List of posts under category: Deep learning
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Jun 12, 2024
In this notebook we will create a simple interface using the Gradio framework for users to interact with our RAG enabled chatbot and maintain their database of reference documents.
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May 26, 2024
In this notebook we will explore running an LLM locally, as well as how our model can utilize RAG to alleviate problems of hallucination and knowledge gaps in the training data.
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Apr 24, 2024
Built a simple application through Python, utilizing deep learning techniques to automatically process e-commerce product photos into desired framings and resolutions saving the user from completing the very tedious and time consuming task.
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Apr 22, 2024
Global retail e-commerce sales reached an estimated 5.8 trillion USD in 2023. A major component of retail e-commerce are the products and the product images that showcases these products to the customers. With the number of platforms that sellers can promote their wide inventory of products, tailoring high quality product images for each platform may be a menial task consuming significant man-hours. With the help of YOLOv8 by ultralytics I aim to automate the creation of these product images.
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Mar 24, 2024
Created an interface using Python for users to draw on, integrating Stable Diffusion for image generation in near real-time. The solution involves sending JSON payloads as API calls to our generative model each time a user adds an element to their drawing. Relevant files can be found in my Github repository.
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Feb 20, 2024
In this notebook we will be exploring the IMDB dataset available on Kaggle, containing 50,000 reviews categorised as either positive or negative reviews. A text classification model will then be fine-tuned over DistilBERT and evaluated.
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Jan 1, 2024
In this notebook we look at changes in surface temperature of areas around the world from 1961-2020 using a dataset from the Food and Agriculture Organization of the United Nations. The changes in temperature are with reference to a baseline climatology found within the reference period of 1951-1980. Thereafter, we will attempt to forecast temperature changes using two popular techniques.
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Jan 1, 2024
In this notebook we will be exploring rainfall patterns in Singapore, showing the seasonal patterns of rainfall and how some areas of the island receive more rainfall than others. Models will also be built and tested to forecast monthly rainfall on the island.
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Jan 1, 2024
In this notebook we will be using an autoencoder on the fraud dataset used in a previous notebook for novelty detection. Novelty detection refers to the identification of new or unknown signals not available to a machine learning system during training. In this case it refers to training a machine learning model only on normal(non-fradulent) transactions data but the resultant model has the ability to recognise fraudulent transactions.
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Jan 1, 2024
In this notebook we train classification models to identify the activities and subjects from a smartphone sensor dataset.
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Jan 1, 2024
In this notebook we build and deploy a flask web application to detect and recognise handwritten words from an image.
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