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BERTasticity – Part 1 Understanding Transformers – the CORE behind the Mammoth (Bert). In Language Modelling domain, BERT is something that has created quite a chaos since it is introduced. A lot of similar models have come from that time which always have a competition in claiming which one is better. Some of the alternatives include: • GPT, • GPT-2, • RoBERTa, • DistilBERT, • XLNet, etc. BERT, and other alternatives,...

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I had no idea how to build a Machine Learning Pipeline. But here’s what I figured. As a postgraduate studying Artificial Intelligence (AI), my exposure to Machine Learning (ML) is largely academic. Yet, when given a task to create a simple ML pipeline for a time series forecast model, I realised how clueless I was. Also, I could barely find any specific information or code out there on this topic, hence I decided to write this topic....

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6-essential practices to successfully implement machine learning solutions in your organization. Executive’s Guide to Successfully Becoming an AI-Driven Enterprise. McKinsey Insights recently published its Global AI Survey and discussed many aspects of the impact AI is generating across multiple companies. What really caught my eye was the comparison done between AI high performing companies versus the rest. According to the...

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Why NLP is important and it’ll be the future – our future NLP – also known as computational linguistics – is the combination of AI and linguistics that allows us to talk to machines as if they were human. Can Neural Networks Develop Attention? Google Thinks they Can Trying to read this article is a complicated task from the neuroscientific standpoint. At this time you are probably bombarded with emails, news, notifications on...

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GitHub Repo Raider and the Automation of Machine Learning Since X never, ever marks the spot, this article raids the GitHub repos in search of quality automated machine learning resources. Read on for projects and papers to help understand and implement AutoML. Complete Data Science Project Template with Mlflow for Non-Dummies. Best practices for everyone working either locally or in the cloud, from start-up ninja to big enterprise...

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Interpretability: Cracking open the black box – Part II In the last post in the series, we defined what interpretability is and looked at a few interpretable models and the quirks and ‘gotchas’ in it. Now let’s dig deeper into the post-hoc interpretation techniques which is useful when you model itself is not transparent. This resonates with most real world use cases, because whether we like it or not, we get better performance...

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Topics extraction and classification of online chats The combination of unsupervised and supervised machine learning approaches can be a great solution when we want to classify unlabelled data, i.e. data for which we don’t have the information we want to classify for. This blog post goes through a possible solution to • first, automatically identify the topics within a corpus of textual data by using unsupervised topic...

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Impact of using transfer learning in NLP We analyze the impact of classifying movie reviews sentiments based on a language model trained from scratch, or a pre-trained model using the corpus wikitext-103 Multicollinearity: Why is it a problem? Having come from an economic background multicollinearity is something I have grown familiar with during my academic career. However, once I entered industry I have found that the professionals...

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Machine Learning for Day Trading In this post, I’m going to explore machine learning algorithms for time-series analysis and explain why they don’t work for day trading. If you’re a novice in this field you might get fooled by authors with amazing results where test data match predictions almost perfectly. A common trick is to show a plot with predicted values on a long period of data, which creates an illusion that lag is...

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Multi-Label Text Classification with XLNet Achieve state-of-the-art multi-label and multi-class text classification with XLNet. At the time of its publication on 19 June 2019, XLNet achieved state-of-the-art results on 18 tasks including text classification, question-answering, natural language inference, sentiment analysis, and document ranking. It even outperformed BERT on 20 tasks! Developed by Carnegie Mellon University and Google...

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What does it mean for a machine to ‘understand’? Critics of recent advances in artificial intelligence complain that although these advances have produced remarkable improvements in AI systems, these systems still do not exhibit ‘real’, ‘true’, or ‘genuine’ understanding. The use of words like ‘real’, ‘true’, and ‘genuine’ imply that ‘understanding’ is binary. A system either exhibits ‘genuine’...