A framework for training and evaluating a transformer with scaled dot product attention on a tensorflow dataset.
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Updated
Jul 19, 2021 - Python
A framework for training and evaluating a transformer with scaled dot product attention on a tensorflow dataset.
We are going to use CPU for Extract , Transform and Load, and GPU for training model parallelly
Classifying citrus leaf images based on disease type using Convolutional Neural Networks(CNNs).
Library aiding testing of models resicilience to adverasial attacks. Created as a part of Bachelor's Thesis.
It's a sample project for a recommender system using TensorFlow
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.
Using Keras models and datasets to build custom prediction models
TensorFlow implementation of Deep Convolutional Generative Adversarial Networks
You can freely purchase the block from:
Create Convolutional Neural Network from scratch with potato disease classification. App will allow farmers to snap a picture of a plant and determine whether the plant has a disease or not.
building recommendation system using TensorFlow Recommenders
Tf dataset Citrus_leaves is demonstrated for the Data Augmentation deep learning.
The repository contains the materials discussed in part 1 of the Image Classification with YonoHub & Tensorflow V2.0 Series
High-level API for tar-based dataset
ZnH5MD - High Performance Interface for H5MD Trajectories
Demo of data augmentation: a technique to increase the diversity of your training set by applying random (but realistic) transformations, such as image rotation
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.
Scripts used in experiments to develop models for image classification
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