Article
There are plenty of guides explaining how transformers work, and for building an intuition on a key element of them - token and position embedding. Positionally embedding tokens allowed transformers to represent non-rigid relationships between tokens (usually, words), which is much better at modeling our context-driven speech in language modeling....
David Landup
Transformers, even though released in 2017, have only started gaining significant traction in the last couple of years. With the proliferation of the technology through platforms like HuggingFace, NLP and Large Language Models (LLMs) have become more accessible than ever. Yet - even with all the hype around them and...
TensorFlow Datasets, also known as tfds is is a library that serves as a wrapper to a wide selection of datasets, with proprietary functions to load, split and prepare datasets for Machine and Deep Learning, primarily with TensorFlow. Note: While the TensorFlow Datasets library is used to get data, it's...
Deep Learning models are very versatile and powerful - they're routinely outperforming humans in narrow tasks, and their generalization power is increasing at a rapid rate. New models are being released and benchmarked against community-accepted datasets frequently, and keeping up with all of them is getting harder. Most of these...
Keras is a high-level API, typically used with the TensorFlow library, and has lowered the barrier to entry for many and democratized the creation of Deep Learning models and systems. When just starting out, a high-level API that abstracts most of the inner-workings helps people get the hang of the...
Let me preface the potentially provocative title with: It's true, nobody wants overfitting end models, just like nobody wants underfitting end models. Overfit models perform great on training data, but can't generalize well to new instances. What you end up with is a model that's approaching a fully hard-coded model...
If you are a Machine Learning Engineer, Data Scientist, or a hobbyist developing Machine Learning Models from time to time just for fun, then it is very likely that you are familiar with Tensorflow. Tensorflow is an open-source and free framework developed by Google Brain Team written in Python, C+...
Guest Contributor
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...
Usman Malik
After much hype, Google finally released TensorFlow 2.0 which is the latest version of Google's flagship deep learning platform. A lot of long-awaited features have been introduced in TensorFlow 2.0. This article very briefly covers how you can develop simple classification and regression models using TensorFlow 2.0....
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