Machine Translation Weekly 66: Means against ends of sentences

This week I am going to revisit the mystery of decoding in neural machine translation for one more time. It has been more than a year ago when Felix Stahlberg and Bill Byrne discovered the very disturbing feature of neural machine translation models – that the most probable target sentence is an empty sequence and this it is a sort of luck that we decode good translations from the models (MT Weekly 20). The paper disproved the narrative of NMT […]

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How to Iterate Over a Dictionary in Python

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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Regression Metrics for Machine Learning

Regression refers to predictive modeling problems that involve predicting a numeric value. It is different from classification that involves predicting a class label. Unlike classification, you cannot use classification accuracy to evaluate the predictions made by a regression model. Instead, you must use error metrics specifically designed for evaluating predictions made on regression problems. In this tutorial, you will discover how to calculate error metrics for regression predictive modeling projects. After completing this tutorial, you will know: Regression predictive modeling […]

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How to Get Started With Recommender Systems

Recommender systems may be the most common type of predictive model that the average person may encounter. They provide the basis for recommendations on services such as Amazon, Spotify, and Youtube. Recommender systems are a huge daunting topic if you’re just getting started. There is a myriad of data preparation techniques, algorithms, and model evaluation methods. Not all of the techniques will be relevant, and in fact, the state-of-the-art can be ignored for now as you will likely get very […]

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Password Monitor: Safeguarding passwords in Microsoft Edge

One of the biggest pillars for Microsoft Edge is trust. Today, to further bolster that trust while keeping our customers safe, we introduce a new feature called Password Monitor. The feature notifies users if any of their saved passwords have been found in a third-party breach. All this is done while ensuring Microsoft doesn’t learn the user’s passwords. The underlying technology ensures privacy and security of the user’s passwords, which means that neither Microsoft nor any other party can learn […]

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Python: Catch Multiple Exceptions in One Line

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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Streamlit Web API for NLP: Tweet Sentiment Analysis

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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Implementation of Attention Mechanism for Caption Generation on Transformers using TensorFlow

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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Three mysteries in deep learning: Ensemble, knowledge distillation, and self-distillation

Under now-standard techniques, such as over-parameterization, batch-normalization, and adding residual links, “modern age” neural network training—at least for image classification tasks and many others—is usually quite stable. Using standard neural network architectures and training algorithms (typically SGD with momentum), the learned models perform consistently well, not only in terms of training accuracy but even in test accuracy, regardless of which random initialization or random data order is used during the training. For instance, if one trains the same WideResNet-28-10 architecture […]

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