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BabyGPT: Build Your Own GPT Large Language Model from Scratch Pre-Training Generative Transformer Models: Building GPT from Scratch with a Step-by-Step Guide to Generative AI in PyTorch and Python
This library provides a set of basic functions for different type of deep learning (and other) algorithms in C.This deep learning library will be constantly updated
A collection of deep learning exercises collected while completing an Intro to Deep Learning course. We use TensorFlow and Keras to build and train neural networks for structured data.
This GitHub repository explores the importance of MLP components using the MNIST dataset. Techniques like Dropout, Batch Normalization, and optimization algorithms are experimented with to improve MLP performance. Gain a deeper understanding of MLP components and learn to fine-tune for optimal classification performance on MNIST.
The primary objective of this project is to design and train a deep neural network that can generalize well to new, unseen data, effectively distinguishing between rocks and metal cylinders based on the sonar chirp returns.
To provide a complete pipeline to develop a deep learning model. More specifically, a multiclass classification (single label) deep learning model that can predict what stage of Alzheimer's a patient is, from their MRI image
Implementation of CNN (consisting of maxpool, relu, fully-connected and convolutional layers) using Numpy Vectorisation (from scratch without any third-party library), followed by analysis using hyperparameter tuning and different regularisation techniques