Learning To Learn

यह प्रोजेक्ट एक TensorFlow मेटा-लर्निंग फ्रेमवर्क और रिसर्च टूलकिट है जिसे लर्न ऑप्टिमाइज़र्स को लागू करने और ट्रेन करने के लिए डिज़ाइन किया गया है। यह उन न्यूरल नेटवर्क्स को विकसित करने के लिए टूल्स की एक लाइब्रेरी प्रदान करता है जो अन्य मॉडल्स को ऑप्टिमाइज़ करना सीखते हैं, जो पारंपरिक ग्रेडिएंट-आधारित ऑप्टिमाइज़ेशन एल्गोरिदम की जगह लेते हैं।

इस फ्रेमवर्क में एक प्रॉब्लम एन्सेम्बल मैनेजर शामिल है जो एक साथ ट्रेनिंग के लिए कई अलग-अलग ऑप्टिमाइज़ेशन कार्यों को एक ही वेटेड लॉस फंक्शन में संयोजित करने की अनुमति देता है। यह नेटवर्क इंस्टेंटिएशन के लिए फैक्ट्री पैटर्न का उपयोग करता है और लर्निंग एल्गोरिदम के लक्ष्यों के रूप में कस्टम ऑब्जेक्टिव फंक्शन्स और लॉस ग्राफ्स की परिभाषा का समर्थन करता है।

यह टूलकिट ग्रेडिएंट-आधारित मेटा-ऑप्टिमाइज़ेशन, मॉडल बेंचमार्किंग, और कॉन्फ़िगर करने योग्य अनरोल लेंथ के साथ ट्रेनिंग लूप्स के निष्पादन सहित क्षमताओं की एक विस्तृत श्रृंखला को कवर करती है। यह ग्रेडिएंट प्रीप्रोसेसिंग, सीरियलाइज्ड स्टेट पर्सिस्टेंस, और मीन फाइनल एरर और इपोक ड्यूरेशन जैसे प्रयोग के आंकड़ों की रिपोर्टिंग के लिए यूटिलिटीज भी प्रदान करती है।

Features

Meta-Optimization - Uses gradient-based meta-optimization to refine the learning process of target neural networks.
Deep Learning Toolkits - Provides a set of utilities for configuring problem architectures and evaluating convergence on benchmark datasets.
Problem Ensembling - Manages the combination of multiple distinct optimization tasks into a single weighted loss function.
Learned Optimizers - Implements a library of tools for building and training neural networks that act as optimizers.
Loss Graph Definitions - Provides a way to define mathematical tasks and loss graphs as targets for learned optimization algorithms.
Custom Loss Functions - Implements custom objective functions and weighted problem ensembles to calculate error during training.
Meta Learning Frameworks - Provides a TensorFlow-based framework for training neural networks to optimize other models.
Learned Optimizer Training - Implements training loops with configurable epochs and unroll lengths to improve the performance of learned optimizers.
Meta-Optimizer Training - Executes training loops that optimize a learning algorithm based on cumulative evaluation costs over specified epochs.
Problem Ensembling - Combines multiple distinct optimization tasks into a single weighted loss function for simultaneous training.
Meta Learning Libraries - Provides a toolkit for developing and training neural networks that learn how to optimize other models.
Problem Ensemble Composition - Combines multiple distinct optimization tasks into a single weighted loss function for simultaneous training.
Data Preprocessing - Transforms input gradients using logarithmic scaling and sign extraction to prepare them for model consumption.
Gradient Distribution Transformations - Applies logarithmic transformations and preserves input signs to reshape the distribution of processing data.
Meta-Learning Problem Definitions - Enables the creation of loss graphs for mathematical tasks to serve as training targets for learning algorithms.
Gradient Flow Stabilizers - Applies logarithmic scaling and sign extraction to gradients to ensure numerical stability during training.
Optimizer Convergence Evaluators - Provides utilities to test optimizer effectiveness by measuring convergence and performance on target problems.
Model Execution Benchmarks - Evaluates the execution cost and performance of optimization algorithms across diverse mathematical problem sets.
Benchmark Problem Configurations - Provides tools for setting up network architectures and hyperparameters for consistent testing across benchmark tasks.
Problem Modeling - Provides a framework for defining optimization problem structures, including custom objective functions and loss operations.
Unroll-Length Optimizations - Executes training iterations over specified sequence lengths to optimize the learning algorithm's performance.

