Deep Learning, NLP, NMT, AI, ML
Introduction In this article, we’ll take a look at how to find the size of a dictionary in Python. Dictionary size can mean its length, or space it occupies in memory. To find the number of elements stored in a dictionary we can use the len() function. To find the size of a dictionary in bytes we can use the getsizeof() function of the sys module. To count the elements of a nested dictionary, we can use a recursive function. […]
Read more04 Feb21 Issue #116 – Fully Non-autoregressive Neural Machine Translation Author: Dr. Patrik Lambert, Senior Machine Translation Scientist @ Iconic Introduction The standard Transformer model is autoregressive (AT), which means that the prediction of each target word is based on the predictions for the previous words. The output is generated from left to right, a process which cannot be parallelised because the prediction probability of a token depends on previous tokens. In the last few years, new approaches have been […]
Read moreMicrosoft Vision Model ResNet-50 is a state-of-the-art pretrained ResNet-50 model, measured above by the mean average score across seven popular computer vision benchmarks. Pretrained vision models accelerate deep learning research and bring down the cost of performing computer vision tasks in production. By pretraining one large vision model to learn general visual representation of images, then transferring the learning across multiple downstream tasks, a team achieves competitive performance at a fraction of the cost when compared to collecting new
Read more
Last Updated on February 1, 2021 There is a lot of confusion for beginners around what algorithm is used to train deep learning neural network models. It is common to hear neural networks learn using the “back-propagation of error” algorithm or “stochastic gradient descent.” Sometimes, either of these algorithms is used as a shorthand for how a neural net is fit on a training dataset, although in many cases, there is a deep confusion as to what these algorithms are, […]
Read more
Weight initialization is an important design choice when developing deep learning neural network models. Historically, weight initialization involved using small random numbers, although over the last decade, more specific heuristics have been developed that use information, such as the type of activation function that is being used and the number of inputs to the node. These more tailored heuristics can result in more effective training of neural network models using the stochastic gradient descent optimization algorithm. In this tutorial, you […]
Read more
Introduction Pandas is a Python library for data analysis and manipulation. Almost all operations in pandas revolve around DataFrames, an abstract data structure tailor-made for handling a metric ton of data. In the aforementioned metric ton of data, some of it is bound to be missing for various reasons. Resulting in a missing (null/None/Nan) value in our DataFrame. Which is why, in this article, we’ll be discussing how to handle missing data in a Pandas DataFrame. Data Inspection Real-world datasets […]
Read more
Optimization refers to finding the set of inputs to an objective function that results in the maximum or minimum output from the objective function. It is common to describe optimization problems in terms of local vs. global optimization. Similarly, it is also common to describe optimization algorithms or search algorithms in terms of local vs. global search. In this tutorial, you will discover the practical differences between local and global optimization. After completing this tutorial, you will know: Local optimization […]
Read more
Introduction Having to manually format a number as a currency string can be a tedious process. You may have just a few lines of modifications to make, however, when we need to do a fair bit of conversions, it becomes very tedious. The first step to automating these kind of tasks will require a function. In this article, we’ll be going over a few methods you can use to format numbers as currency strings in Python. Methods for Formatting Numbers […]
Read more
Introduction Note from the author: In this article, we will learn how to create your own Question and Answering(QA) API using python, flask, and haystack framework with docker. The haystack framework will provide the complete QA features which are highly scalable and customizable. In this article Medium Rules, the text will be used as the target document and fine-tuning the model as well. Basic Knowledge Required: Elasticsearch & Docker This article contains the working code which can be directly build […]
Read more28 Jan21 Issue #115 – Revisiting Low-Resource Neural Machine Translation: A Case Study Author: Akshai Ramesh, Machine Translation Scientist @ Iconic Introduction Although deep neural models produce state-of-the-art results in many translation tasks, they are found to underperform phrase-based statistical machine translation in resource poor conditions. The majority of research on low-resource neural machine translation (NMT) focuses on the exploitation of monolingual or parallel data involving other language pairs. There is notably less attention into the research of low-resource NMT […]
Read more