A simple JavaScript library to help you quickly identify unseemly images; all in the client's browser. NSFWJS isn't perfect, but it's pretty accurate (~90% with small and ~93% with midsized model)... and it's getting more accurate all the time.
The library categorizes image probabilities in the following 5 classes:
Tags | javascript machine-learning content-management machinelearning node-module tensorflowjs tensorflow-js nsfw-recognition |
Implementation | Javascript |
License | MIT |
Platform | NodeJS |
Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
tensorflow tensorflow-tutorials tensorflow-android machine-learning machine-learning-android tensorflow-models tensorflow-examples deep-learning deep-neural-networks deeplearning deep-learning-tutorialTensorFlow is Google's machine learning runtime. It is implemented as C++ runtime, along with Python framework to support building a variety of models, especially neural networks for deep learning. It is interesting to be able to use TensorFlow in a node.js application using just JavaScript (or TypeScript if that's your preference). However, the Python functionality is vast (several ops, estimator implementations etc.) and continually expanding. Instead, it would be more practical to consider building Graphs and training models in Python, and then consuming those for runtime use-cases (like prediction or inference) in a pure node.js and Python-free deployment. This is what this node module enables.
tensorflow node-tensorflow nodejs machine-learning deep-learning npm-package tf tensor ml ai neural-networks neuralnetworks deeplearning model numerical-computation googleA TensorFlow backed FaceNet implementation for Node.js, which can solve face verification, recognition and clustering problems. FaceNet is a deep convolutional network designed by Google, trained to solve face verification, recognition and clustering problem with efficiently at scale.
facenet openface deepface face recognition verification clustering machine deep learning neural network tensorflowAll pull requests are welcome, make sure to follow the contribution guidelines when you submit pull request.
tensorflow tensorflow-tutorials mnist-classification mnist machine-learning android tensorflow-models machine-learning-android tensorflow-android tensorflow-model mnist-model deep-learning deep-neural-networks deeplearning deep-learning-tutorialCompared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. Other research on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed. Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.
machine-learning deep-learning lstm human-activity-recognition neural-network rnn recurrent-neural-networks tensorflow activity-recognitionNLP-Models-Tensorflow, Gathers machine learning and tensorflow deep learning models for NLP problems, code simplify inside Jupyter Notebooks 100%. I will attached github repositories for models that I not implemented from scratch, basically I copy, paste and fix those code for deprecated issues.
nlp machine-learning embedded deep-learning chatbot language-detection lstm summarization attention speech-to-text neural-machine-translation optical-character-recognition pos-tagging lstm-seq2seq-tf dnc-seq2seq luong-apiA generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception. This model has been pre-trained for the ImageNet Large Visual Recognition Challenge using the data from 2012, and it can differentiate between 1,000 different classes, like Dalmatian, dishwasher etc. The program applies Transfer Learning to this existing model and re-trains it to classify a new set of images.
image-detection machine-learning deep-learning deep-neural-networks convolutional-neural-networks tensorflowLudwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. All you need to provide is a CSV file containing your data, a list of columns to use as inputs, and a list of columns to use as outputs, Ludwig will do the rest. Simple commands can be used to train models both locally and in a distributed way, and to use them to predict on new data.
deep-learning deeplearning deep-neural-networks deep learning machine-learning machinelearning machine natural-language-processing natural-language-understanding natural-language natural-language-generation computer-vision python393% Accuracy with the following confusion matrix, based on Inception V3. Review the _art folder for previous incarnations of this model.
machine-learning keras machinelearning inceptionv3 inception-v3 nsfw-dataNeuropod is a library that provides a uniform interface to run deep learning models from multiple frameworks in C++ and Python. Neuropod makes it easy for researchers to build models in a framework of their choosing while also simplifying productionization of these models. It currently supports TensorFlow, PyTorch, TorchScript, Keras and Ludwig.
machine-learning deep-learning tensorflow keras inference pytorch machinelearning deeplearning incubationThis is a research project, not an official NVIDIA product. OpenSeq2Seq main goal is to allow researchers to most effectively explore various sequence-to-sequence models. The efficiency is achieved by fully supporting distributed and mixed-precision training. OpenSeq2Seq is built using TensorFlow and provides all the necessary building blocks for training encoder-decoder models for neural machine translation and automatic speech recognition. We plan to extend it with other modalities in the future.
neural-machine-translation multi-gpu deep-learning sequence-to-sequence seq2seq multi-node speech-recognition speech-to-text mixed-precision float16Project DeepSpeech is an open source Speech-To-Text engine. It uses a model trained by machine learning techniques, based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow project to make the implementation easier.
deep-learning machine-learning neural-networks tensorflow speech-recognition speech-to-textA JavaScript application framework for machine learning and its engineering. With the mission of enabling JavaScript engineers to utilize the power of machine learning without any prerequisites and the vision to lead front-end technical field to the intelligention. Pipcook is to become the JavaScript application framework for the cross-cutting area of machine learning and front-end interaction.
machine-learning js pipeline tensorflowThis is the code that accompanies my blog post Getting started with TensorFlow on iOS. It uses TensorFlow to train a basic binary classifier on the Gender Recognition by Voice and Speech Analysis dataset.
tensorflow metal ios machine-learning logistic-regression voice-recognitionSpeech recognition using google's tensorflow deep learning framework, sequence-to-sequence neural networks. Replaces caffe-speech-recognition, see there for some background.
tensorflow speech-recognition neural-network deep-learning stt speech-to-textThe goal of this repository is to provide comprehensive tutorials for TensorFlow while maintaining the simplicity of the code. Each tutorial includes a detailed explanation (written in .ipynb) format, as well as the source code (in .py format).
deep-learning tensorflow reinforcement-learning machine-learning pattern-recognition object-detection convolutional-neural-networks recurrent-neural-networks neural-networkA Python ML package mainly for educational use.
numpy machine-learning deep-learning visualization tensorflow pytorchThis repository is the out project about mood recognition using convolutional neural network for the course Seminar Neural Networks at TU Delft. We use the FER-2013 Faces Database, a set of 28,709 pictures of people displaying 7 emotional expressions (angry, disgusted, fearful, happy, sad, surprised and neutral).
emotion-recognition tensorflow machine-learning deep-neural-networks convolutional-neural-networksVisual attention-based OCR model for image recognition with additional tools for creating TFRecords datasets and exporting the trained model with weights as a SavedModel or a frozen graph. This project is based on a model by Qi Guo and Yuntian Deng. You can find the original model in the da03/Attention-OCR repository.
machine-learning ocr tensorflow google-cloud ml cnn seq2seq image-recognition hacktoberfest ocr-recognition google-cloud-mlApostropheCMS is content software for everyone in an organization. It helps teams of all sizes create dynamic digital experiences with elegance and efficiency by blending powerful features, developer happiness, and a low learning curve for content creators. Apostrophe has powered websites and web apps for organizations large and small for over a decade. To get started with Apostrophe 3, follow these steps to set up a local development environment. For more detail, refer to the A3 getting started guide in the documentation.
nodejs cms node node-js cms-framework apostrophe hacktoberfest apostrophe-cms apostrophecms node-cms headless-cms content-management-system
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