Simple and Efficient Tensorflow implementations of NER models with tf.estimator and tf.data
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Updated
Dec 18, 2018 - Python
Simple and Efficient Tensorflow implementations of NER models with tf.estimator and tf.data
<개발자를 위한 머신러닝&딥러닝> 도서의 코드 저장소
Here's the code associated with my Machine Learning (ML) based hurricane forecasting system
Code relevant for training, evaluating, assessing, and deploying CNNs for image classification and segmentation of Digital Mammography images
Example to load, train, and evaluate ImageNet2012 dataset on a Keras model
An image augmentation library for tensorflow. It can be used seamlessly with tf.data.Dataset
TinyML project. This system monitors your room or surrounding with an onboard microphone of Arduino nano BLE sense. Still Under Developement
TensorFlow 2 Toolkit for Sequence-level Text Recognition that simplifies the process of importing, handling, and visualizing sequence data, as well as providing most used loss functions and evaluation metrics in the development of Sequence Text Recognition models
Multi-task classification project, with the custom training loop implemented from scratch in TF 2.2+ and the usage of "tf.data.Dataset" object for handling the data.
Computer Vision project, built around Convolutional neural network (CNN) for multi-class classification. The project represents an attempt to build modular, OOP approach with an example of how to use modules on MNIST 10-class classification.
Convolutional Denoising Autoencoder for low light image denoising
A web app designed for identifying different disseases for a specific crop
A simple implementation AlexNet
A library to help with the development of AI models with Keras, with a focus on edge / IoT applications. Based originally on https://github.com/yingkaisha/keras-unet-collection
Visual debugging of an MNIST MLP with low-level TensorFlow 2 using tf.data + TensorBoard (scalars, histograms, images)
Streamlit Demo of CIFAR10 Data Classifiers
IMDB sentiment analysis with Keras RNNs (LSTM/GRU). Within-batch padding, bucketing, embeddings, and masking for efficient, accurate training.
German Traffic Signs Classification
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