There are 3 repositories under tensorflow-datasets topic.
A step-by-step tutorial on developing a practical recommendation system (retrieval and ranking) using TensorFlow Recommenders and Keras.
Try to use tf.estimator and tf.data together to train a cnn model.
Repository for Google Summer of Code 2019 https://summerofcode.withgoogle.com/projects/#4662790671826944
Building an image classifier in TF2
TensorFlow 2.0 implementation of Improved Training of Wasserstein GANs
Libraries for efficient and scalable group-structured dataset pipelines.
A collection of Korean Text Datasets ready to use using Tensorflow-Datasets.
tiny-imagenet dataset downloader & reader using tensorflow_datasets (tfds) api
Cross Validation, Grid Search and Random Search for TensorFlow 2 Datasets
GPU Optimized AlexNet Implementation to train on ImageNet 2012 using Tensorflow 2.x
Example to load, train, and evaluate ImageNet2012 dataset on a Keras model
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.
Build MAXELLA App to recommend Movies using TensorFlow Recommenders (TFRS)
This repository contains the exercise notebooks for the Data Pipelines with TensorFlow Data Services (Coursera) course.
VGGNet-Family (11, 13, 16 & 19) Implementation to train on ImageNet 2012 using Tensorflow 2.x
tfrecord
Tf dataset Citrus_leaves is demonstrated for the Data Augmentation deep learning.
Google Summer of Code - 2019
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.
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.
We are going to use CPU for Extract , Transform and Load, and GPU for training model parallelly
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.
Utilize mobilenet_v2 and Finetune it with potato disease image dataset (3 classes). App will allow farmers to snap a picture of a plant and determine whether the plant has a disease or not.
The repository contains the materials discussed in part 1 of the Image Classification with YonoHub & Tensorflow V2.0 Series
Classifying citrus leaf images based on disease type using Convolutional Neural Networks(CNNs).
dataset generator
Demo of data augmentation: a technique to increase the diversity of your training set by applying random (but realistic) transformations, such as image rotation
This repo consists a Python Notebook file where I have performed transfer learning using Keras Xception Transformer.
Using Keras models and datasets to build custom prediction models