स्टार हिस्ट्री

google-deepmindlearning-to-learn

Name: google-deepmind/learning-to-learn
Author: google-deepmind

View on GitHub

4,068 स्टार्स602 फोर्क्सPythonApache-2.04 व्यूज़arxiv.org/abs/1606.04474

Learning To Learn

Features

Meta-Optimization - Uses gradient-based meta-optimization to refine the learning process of target neural networks.
Deep Learning Toolkits - Provides a set of utilities for configuring problem architectures and evaluating convergence on benchmark datasets.
Problem Ensembling - Manages the combination of multiple distinct optimization tasks into a single weighted loss function.
Learned Optimizers - Implements a library of tools for building and training neural networks that act as optimizers.
Loss Graph Definitions - Provides a way to define mathematical tasks and loss graphs as targets for learned optimization algorithms.
Custom Loss Functions - Implements custom objective functions and weighted problem ensembles to calculate error during training.
Meta Learning Frameworks - Provides a TensorFlow-based framework for training neural networks to optimize other models.
Learned Optimizer Training - Implements training loops with configurable epochs and unroll lengths to improve the performance of learned optimizers.
Meta-Optimizer Training - Executes training loops that optimize a learning algorithm based on cumulative evaluation costs over specified epochs.
Problem Ensembling - Combines multiple distinct optimization tasks into a single weighted loss function for simultaneous training.
Meta Learning Libraries - Provides a toolkit for developing and training neural networks that learn how to optimize other models.
Problem Ensemble Composition - Combines multiple distinct optimization tasks into a single weighted loss function for simultaneous training.
Data Preprocessing - Transforms input gradients using logarithmic scaling and sign extraction to prepare them for model consumption.
Gradient Distribution Transformations - Applies logarithmic transformations and preserves input signs to reshape the distribution of processing data.
Meta-Learning Problem Definitions - Enables the creation of loss graphs for mathematical tasks to serve as training targets for learning algorithms.
Gradient Flow Stabilizers - Applies logarithmic scaling and sign extraction to gradients to ensure numerical stability during training.
Optimizer Convergence Evaluators - Provides utilities to test optimizer effectiveness by measuring convergence and performance on target problems.
Model Execution Benchmarks - Evaluates the execution cost and performance of optimization algorithms across diverse mathematical problem sets.
Benchmark Problem Configurations - Provides tools for setting up network architectures and hyperparameters for consistent testing across benchmark tasks.
Problem Modeling - Provides a framework for defining optimization problem structures, including custom objective functions and loss operations.
Unroll-Length Optimizations - Executes training iterations over specified sequence lengths to optimize the learning algorithm's performance.

Learning To Learn के ओपन-सोर्स विकल्प

समान ओपन-सोर्स प्रोजेक्ट्स, जो Learning To Learn के साथ साझा की गई सुविधाओं के आधार पर रैंक किए गए हैं।

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Learning To Learn के सभी 30 विकल्प देखें

Learning To Learn को शामिल करने वाली क्यूरेटेड खोजें

चुनिंदा कलेक्शन जहाँ Learning To Learn दिखाई देता है।

स्किल डेवलपमेंट के लिए प्रोग्रामिंग प्रोजेक्ट आइडियाज

अक्सर पूछे जाने वाले प्रश्न

google-deepmind/learning-to-learn क्या करता है?

google-deepmind/learning-to-learn की मुख्य विशेषताएं क्या हैं?

google-deepmind/learning-to-learn की मुख्य विशेषताएं हैं: Meta-Optimization, Deep Learning Toolkits, Problem Ensembling, Learned Optimizers, Loss Graph Definitions, Custom Loss Functions, Meta Learning Frameworks, Learned Optimizer Training।

google-deepmind/learning-to-learn के कुछ ओपन-सोर्स विकल्प क्या हैं?

google-deepmind/learning-to-learn के ओपन-सोर्स विकल्पों में शामिल हैं: catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,… unity-technologies/game-programming-patterns-demo — This project provides a collection of architectural patterns and structural approaches designed to organize game logic… allenai/allennlp — AllenNLP is a PyTorch-based research library and deep learning language toolkit designed for developing and training… antixk/pytorch-vae — This project is a deep learning research toolkit and generative model library providing implementations of Variational… microsoft/cntk — CNTK is a deep learning toolkit used for the design, construction, and training of neural networks. It defines model… speechbrain/speechbrain — SpeechBrain is an all-in-one deep learning toolkit designed for speech and audio processing. Built as a modular…

स्टार हिस्ट्री

अक्सर पूछे जाने वाले प्रश्न

google-deepmind/learning-to-learn क्या करता है?

google-deepmind/learning-to-learn की मुख्य विशेषताएं क्या हैं?

google-deepmind/learning-to-learn के कुछ ओपन-सोर्स विकल्प क्या हैं?

Learning To Learn

Features

स्टार हिस्ट्री

Learning To Learn

Features

Learning To Learn के ओपन-सोर्स विकल्प

catboost/catboost

Unity-Technologies/game-programming-patterns-demo

allenai/allennlp

AntixK/PyTorch-VAE

Learning To Learn को शामिल करने वाली क्यूरेटेड खोजें

अक्सर पूछे जाने वाले प्रश्न

स्टार हिस्ट्री

Learning To Learn को शामिल करने वाली क्यूरेटेड खोजें

Learning To Learn के ओपन-सोर्स विकल्प

catboost/catboost

Unity-Technologies/game-programming-patterns-demo

allenai/allennlp

AntixK/PyTorch-VAE

अक्सर पूछे जाने वाले प्रश्न