Build CIFAR10 classifiers using Tensorflow, PyTorch, PyTorch Lightning

Implementing different approaches of hyperparameter optimization including random search and bayesian optimization

Deep learning projects, using TensorFlow Keras package

Using the features in the provided dataset, creating a binary classifier that can predict whether applicants will be successful if funded by Alphabet Soup.

Classifies wild cats images

Convolutional Neural Network on Images with and without Forest Fires

Examples of techniques that can be used to optimize neural network models (some techniques can apply more generally).

training, evaluation and api for forest-cover dataset

My project for two advanced training courses about machine learning and neural networks at educx (https://educx.de/).

Exploring machine learning with nueral networks for a charity analysis. Adjusting the model to try and improve accuracy to predict which projects are likely to be successful.

Keras Tuner used for hyperparameter tuning the neural networks.

The notebook shows how deep learning tools (TensorFlow/Keras and PyTorch ) work in practice.

Neural Network experimentation on the CIFAR-10 dataset ( https://www.cs.toronto.edu/~kriz/cifar.html )

We used a dataset that included birth and personal data as well as Autism Spectrum Quotient test scores to train machine learning algorithms to predict autism. We used Logistic Regression, Neural Network Models and Keras Tuner with Random Oversampling to train one with 90% accuracy.

Histopathology Images Classification

Create a neural network through TensorFlow and Keras to build a model which has the ability to assess an organisation's ability to be successful with funding from the Alphabet Soup charity

A machine learning solution for automating nucleus detection in biomedical images, leveraging the U-Net architecture to accelerate medical research and disease treatment discovery.

Optimization of Neural Network using Keras Tuner

University machine learning labs. MLP, convolutional neural networks, autoencoders, RNN, LSTM, GRU