In this project we will use ChatGPT to utilize its chat format to have extended conversations with chatbots personalized or specialized for specific tasks or behaviors.
We will use ChatGPT to generate customer service emails that are tailored to each customer’s review.
In this article we will explore how to use Large Language Models for text transformation tasks such as language translation, spelling and grammar checking, tone adjustment, and format conversion.
Here we look at how to use Large Language Models such as ChatGPT to infer sentiment and topics from product reviews and news articles
In this article we look at how to use Large Language Models such as ChatGPT to summarize text with a focus on specific topics
Here we look at how to develop prompts for large language models iteratively
In this article we look at two prompting principles and their related tactics in order to write effective prompts for large language models.
In this project we fine-tune a pre-trained model for sentiment analysis model using Hugging Face
In this article we will look in a bit more detail at what you might need to do to fine-tune a pre-trained model for text similarity using Hugging Face
In this article we will look in a bit more detail at what you might need to do to prepare your data for fine-tuning a pre-trained model for text similarity using Hugging Face
In this non-technical article we describe the basics of how transfomer models work which is the underlying technology behind Chat-GPT and most of the recent advances in AI
Here we are going to use the Reformer aka the efficient Transformer to create a more advanced conversational chatbot. It will learn how to understand context to better answer questions and it will also know how to ask questions if it needs more info, which could be useful for customer service applications.
In this post we will explore Reversible Residual Networks and see how they can be used to improve Transfomer models
Here we look at how to make transfomers more efficient using Reversible Layers and Locality Sensitive Hashing (LSH)
In this article, we will fine-tune a model using Hugging Face transformers to create a better chat bot for question answering
We will use Hugging Face transformers to download and use the DistilBERT model to create a chat bot for question answering
We implement the Text to Text Transfer from Transformers model (better known as T5) which can perform a wide variety of NLP tasks and is a versatile model.
Text summarization is an important task in natural language processing. In this article we will create a transfomer decoder model to perform text summarization.
In this article we’ll explore the transformer decoder which is the architecture behind GPT-2 and see how to implement it with trax.
In this article we explore the three ways of attention (encoder-decoder attention, causal attention, and bi-directional self attention) used in transformer NLP models and introducted in the 2017 paper Attention is all you need and see how to implement the latter two with dot product attention.
The 2017 paper Attention Is All You Need introduced the Transformer model and scaled dot-product attention, sometimes also called QKV (Queries, Keys, Values) attention. In this article we’ll implement a simplified version of scaled dot-product attention and replicate word alignment between English and French, as shown in the earlier paper Bhadanau, et al. (2014).
The attention mechanism is behind some of the recent advances in deep learning using the Transfomer model architecture. In this article we look at the first attention mechanism proposed in a paper by Bhadanau et al (2014) used to improve seq2seq models for language translation.
In this project we will create our own human workforce, a human task UI, and then define the human review workflow to perform data labeling for an ML task.
AWS Sagemaker offers many options for deploying models, in this project we will create an endpoint for a text classification model, splitting the traffic between them. Then after testing and reviewing the endpoint performance metrics, we will shift the traffic to one variant and configure it to autoscale.
When training ML models, hyperparameter tuning is a step taken to find the best performing training model. In this article we will apply a random algorithm of Automated Hyperparameter Tuning to train a BERT-based natural language processing (NLP) classifier. The model analyzes customer feedback and classifies the messages into positive, neutral, and negative sentiments.
In this project we train and deploy a BERT Based text classifier using AWS Sagemaker pipelines, and describe how this can help with MLOPS to provide the most efficient path to production for training deploying and maintaining machine learning models at scale in production.
We train a text classifier using a variant of the BERT deep learning model architecture called RoBERTa - a Robustly Optimized BERT Pretraining Approach, within a PyTorch model ran as a SageMaker Training Job.
We will prepare to train a BERT-based natural language processing (NLP) model converting review text into machine-readable features used by BERT. With the required feature transformation we will configure an Amazon SageMaker processing job to perform the task.
In this article we will use the AWS SageMaker BlazingText built-in deep learning model to predict the sentiment for customer text reviews. BlazingText is a variant of FastText which is based on word2vec.
We will use Amazon Sagemaker Autopilot to automatically train a natural language processing (NLP) model. The model will analyze customer feedback and classify the messages into positive (1), neutral (0) and negative (-1) sentiment.
In Data Science and machine learning, bias can be present in data before any model training occurs. In this article we will analyze bias on a dataset, generate and analyze bias reports, and prepare the dataset for the model training.
In this project we will explore text reviews for clothing products using tools from the cloud data science service AWS Sagemaker to load and visualise the data and to gain key insights from it.
In this project we will be using a deep learning model to classify satellite images of the amazon rain forest. Here the main objective is not to get the best results for this task, rather to use this dataset to illustrate the use of the Fastai deep learning library
Deep Learning and AI is powering some of the most recent amazing advances in text & natural language processing (NLP) applications, such as GPT-3, Chat-GPT and Dall-E but these often require specialist resources such as deep learning. With Machine Learning (ML) its possible to create useful NLP applications for businesses without using AI and Deep Learning.
