#Tensorflow

Explore a tutorial focused on applying deep learning in image processing, without overstatements or promotional language. The course targets learners at all levels, offering video sessions on constructing and training networks using PyTorch and TensorFlow. Gain insights into models like LeNet, AlexNet, ResNet, and their application across tasks such as classification, detection, and segmentation. Detailed navigation includes network explanations and coding examples, with resources like downloadable PPTs for an efficient learning path.

This project utilizes a seq2seq model to generate Chinese couplets using Tensorflow. It offers a demo and requires Python 3.6 and a dataset. The model can be trained via 'couplet.py', with metrics like loss and BLEU score tracked on Tensorboard. For continuous training, sessions can be resumed effortlessly. Additionally, the model can be deployed as a web service via 'server.py' or Docker. Example couplets include '天朗气清风和畅' paired with '云蒸霞蔚日光辉'. Suitable for those interested in NLP and language generation.

Discover a comprehensive reinforcement learning framework leveraging OpenAI Gym and TensorFlow. This system offers a unified interface, essential RL algorithm implementations, and automated analytics, optimizing algorithm development. Suitable for extensive experimentation, hypothesis testing, and hyperparameter optimization, with settings stored for reproducibility. Evaluate algorithm performance across various environments using the Fitness Matrix. Start developing RL agents with provided components and look forward to future support for PyTorch.

This project evaluates the speed and format support of audio I/O performance across Python libraries, essential for real-time machine learning models. It benchmarks libraries such as scipy, librosa, and torchaudio on their efficiency at loading audio into Numpy, PyTorch, and TensorFlow tensors. The results highlight the quickest options for various output types. The framework also facilitates the generation of sample audio data and the setup of testing environments using Docker or virtual setups for simplified replication and contribution.

Discover advanced models for image and text recognition and segmentation using Keras' cv_attention_models. The library includes a wide selection of model architectures like CoAtNet, EfficientNet, and YOLO, facilitating model customization and backend conversion with PyTorch support. Seamlessly integrates with TensorFlow, making it ideal for tasks such as ImageNet training and evaluation. Suitable for researchers and developers aiming to enhance their AI initiatives with leading-edge technologies.

Access essential cheat sheets offering insights and quick references for engineers in machine learning and deep learning. These resources cover tools like TensorFlow, Keras, PyTorch, and Numpy, and are tailored to support both novices and experienced professionals in improving their skills and efficiency. Each cheat sheet is crafted to present clear and informative content, enabling swift comprehension of key methodologies. Explore a variety of frameworks and libraries that facilitate modern AI and data science applications.

Athena is an open-source engine for end-to-end speech processing, suitable for both industrial and research applications. Built on Tensorflow, it includes models for tasks such as ASR, TTS, VAD, and KWS. Athena supports hybrid attention/CTC models, multi-GPU training with Horovod, and WFST-based decoding. Recent enhancements allow Tensorflow C++ deployment and introduce models like AV-Transformer and Conformer-CTC. The platform aims to make advanced speech processing accessible to all, backed by thorough documentation and community resources.

The project introduces an attention-based OCR model designed for image recognition, with tools for creating datasets and exporting trained models. Originating from the work of Qi Guo and Yuntian Deng, it employs CNN, LSTM, and attention mechanisms to enhance OCR accuracy. Installation is straightforward, accompanied by extensive training and testing features, including customizable dataset creation and model visualization. Export formats like SavedModel and frozen graph are supported. The model is suitable for scalable deployment via Tensorflow Serving and Google Cloud ML Engine, with flexible settings for diverse image processing applications.

FER is a Python tool for facial expression recognition supporting version 3.6 and above, leveraging OpenCV and TensorFlow for efficient emotion detection in images and videos. It utilizes OpenCV's Haar Cascade and MTCNN for improved accuracy. Performance can be boosted with TensorFlow-GPU support. The tool includes a Keras model for flexibility but allows custom models too. Suitable for applications in research and security, FER integrates easily into various projects, offering dependable results.

The TensorFlow template aids in structuring deep learning projects with a focus on simplicity, optimized folder organization, and sound OOP principles. It efficiently manages shared tasks and shifts focus to primary components such as models and training routines. Noteworthy features include a user-friendly architecture, a detailed folder structure with model and trainer templates, and Comet.ml integration for real-time metrics and version control. The open-source project invites community contributions and feedback for ongoing improvements.

Discover a diverse array of open-source resources and tutorials aimed at providing a solid foundation in machine learning and deep learning. This guide covers essential mathematical concepts, beginner-friendly paths, and advanced topics in ML and DL, including applications in computer vision, NLP, and reinforcement learning. It also provides practical advice on participating in Kaggle competitions and gaining engineering skills essential for future ML engineers.

This open-source project utilizes Bumble's EfficientNet-v2-based model for detecting lewd images, allowing for potential safety improvements on the internet. The Private Detector model, trained on an internal dataset, is designed for easy deployment and community finetuning. Users can download the pretrained model and use the provided scripts for straightforward inference. The project also supports custom training on user-specific data, offering opportunities for personalization. It focuses on security and privacy, encouraging developers to contribute to a better digital environment. Supports community-driven customization for diverse applications.

Explore an innovative machine learning framework specifically crafted for integration with Arm processors. This streamlined solution, derived from TensorFlow, allows developers to deploy and test models on embedded systems with minimal memory utilization, approximately 2KB. The architecture is designed for low-power usage, guaranteeing system safety and ease of debugging. It offers tutorials, high-level APIs, and customizable operators for operation optimization. Whether developing locally or using Arm Mbed OS, this framework assures adaptability and performance for cutting-edge edge computing applications.

Discover the updated Gomoku AI featuring a rewritten codebase that enhances stability and simplicity. This AI applies the minimax algorithm and performance optimizations for a stable gaming experience, and it incorporates the latest React version (V18). Run locally after first connecting online. It is perfect for those interested in AI concepts and browser-based execution challenges. Engage with a learning community and access detailed tutorials and open-source resources for deeper understanding.

Discover a variety of practical NLP tutorials powered by TensorFlow 2.0. Access insights from a comprehensive 1,000-page e-Book on deep learning. Recent updates include BERT and KoGPT-2 examples in text classification, named entity recognition, and chatbot development. Perfect for learners interested in hands-on training via Colab links without requiring local Python setups.

Discover insights into speech recognition using the TensorFlow sequence-to-sequence framework. Despite its outdated status, this project serves educational purposes, focusing on creating standalone Linux speech recognition. While new projects like Whisper and Mozilla's DeepSpeech lead advancements, foundational techniques remain essential. Packed with modular extensions and educational examples, it offers a platform for learning and experimentation. Detailed installation guides specify key dependencies such as PyAudio.

The project provides an optimized Tensorflow Lite binary for Raspberry Pi with XNNPACK support to boost on-device inference performance. It supports various Raspberry Pi models and OS versions, offering efficient machine learning capabilities with easy setup. The binary includes Python API support and installation instructions for Tensorflow v1 and v2 across different Linux environments. Previous Wheel versions are accessible, and guidelines for compiling Tensorflow C bindings are available.