5 रिपॉजिटरी
Awesome GitHub RepositoriesAutoencoder Compression
Using autoencoder architectures to compress input data into lower-dimensional latent representations.
Distinct from Neural Network Model Compression: Distinct from general model compression (pruning/quantization) as it refers to the architectural compression of data via latent spaces.
Explore 5 awesome GitHub repositories matching artificial intelligence & ml · Autoencoder Compression. Refine with filters or upvote what's useful.
Awesome Autoencoder Compression GitHub Repositories
AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
- pkmital/tensorflow_tutorials
pkmital/tensorflow_tutorials
5,668GitHub पर देखेंयह प्रोजेक्ट शैक्षिक Jupyter Notebooks का एक संग्रह है जो TensorFlow फ्रेमवर्क का उपयोग करके न्यूरल नेटवर्क निर्माण और टेंसर ऑपरेशंस पर ट्यूटोरियल प्रदान करता है। यह मशीन लर्निंग के छात्रों के लिए एक शैक्षिक रिपॉजिटरी और इम्प्लीमेंटेशन गाइड के रूप में कार्य करता है। यह सूट विशिष्ट उन्नत आर्किटेक्चर पर केंद्रित है, जिसमें इमेज क्लासिफिकेशन के लिए कन्वेन्शनल नेटवर्क, ट्रेनिंग स्टेबिलिटी के लिए स्किप कनेक्शन वाले रेजिडुअल नेटवर्क, और जेनरेटिव मॉडलिंग व डेटा सिंथेसिस के लिए वेरिएशनल ऑटोएनकोडर शामिल हैं। इसमें फीचर एक्सट्रैक्शन और डाइमेंशनलिटी रिडक्शन करने के लिए डीनोइजिंग और डीप ऑटोएनकोडर बनाने के लिए गाइड भी शामिल हैं। रिपॉजिटरी प्रेडिक्टिव मॉडलिंग के व्यापक दायरे को कवर करती है, जिसमें निरंतर मानों और बाइनरी परिणामों की भविष्यवाणी के लिए लीनियर, पॉलिनोमियल और लॉजिस्टिक रिग्रेशन के इम्प्लीमेंटेशन शामिल हैं।
Provides implementations of autoencoders to compress input data into lower-dimensional latent representations.
Jupyter Notebook - binroot/tensorflow-book
BinRoot/TensorFlow-Book
4,431GitHub पर देखेंThis project is a collection of TensorFlow machine learning examples providing reference implementations for various neural network paradigms. It covers supervised, unsupervised, reinforcement, and sequential learning models. The repository includes implementations for convolutional neural networks focused on image classification and ranking, as well as recurrent neural networks for time-series forecasting and sequence-to-sequence translation. It further provides examples of reinforcement learning agents trained via reward optimization and unsupervised learning techniques such as autoencoders
Uses autoencoder architectures to compress image data into lower-dimensional latent representations for noise removal.
Jupyter Notebookautoencoderbookclassification - commaai/research
commaai/research
4,121GitHub पर देखेंThis research framework provides a deep learning driving simulator and a multimodal data pipeline for autonomous vehicle research. It centers on the creation of synchronized autonomous vehicle datasets, which combine high-frequency vehicle telemetry with camera frames to train neural networks. The project implements a convolutional neural network trainer specifically designed to predict steering angles and vehicle transition states from visual data. It features generative capabilities, using autoencoders and transition models to synthesize driving environments and simulate future vehicle move
Uses autoencoders to learn compressed representations of driving data for image synthesis and model checkpoints.
Python - divamgupta/stable-diffusion-tensorflow
divamgupta/stable-diffusion-tensorflow
1,611GitHub पर देखेंThis project provides a TensorFlow implementation of the Stable Diffusion model, serving as a generative engine for creating and modifying visual content. It functions as a machine learning architecture that translates natural language descriptions into high-quality images by iteratively refining noise within a compressed latent space. The system enables a variety of generative tasks, including text-to-image synthesis, image inpainting to fill missing or masked regions, and image editing to transform existing visuals based on text prompts. Beyond static imagery, the framework supports the gen
Compresses high-resolution pixel data into compact latent representations to reduce computational overhead.
Python - glouppe/info8010-deep-learning
glouppe/info8010-deep-learning
1,291GitHub पर देखेंThis project provides a comprehensive educational curriculum and research resource for deep learning, focusing on the theoretical and technical foundations of neural network implementation. It serves as a structured academic guide for building and training complex models from scratch, covering the essential mathematical primitives, computational graph construction, and automatic differentiation mechanisms required for modern machine learning. The repository distinguishes itself through its extensive coverage of generative modeling and specialized neural architectures. It includes practical im
Reduces high-dimensional input data into compact latent representations using autoencoder architectures.
Jupyter Notebook