What’s the difference between machine learning and deep learning? In this article we will explain the differences between machine learning & deep learning, and will illustrate this by building a machine learning and a deep learning model from scratch.
In this project I will create a model that can associate short text phrases with the correct US patent classification.
This article covers lesson 1 the fastai 2022 course where I will create a model that can identify different types of galaxies. I will also highlight some notable differences from earlier versions of the fastai course and library.
In this project we will build a model to predict the 10-year risk of death of individuals from the NHANES I epidemiology dataset
In this project we will build a Prognostic risk score model for retinopathy in diabetes patients using logistic regression
In this project we will be working with the results of the X-ray classification model for diseases we developed in the previous article, and evaluate the model performance on each of these classes using various classification metrics.
In this project, I will explore medical image diagnosis by building a state-of-the-art deep learning chest X-ray classifier using Keras that can classify 14 different medical conditions.
In this article we will look at the history of the International Classification of Diseases (ICD) system, which has been developed collaboratively so that the medical terms and information in death certificates can be grouped together for statistical purposes. In practical examples we will look at how to extract ICD-9 codes from MIMIC III database and visualise them.
In this article we will further explore the MIMIC-III critical care Electronic Health Record Dataset, looking at how we examine clinical outcomes as well as extracting indivdual patient data.
In this article we will look at the MIMIC-III Electronic Health Record (EHR) database. In particular, we will learn about the design of this relational database, and what tools are available to query, extract and visualise descriptive analytics.
In this article we will look at MIMIC-III, which is the largest publicly Electronic Health Record (EHR) database available to benchmark machine learning algorithms.
Epidemiological studies can provide valuable insights about a disease, however a study can yield biased results for many different reasons. In this article we explore some of these factors, and provides guidance on how to deal with bias in epidemiological research.
In this article, we will learn about the main epidemiological study designs, including cross-sectional and ecological studies, case-control and cohort studies, as well as the more complex nested case-control, case-cohort designs, and randomised controlled trials.
In this article we look at the fundamental tools of Epidemiology (the study of disease) essential to conduct studies such as measures to describe the frequency of disease, how to quantify the strength of an association, how to describe different strategies for prevention, how to identify strengths and weaknesses of diagnostic tests, and when a screening programme may be appropriate.
In this project I develop a deep learning CNN model to predict Alzheimer’s disease using 3D MRI medical images of the Hippocampus region of the brain.
Utilizing a synthetic Diabetes patient dataset, we will create a deep learning model trained on EHR data (Electronic Health Records) to find suitable patients for testing a new Diabetes drug.
In this project, I will analyze data from the NIH Chest X-ray 2D Medical image dataset and train a deep learning model to classify a given chest x-ray for the presence or absence of pneumonia.
In Python Power Tools for Data Science articles I look at python tools that help automate or simplify common tasks a Data Scientist would need to perform. In this article I look at the Pycaret Anomaly Detection module and see how this can help automate this process.
Singular Value Decomposition (SVD) is a method from Linear Algebra widley used accross science and engineering. In this article we will introduce the concept and show how it can be used for Topic Modelling in Natural Language Processing (NLP).
In Python Power Tools for Data Science articles I look at python tools that help automate or simplify common tasks a Data Scientist would need to perform. In this article I look at how Pycaret can help automate the machine learning workflow.
In this article we will introduce Network Analysis, and use it to study the structure and relationships within a Karate Club.
In this article we will look at how Class Acivation Maps (CAM’s) can be used to understand and interpret the decisions that Convolutional Neural Networks (CNN’s) make.
In this article we will cover building a basic neural network from the most basic elements.
In this article we will look at methods to improve gradient decent optimisation for training neural networks beyond SGD including momentum, RMSProp and Adam.
In this article we will build a ResNet type convolutional image networks from scratch using PyTorch, and see why they are key to building deeper neural networks.
In this article we will look at how to build custom applications in the fastai library, by looking at how current fastai image model applications are actually built.
In this article we are going to look at building a convolutional neural network from scratch, using Pytorch as well as one-cycle training and batch normalisation.
In this article we will look at how we build an LSTM language model from scratch that is able to predict the next word in a sequence of words. This covers all the details of how to build the AWD-LSTM architecture.
In this article we will introduce and explore the fastai mid-level API, in particular it’s data preparation features.
In this article we are going to create a deep learning text classifier using the fastai library, and the ULMFit approach.
In this article we will look to build a collaberitive filtering model from scratch, using pure Pytorch and some support from the Fastai deep learning library.
In this article we are going to look at some of the most advanced techniques available in 2021 for training deep learning vision models.
In this project I look at applying AI to recognising buildings, woodlands & water areas from satellite images
Many of the greatest challenges the world faces today are global in nature, AI and satellite images is a powerful technology that holds huge potential for helping us solve many problems we face.
AI systems are being used everywhere, but often little work is done to gain a deeper understanding how and why they work. We have so much to gain from trying to look deeper inside these AI systems to understand them better.
