Making Linear Predictions in PyTorch
Linear regression is a statistical technique for estimating the relationship between two variables. A simple example of linear regression is to predict the height of someone based on the square root of the person’s weight (that’s what BMI is based on). To do this, we need to find the slope and intercept of the line. The slope is how much one variable changes with the change in other variable by one unit. The intercept is where our line crosses with the $y$-axis.
Let’s use the simple linear equat
Training a Linear Regression Model in PyTorch
Linear regression is a simple yet powerful technique for predicting the values of variables based on other variables. It is often used for modeling relationships between two or more continuous variables, such as the relationship between income and age, or the relationship between weight and height. Likewise, linear regression can be used to predict continuous outcomes such as price or quantity demand, based on other variables that are known to influence these outcomes.
In order to train a linear
Implementing Gradient Descent in PyTorch
The gradient descent algorithm is one of the most popular techniques for training deep neural networks. It has many applications in fields such as computer vision, speech recognition, and natural language processing. While the idea of gradient descent has been around for decades, it’s only recently that it’s been applied to applications related to deep learning.
Gradient descent is an iterative optimization method used to find the minimum of an objective function by updating values iteratively o
Using Dataset Classes in PyTorch
In machine learning and deep learning problems, a lot of effort goes into preparing the data. Data is usually messy and needs to be preprocessed before it can be used for training a model. If the data is not prepared correctly, the model won’t be able to generalize well.
Some of the common steps required for data preprocessing include:
Data normalization: This includes normalizing the data between a range of values in a dataset.
Data augmentation: This includes generating new samples from existi
Loading and Providing Datasets in PyTorch
Structuring the data pipeline in a way that it can be effortlessly linked to your deep learning model is an important aspect of any deep learning-based system. PyTorch packs everything to do just that.
While in the previous tutorial, we used simple datasets, we’ll need to work with larger datasets in real world scenarios in order to fully exploit the potential of deep learning and neural networks.
In this tutorial, you’ll learn how to build custom datasets in PyTorch. While the focus here remain
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Attend the Data Science Symposium 2022, November 8 in Cincinnati
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A Brief Introduction to BERT
As we learned what a Transformer is and how we might train the Transformer model, we notice that it is a great tool to make a computer understand human language. However, the Transformer was originally designed as a model to translate one language to another. If we repurpose it for a different task, we would likely need to retrain the whole model from scratch. Given the time it takes to train a Transformer model is enormous, we would like to have a solution that enables us to readily reuse the t
Implementing the Transformer Decoder From Scratch in TensorFlow and Keras
There are many similarities between the Transformer encoder and decoder, such as in their implementation of multi-head attention, layer normalization and a fully connected feed-forward network as their final sub-layer. Having implemented the Transformer encoder, we will now proceed to apply our knowledge in implementing the Transformer decoder, as a further step towards implementing the complete Transformer model. Our end goal remains the application of the complete model to Natural Language Pro
Joining the Transformer Encoder and Decoder, and Masking
We have arrived to a point where we have implemented and tested the Transformer encoder and decoder separately, and we may now join the two together into a complete model. We will also be seeing how to create padding and look-ahead masks by which we will be suppressing the input values that we will not be considering in either of the encoder or decoder computations. Our end goal remains the application of the complete model to Natural Language Processing (NLP).
In this tutorial, you will discove
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 be making use of a training dataset for this purpose, which contains short English and German sentence pairs. We will also be revisiting the role of masking in computing the accuracy and loss metrics during the training process.
In this tutorial, you will discover how to train the Transformer model for neural machine translation.
After completing this tutorial, you wil
Attend the Data Science Symposium 2022
Sponsored Post
Attend the Data Science Symposium 2022 on November 8
The Center for Business Analytics at the University of Cincinnati will present its annual Data Science Symposium 2022 on November 8. This all day in-person event will have three featured speakers and two tech talk tracks with four concurrent presentations in each track. The event, held at the Lindner College of Business, is open to all
Featured speakers include “the father of the data warehouse”, Bill Inmon, presenting on h
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, in order to be able to plot the training and validation loss curves that can be generated during the learning process.
The training and validation loss values provide important pieces of information, because they allow us to have a better insight on how the learning performance is changing ove
Inferencing the Transformer Model
We have seen how to train the Transformer model on a dataset of English and German sentence pairs, as well as how to plot the training and validation loss curves in order to diagnose the model’s learning performance and decide at which epoch to inference the trained model. We are now ready to inference the trained Transformer model for the purpose of translating an input sentence.
In this tutorial, you will discover how to inference the trained Transformer model for neural machine translation.
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How to Implement Scaled Dot-Product Attention From Scratch in TensorFlow and Keras
Having familiarised ourselves with the theory behind the Transformer model and its attention mechanism, we’ll be starting our journey of implementing a complete Transformer model by first seeing how to implement the scaled-dot product attention. The scaled dot-product attention is an integral part of the multi-head attention, which in turn, is an important component of both the Transformer encoder and decoder. Our end goal will be the application of the complete Transformer model to Natural Lang
How to Implement Multi-Head Attention From Scratch in TensorFlow and Keras
We have already familiarised ourselves with the theory behind the Transformer model and its attention mechanism, and we have already started our journey of implementing a complete model by seeing how to implement the scaled-dot product attention. We shall now progress one step further into our journey by encapsulating the scaled-dot product attention into a multi-head attention mechanism, of which it is a core component. Our end goal remains the application of the complete model to Natural Langu
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 will discover the architecture of the Vision Transformer model, and its application to the task of image classification.
After c
Implementing the Transformer Encoder From Scratch in TensorFlow and Keras
Having seen how to implement the scaled dot-product attention, and integrate it within the multi-head attention of the Transformer model, we may progress one step further towards implementing a complete Transformer model by implementing its encoder. Our end goal remains the application of the complete model to Natural Language Processing (NLP).
In this tutorial, you will discover how to implement the Transformer encoder from scratch in TensorFlow and Keras.
After completing this tutorial, you w