An introduction to the task of statistical and neural summarization. Also, a practical experiment to try, evaluate, and select a SOTA model for personal use. Summarization is a human right! Read on to learn more :)
At some point, it becomes tedious to work with Kaggle or Colab, and you need to have more flexibility to produce better code. In this article, I detail key challenges of remote training, delve into crucial Transformer concepts, and share some practical tips for accomplishing this task.
A learning process is not complete unless we practice solving real problems from start to finish. Kaggle Competitions are an excellent resource for this. Read on to find out how to approach and get the most out of them!
Transformer models have replaced RNNs allowing SOTA results for many NLP tasks. This Encoder-Decoder architecture plus the use of the Attention mechanism and Transfer Learning are the base of modern NLP. Read further to understand how the training dataset travels through the network, how it changes for the Attention calculations, and how transfer learning dramatically improves the model performance. Code included below!
Encoder-Decoder architectures enable modern machine translation. They can handle any sequence of words as input. The Attention mechanism helps to focus on the most relevant elements of the source sentence when predicting the following word in the target language. Read on to understand the details of Neural Machine Translation and its implementation with PyTorch. As always, code is included!
Human communication can transmit complex ideas through the composition of words into grammatical structures. For machines to understand what we say, they need to know the building blocks of these structures and how they depend on each other. Read on to learn more about these dependencies and how to code a neural network capable of learning these complex relationships!
A look at the basics of the most popular framework for creating word embeddings. In this article, I review the mathematical foundations of word2vec and its implementation in Python using NumPy. Code included!
For machines to understand our language, we need to represent words numerically. We can do so with supervised or unsupervised ML techniques. After training our model, we can use the concept of similarity for various tasks, and in doing so, oh surprise, we run into bias! Read on to know more! (Jupyter Notebook included)
With pyenv, it is possible to install isolated python versions on our computers. By adding the power of poetry, we can effortlessly manage the project's dependencies. Both tools allow us to minimize the time needed to be productive and start coding ASAP. Read on to find out how to get started!