A quantitative measure of disease progression one year after baseline
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Updated
Jan 5, 2022 - Jupyter Notebook
A quantitative measure of disease progression one year after baseline
A CNN model to identify images of plant seedlings.
Implementation of early stopping in tensorflow based on any chosen metric
I implemented a CNN to train and test a handwritten digit recognition system using the MNIST dataset. I also read the paper “Backpropagation Applied to Handwritten Zip Code Recognition” by LeCun et al. 1989 for more details, but my architecture does not mirror everything mentioned in the paper. I also carried out a few experiments such as adding…
Time series analysis of SINE wave using Recurrent neural network.
The objective of this repository is to provide a learning and experimentation environment to better understand the details and fundamental concepts of neural networks by building neural networks from scratch.
Deep learning projects using Pytorch and tensorflow.
A Fork of "Early stopping for PyTorch" with a whl file
Single Layer Perceptrons (SLPs) and Multi-Layer Perceptrons (MLPs) from scratch, only with numpy, for classification and regression. MLPs with Keras for time-series prediction.
The objective of this projects is to build a CNN model to accurately detect the presence of Parkinson’s disease in an individual.
Tuner Implementation of Parallel Architecture and Hyperparameter Search via Successive Halving and Classification (SHAC)
A classification model to detect breast cancer
In this repository, I put into test my newly acquired Deep Learning skills in order to solve the Kaggle's famous Image Classification Problem, called "Dogs vs. Cats".
Enhance medical diagnostics with our CNN-powered X-Ray Image Classifier, accurately identifying Covid-19, Normal, and Viral Pneumonia cases for proactive patient care.
Building Early Stopping mechanism using PyTorch
A basic introduction to learning CNN through applications of VGG models.
Features injected recurrent neural networks for short-term traffic speed prediction
Linear regression models on a Car-Price Dataset
implementing AdaBoost from scratch and comparing it with Scikit-Learn's implementation along with exploring concept of early stopping and weighted errors in boosting algorithms.
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