Thomas Wood

verfied profile

I am a Consultant Data Scientist with my own consultancy, Fast Data Science.

I help organisations extract value from unstructured data with AI and machine learning. I specialise in text (natural language processing), images, and healthcare/pharmaceuticals.

If an organisation has large volumes of text data, incoming emails to triage, insurance reports, legal or scientific documents, I am the right person to leverage machine learning to extract useful information from the materials.

Career

I originally studied Physics to Masters level at the University of Durham, UK. I moved into the machine learning field in 2007, completing a second Masters in Computer Speech, Text and Internet Technology at the University of Cambridge in 2008.

Since then I have stayed in the exciting field of machine learning for more than 10 years. I have worked in a variety of companies in industries including consulting, pharmaceuticals, computer science, recruitment, retail and security, as well as some research experience.

Since 2018 I have been working as a freelance data scientist consultant, helping large organisations around the globe extract value from unstructured data such as text and images.

You can see information about me personally here.

Activation Function

An activation function sets the output behavior of each node, or “neuron” in an artificial neural network.

Rectified Linear Units Vector Perceptron
09/27/2020 ∙ 802 ∙ share

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Sigmoid Function

A sigmoid function is a type of activation function, and more specifically defined as a squashing function, which limits the output to a range between 0 and 1.

Odds (Probability) Estimator (Statistics) Neural Network
09/27/2020 ∙ 496 ∙ share

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Random Forests

The random forest is a supervised learning algorithm that randomly creates and merges multiple decision trees into one “forest.”

Classifier Machine Learning Estimator (Statistics)
09/10/2020 ∙ 846 ∙ share

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Backpropagation

Backpropagation, short for backward propagation of errors, is a widely used method for calculating derivatives inside deep feedforward neural networks.

Supervised Learning Classifier Neural Network
09/02/2020 ∙ 557 ∙ share

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Unsupervised Learning

Unsupervised learning is a deep learning technique that identifies hidden patterns, or clusters in raw, unlabeled data.

Generative Adversarial Network Supervised Learning Classifier
08/13/2020 ∙ 845 ∙ share

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Precision and Recall

Precision can be measured as of the total actual positive cases, how many positives were predicted correctly. It can be represented as: Precision = TP / (TP + FP) Whereas recall is described as the measured of how many of the positive predictions were correct It can be represented as: Recall = TP / (TP + FN)

Classifier Receiver Operating Characteristic Curve Harmonic Mean
08/11/2020 ∙ 379 ∙ share

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Generative Adversarial Network

A generative adversarial network (GAN) is an unsupervised machine learning architecture that trains two neural networks by forcing them to “outwit” each other.

Classifier Estimator (Statistics) Autoencoder
07/22/2020 ∙ 2129 ∙ share

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Transformer Neural Network

The transformer is a component used in many neural network designs that takes an input in the form of a sequence of vectors, and converts it into a vector called an encoding, and then decodes it back into another sequence.

Supervised Learning Open Source Tensorflow
07/07/2020 ∙ 401 ∙ share

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Convolutional Neural Network

A convolutional neural network, or CNN, is a deep learning neural network designed for processing structured arrays of data such as images.

ImageNet Classifier Estimator (Statistics)
05/17/2019 ∙ 1813 ∙ share

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F-Score

The F score, also called the F1 score or F measure, is a measure of a test’s accuracy.

Classifier Machine Learning Harmonic Mean
05/17/2019 ∙ 1056 ∙ share

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Softmax Function

The softmax function is a function that turns a vector of K real values into a vector of K real values that sum to 1. The input values can be positive, negative, zero, or greater than one, but the softmax transforms them into values between 0 and 1, so that they can be interpreted as probabilities.

Vector Classifier Machine Learning
05/17/2019 ∙ 297 ∙ share

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