Building Transformer Models with Attention Implementing a Neural Machine Translator from Scratch in Keras …another NLP book?This one is different! Handling text and human language is a tedious job. Not only is a lot of data cleansing needed, but multiple levels of preprocessing are also required depending on the algorithm you apply. But unarguably, the […]
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Inferencing the Transformer Model
We have seen how to train the Transformer model on a dataset of English and German sentence pairs and how to plot the training and validation loss curves to diagnose the model’s learning performance and decide at which epoch to run inference on the trained model. We are now ready to run inference on the […]
Plotting the Training and Validation Loss Curves for the Transformer Model
We have previously seen how to train the Transformer model for neural machine translation. Before moving on to inferencing the trained model, let us first explore how to modify the training code slightly to be able to plot the training and validation loss curves that can be generated during the learning process. The training and […]
Training the Transformer Model
We have put together the complete Transformer model, and now we are ready to train it for neural machine translation. We shall use a training dataset for this purpose, which contains short English and German sentence pairs. We will also revisit the role of masking in computing the accuracy and loss metrics during the training […]
The Vision Transformer Model
With the Transformer architecture revolutionizing the implementation of attention, and achieving very promising results in the natural language processing domain, it was only a matter of time before we could see its application in the computer vision domain too. This was eventually achieved with the implementation of the Vision Transformer (ViT). In this tutorial, you […]
A Gentle Introduction to Positional Encoding in Transformer Models, Part 1
In languages, the order of the words and their position in a sentence really matters. The meaning of the entire sentence can change if the words are re-ordered. When implementing NLP solutions, recurrent neural networks have an inbuilt mechanism that deals with the order of sequences. The transformer model, however, does not use recurrence or […]
Some Language Features in Python
The Python language syntax is quite powerful and expressive. Hence it is concise to express an algorithm in Python. Maybe this is the reason why it is popular in machine learning, as we need to experiment a lot in developing a machine learning model. If you’re new to Python but with experience in another programming […]
Difference Between Algorithm and Model in Machine Learning
Machine learning involves the use of machine learning algorithms and models. For beginners, this is very confusing as often “machine learning algorithm” is used interchangeably with “machine learning model.” Are they the same thing or something different? As a developer, your intuition with “algorithms” like sort algorithms and search algorithms will help to clear up […]
Predictive Model for the Phoneme Imbalanced Classification Dataset
Many binary classification tasks do not have an equal number of examples from each class, e.g. the class distribution is skewed or imbalanced. Nevertheless, accuracy is equally important in both classes. An example is the classification of vowel sounds from European languages as either nasal or oral on speech recognition where there are many more […]
How to Develop LSTM Models for Time Series Forecasting
Long Short-Term Memory networks, or LSTMs for short, can be applied to time series forecasting. There are many types of LSTM models that can be used for each specific type of time series forecasting problem. In this tutorial, you will discover how to develop a suite of LSTM models for a range of standard time […]