Cross Validation, Grid Search and Random Search for TensorFlow 2 Datasets
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Updated
May 20, 2024 - Python
Cross Validation, Grid Search and Random Search for TensorFlow 2 Datasets
Emoji Drawings dataset
ZnH5MD - High Performance Interface for H5MD Trajectories
Scripts for downloading, preprocessing, and numpy-ifying popular machine learning datasets
Libraries for efficient and scalable group-structured dataset pipelines.
Classifying citrus leaf images based on disease type using Convolutional Neural Networks(CNNs).
Scripts used in experiments to develop models for image classification
High-level API for tar-based dataset
A small library for managing deep learning models, hyperparameters and datasets
This repo consists a Python Notebook file where I have performed transfer learning using Keras Xception Transformer.
A step-by-step tutorial on developing a practical recommendation system (retrieval and ranking) using TensorFlow Recommenders and Keras.
GPU Optimized AlexNet Implementation to train on ImageNet 2012 using Tensorflow 2.x
Re-implementation of Word2Vec using Tensorflow v2 Estimators and Datasets
Library aiding testing of models resicilience to adverasial attacks. Created as a part of Bachelor's Thesis.
Using Keras models and datasets to build custom prediction models
I've completed a number of deep learning and machine learning projects in this repository. In the future, I'll be adding other projects as well. The majority of the data was gathered from Kaggle, TensorFlow datasets, and other places that offer free data. In order to create my models, I used the Google Colab environment.
dataset generator
In this project I trained a CNN model and predicted three types of potato leaf. Either the potato may be healthy or has an early blight disease or late blight disase. The model has good accuracy on these 3 classes. But it is accepting only images of size (256,256) if we pass images other than that shape it won't work.
This project shows step-by-step guide on how to build a real-world flower classifier of 102 flower types using TensorFlow, Amazon SageMaker, Docker and Python in a Jupyter Notebook.
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