#Keras

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.

This tutorial demonstrates deep learning techniques for ultrasound nerve segmentation using Keras, detailing data pre-processing, model architecture, and training strategies. It guides users through data preparation and model definition, aligning with the Kaggle competition framework, while emphasizing Keras's modular and experimental-friendly nature.

TensorFlow Recommenders is an open-source library crafted for creating recommender system models using TensorFlow. This library guides users through the complete workflow, encompassing data preparation, model training, evaluation, and deployment. Integrated with Keras, it combines an intuitive learning curve with the ability to construct complex models. Through easy pip installation and abundant resources like tutorials and API references, it allows efficient model building with datasets such as Movielens 100K. The library's advanced embedding-based capabilities enhance both user and item representation, boosting recommendation precision.

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.

Development on TensorFlow Addons has officially stopped, and the project is now in a minimal maintenance phase, set to conclude by May 2024. Transitioning to other TensorFlow ecosystems like Keras, Keras-CV, and Keras-NLP is recommended. Initially, Addons provided additional functionalities in line with TensorFlow's API, supplementing what was unavailable in the core. Existing components such as tfa.activations, tfa.layers, and tfa.optimizers can still be used, provided they align with specific TensorFlow versions for proper function. Explore other TensorFlow suite options for continued machine learning advancements.

A Python library built on Keras and TensorFlow, enabling the efficient creation of customizable neural networks for text generation. Supports character and word-level outputs with features like attention-weighting and skip-embedding. Configurable RNN dimensions and bidirectional use enhance training efficiency on GPUs, making it applicable for tasks from chatbots to creative content generation.

Talos facilitates automated hyperparameter tuning and model evaluation in TensorFlow and Keras, aiming for robust results without complexity. It assists researchers and data scientists with a straightforward, single-line pipeline, ensuring complete control over models. With no new syntax to master and minimal additional workload, Talos supports efficient exploration across various prediction tasks. Key features include dynamic optimization strategies, live training monitoring, and detailed analytics, suitable for different OS and hardware configurations. Discover how Talos can optimize model efficiency with simple integration.

Leverage TensorFlow's power in .NET projects by building, training, and deploying models using C#. TensorFlow.NET provides comprehensive TensorFlow API support within the .NET Standard framework, facilitating integration with existing TensorFlow code. It supports model operations across Windows, Linux, and MacOS, with a built-in Keras interface for straightforward code translation from Python. Contributions are welcome to continue advancing the library, offering developers a robust solution to utilize TensorFlow within the .NET ecosystem.

DeLFT is a versatile platform for text processing using Keras and TensorFlow, focusing on sequence labeling and text classification. It handles various text types, including rich text with layout details. By offering state-of-the-art models for reproducibility, DeLFT enables efficient benchmarking. The framework is designed for production-level applications, offering optimized performance and integration with HuggingFace transformers. PyPI availability makes it suitable for large-scale NLP tasks.

Spektral offers a comprehensive framework for developing graph neural networks using Python’s Keras and TensorFlow 2, with tasks including social network user classification and molecular property prediction. The 1.0 release simplifies data handling through new Graph and Dataset containers, a simplified Loader class, and versatile GNN classes, supported by extensive examples and documentation.

Explore advanced image enhancement with Keras implementations of Residual Dense Networks for single image super-resolution. This Python-based project enhances image resolution efficiently, utilizing content and adversarial losses. It includes support for Docker and Google Colab, making it ideal for cloud-based applications. Freely available under Apache 2.0 license and compatible with Python 3.6, it welcomes community contributions.

The project provides modular implementations of essential deep reinforcement learning algorithms using Keras suitable for discrete and continuous action spaces. It features Actor-Critic approaches like A2C and A3C and Deep Q-Learning variations such as DDQN with prioritized experience replay and dueling networks. Requiring Keras 2.1.6 and OpenAI Gym, it facilitates efficient and scalable setups, with tools for visualization and monitoring through TensorBoard and Plotly, focusing on stability and exploration in complex environments.

Discover a streamlined method to incorporate Keras models into C++ applications using this lightweight, header-only library. It enables efficient model predictions without relying on TensorFlow, providing smaller binary sizes and single-core CPU operation. This library is optimized for ease of integration and supports both Keras sequential and functional API models, accommodating complex architectures with popular dependencies like FunctionalPlus and Eigen. Suitable for developers looking to integrate machine learning into C++ efficiently.

TensorFlow GNN is a library for creating Graph Neural Networks within the TensorFlow framework, providing tools like GraphTensor for mixed-type graphs, data preparation utilities, and pre-built models. Initially a Google-internal tool, it supports a variety of uses, integrating smoothly with other scalable graph technology. Practical applications can be explored via Google Colab examples, with demos including molecular graph classification and shortest path learning. Compatible with TensorFlow 2.12+, Apache Beam, and Keras v2, it offers a reliable development setup.

NNoM is a neural network library tailored for microcontrollers, designed to facilitate efficient deployment of models such as Inception, ResNet, and DenseNet. It integrates smoothly with Keras, offering structured interfaces that improve functionality. Features include per-channel quantization and onboard evaluation, optimizing performance without runtime loss. Recent updates bring RNN support, catering to small footprint platforms. Geared towards embedded developers, NNoM focuses on efficient neural network operations on MCUs, managing models, memory, and inference effectively. It supports TensorFlow up to version 2.14.

Explore a variety of interactive machine learning experiments utilizing Jupyter/Colab notebooks and demo pages. This project encompasses a range of concepts, from supervised learning with TensorFlow and Keras to CNNs and RNNs. Serving as an educational platform, it allows for the practice of different algorithms and datasets. Suitable for experimentation with models such as Multilayer Perceptron for digit recognition and CNNs for image classification. Gain insights into model training and performance through comprehensive demos and sessions. This project is intended for educational exploration rather than optimized deployment.

QKeras enhances Keras by introducing quantized layer replacements, facilitating efficient transition to quantized networks while preserving Keras’s core strengths of modularity and user-friendliness. It aids in designing low-latency models for edge devices and offers tools to estimate energy consumption. Explore how QKeras streamlines model quantization.

Gain insights into the fundamentals and advanced techniques of building recommender systems with Python. This guide provides an overview of content-based filtering, collaborative filtering, matrix factorization, and more using popular datasets like Kaggle's movie and movielens datasets. Discover practical implementations using deep learning with Keras, wide & deep models, and innovative methods using ChatGPT and LLM for enhanced explainability. Learn to apply these techniques to datasets such as Naver news and understand the principles behind each method, serving data scientists and developers.

The onnx2tflite project simplifies the conversion of ONNX models to TensorFlow Lite, maintaining high precision and efficiency. It minimizes errors per element to less than 1e-5 compared to ONNX outputs and accelerates model output, achieving speeds 30% faster than other methods. This tool automatically aligns channel formats and supports the deployment of quantized models, including fp16 and uint8. Users can modify input and output layers, add custom operators, and benefit from a straightforward code structure, making it a versatile solution for AI model deployments.

Discover deep learning with TensorFlow and Keras in this course by Jeff Heaton at Washington University. Learn about neural networks like CNN, LSTM, and GAN, and their applications in computer vision, NLP, and data generation. Understand deep learning efficiency on GPUs and explore Python innovations. This course provides a balance of theory and practical skills, connecting knowledge with real-world projects. No prior Python experience needed. Advance your deep learning skills and adapt to new technology trends.