neural networks
Projects with this topic
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A Python library for Secure and Explainable Machine Learning
Documentation available @ https://secml.gitlab.io
Follow us on Twitter @ https://twitter.com/secml_py
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A flexible neural network framework for running experiments and trying ideas.
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Deep Universal Probabilistic Programming Language
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✭ MAGNETRON ™ ✭: DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
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Creating a neural network without a ML frame work to predict handwritten digits from the MNIST Database (https://en.wikipedia.org/wiki/MNIST_database)
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Graph analysis tools such as embedding techniques and similarity measures.
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✭ MAGNETRON ™ ✭: Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
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✭ MAGNETRON ™ ✭: Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
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✭ MAGNETRON ™ ✭: YOLOv5
🚀 in PyTorch > ONNX > CoreML > TFLiteUpdated -
✭ MAGNETRON ™ ✭: HEARING PROXIA. DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
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✭ MAGNETRON ™ ✭:
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorchUpdated -
✭ MAGNETRON ™ ✭: The official Exploit Database repository
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✭ MAGNETRON ™ ✭: for making an IMAGINATION PROXIA/IMAGINATION PROXIA (A1). Instant neural graphics primitives: lightning fast NeRF (Neural Radiance Fields) and more...
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SecML models and databases zoo.
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TensorFlow examples for Artificial Neural Networks course including: MLP with softmax output layer; MLP and CNN for MNIST dataset; CNN for CIFAR-10 dataset with data augmentation; LSTM with CNN layer for IMDB sentiment classification task.
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Implementation of a Keyword Spotting Algorithm (KWS) using deep neural networks in the edge.
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sentiment analysis using keras and tensorflow
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An application that generates Singapore news headlines using a neural network trained on news headlines from a newspaper in Singapore. The dockerized API application served using Flask, gunicorn, and Nginx.
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Experiments regarding Artificial Neural Network's modularization. Main Hypothesis is that modularization helps to reduce training time and the amount of training data required. Furthermore, it can also help generalization and lead to better results.
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