaries are one of the most used data structures in all of software development, and for a good reason. They allow us to store our data in neat key, value pairs, which in turn gives us the ability to, on average, access our data in O(1) time. While using a dictionary it’s important to know how to iterate over it. Not being able to recover the data you stored makes it practically useless. In this article, we’ll see how […]
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Introduction In this article we’re going to be taking a look at the try/except clause, and specifically how you can catch multiple exceptions in a single line, as well as how to use the suppress() method. Both of these techniques will help you in writing more accessible and versatile code that adheres to DRY (don’t repeat yourself) principles. Let’s start by looking at the problem: try: do_the_thing() except TypeError as e: do_the_other_thing() except KeyError as e: do_the_other_thing() except IndexError as […]
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This article was published as a part of the Data Science Blogathon. Introduction Developing Web Apps for data models has always been a hectic task for non-web developers. For developing Web API we need to make the front end as well as back end platform. That is not an easy task. But then python comes to the rescue with its very fascinating frameworks like Streamlit, Flassger, FastAPI. These frameworks help us to build web APIs very elegantly, without worrying about […]
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Overview Learning about the state of the art model that is Transformers. Understand how we can implement Transformers on the already seen image captioning problem using Tensorflow Comparing the results of Transformers vs attention models. Introduction We have seen that Attention mechanisms (in the previous article) have become an integral part of compelling sequence modeling and transduction models in various tasks (such as image captioning), allowing modeling of dependencies without regard to their distance in the input or output […]
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Introduction Selecting a random element or value from a list is a common task – be it for randomized result from a list of recommendations or just a random prompt. In this article, we’ll take a look at how to randomly select elements from a list in Python. We’ll cover the retrieval of both singular random elements, as well as retrieving multiple elements – with and without repetition. Selecting a Random Element From Python List The most intuitive and natural […]
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Introduction People can rarely look at a raw data and immediately deduce a data-oriented observation like: People in stores tend to buy diapers and beer in conjunction! Or even if you as a data scientist can indeed sight read raw data, your investor or boss most likely can’t. In order for us to properly analyze our data, we need to represent it in a tangible, comprehensive way. Which is exactly why we use data visualization! The pandas library offers a […]
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Introduction The world of Natural language processing is recently overtaken by the invention of Transformers. Transformers are entirely indifferent to the conventional sequence-based networks. RNNs are the initial weapon used for sequence-based tasks like text generation, text classification, etc. But with the arrival of LSTM and GRU cells, the issue with capturing long-term dependency in the text got resolved. But learning the model with LSTM cells is a hard task as we cannot make it learn parallelly.
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Introduction Updating Python packages can be a hassle. There are many of them – it’s hard to keep track of all the newest versions, and even when you decide what to update, you still have to update each of them manually. To address this issue, pip-review was created. It lets you smoothly manage all available PyPi updates with simple commands. Originally a part of the pip-tools package, it now lives on as a standalone convenience wrapper around pip. In this […]
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This article was published as a part of the Data Science Blogathon. Introduction In this tutorial, you will learn how you can integrate common Natural Language Processing (NLP) functionalities into your application with minimal effort. We will be doing this using the ‘transformers‘ library provided by Hugging Face. 1. First, Install the transformers library. # Install the library !pip install transformers 2. Next, import the necessary functions. # Necessary imports from transformers import pipeline 3. Irrespective of the task that […]
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Introduction Python is a very high-level programming language, and it tends to stray away from anything remotely resembling internal data structure. Because of this, we usually don’t really need indices of a list to access its elements, however, sometimes we desperately need them. In this article, we will go over different approaches on how to access an index in Python’s for loop. How to Access Index in Python’s for Loop? The easiest, and most popular method to access the index […]
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