Build CIFAR10 classifiers using Tensorflow, PyTorch, PyTorch Lightning
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Updated
Mar 24, 2023 - Jupyter Notebook
Build CIFAR10 classifiers using Tensorflow, PyTorch, PyTorch Lightning
Implementing different approaches of hyperparameter optimization including random search and bayesian optimization
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.
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
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.
Deep learning projects, using TensorFlow Keras package
Optimization of Neural Network using Keras Tuner
University machine learning labs. MLP, convolutional neural networks, autoencoders, RNN, LSTM, GRU
Train a simple CNN on the Fashion MNIST dataset using Tensorflow Keras.
Keras/Tensorflow implementation to detect faces wearing masks | | Data from Kaggle | | Used pretrained MobileNetV2 with imagenet weights(Transfer Learning) | | Data Augmentation using Keras ImageDataGenerator class | | Result obtained was 89.42% as validation accuracy | | Used OpenCV for inference on webcam feed
Histopathology Images Classification
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