Search Results for "neural python" - Page 6
Sort By:
178 projects for "neural python" with 1 filter applied:
-
Save Up to 91% on Cloud Compute With Spot VMsRun batch jobs at 60-91% off with Spot VMs. Long-running workloads get automatic discounts with sustained use.
-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
1
Deep-Learning-with-PyTorch-Tutorials
Deep Learning and PyTorch Introduction Video Tutorial with Source Code
Deep-Learning-with-PyTorch-Tutorials is a companion repository for an introductory deep learning course built around PyTorch. It provides source code, notebooks, and presentation materials for a practical video-based learning path. The lessons begin with PyTorch setup, tensors, indexing, mathematical operations, gradients, and basic optimization. They then move into neural networks, logistic regression, multilayer perceptrons, CNNs, ResNet, RNNs, LSTMs, autoencoders, VAEs, GANs, graph... -
2
Machine Learning From Scratch
Bare bones NumPy implementations of machine learning models
ML-From-Scratch is an open-source machine learning project that demonstrates how to implement common machine learning algorithms using only basic Python and NumPy rather than relying on high-level frameworks. The goal of the project is to help learners understand how machine learning algorithms work internally by building them step by step from fundamental mathematical operations. The repository includes implementations of algorithms ranging from simple models such as linear regression and logistic regression to more complex techniques such as decision trees, support vector machines, clustering methods, and neural networks. ... -
3
CCZero (中国象棋Zero)
Implement AlphaZero/AlphaGo Zero methods on Chinese chess
ChineseChess-AlphaZero is a project that implements the AlphaZero algorithm for the game of Chinese Chess (Xiangqi). It adapts DeepMind’s AlphaZero method—combining neural networks and Monte Carlo Tree Search (MCTS)—to learn and play Chinese Chess without prior human data. The system includes self-play, training, and evaluation pipelines tailored to Xiangqi's unique game mechanics. -
4
ELI5
A library for debugging/inspecting machine learning classifiers
ELI5 is a Python library designed to help developers interpret, debug, and explain the predictions of machine learning models. The project focuses on improving model transparency by providing tools that visualize feature importance and prediction reasoning. It supports several popular machine learning frameworks including scikit-learn, XGBoost, LightGBM, CatBoost, and Keras. The library allows users to inspect model weights, analyze decision trees, and compute permutation feature importance... -
$300 Free Credits to Build on Google CloudStart your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query petabytes in BigQuery, or build agents with Gemini Enterprise Agent Platform. Once your credits are used, keep building with 20+ always-free tier products including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. No commitment required—just sign up and start building.
-
5
pytorch-examples
Simple examples to introduce PyTorch
The pytorch-examples project is a collection of concise and practical examples demonstrating how to use PyTorch for machine learning and deep learning tasks. It focuses on clarity and minimalism, providing small, self-contained scripts that illustrate key concepts such as neural network training, optimization, and data handling. The examples cover a range of topics including supervised learning, generative models, and reinforcement learning, making it a valuable resource for both beginners... -
6
UnsupervisedMT
Phrase-Based & Neural Unsupervised Machine Translation
Unsupervised Machine Translation is a research repository that implements both phrase-based SMT and neural MT approaches for translation without parallel corpora. The neural component supports multiple architectures—seq2seq, biLSTM with attention, and Transformer—and allows extensive parameter sharing across languages to improve data efficiency. Training relies on denoising auto-encoding and back-translation, with on-the-fly, multithreaded generation of synthetic parallel data to continually... -
7
TensorFlow Docs
TensorFlow latest official documentation Chinese version
TensorFlow Docs repository maintained by the Xitu translation community provides a Chinese version of the official TensorFlow documentation. Its goal is to make the extensive TensorFlow ecosystem more accessible to developers and researchers who prefer to learn in Chinese. The repository contains translated guides, API explanations, tutorials, and conceptual documentation that mirror the structure of the original TensorFlow documentation site. Contributors from technology companies,... -
8
MIT Deep Learning
Tutorials, assignments, and competitions for MIT Deep Learning
MIT Deep Learning is an open-source repository that contains tutorials, assignments, and learning materials related to deep learning courses taught at MIT. The repository provides hands-on tutorials that introduce the fundamental concepts behind neural networks, deep learning architectures, and modern machine learning techniques. Many of the tutorials include practical implementations that demonstrate tasks such as image classification, generative models, and neural network training... -
9
data-science-ipython-notebooks
Data science Python notebooks: Deep learning
...Advanced sections touch on neural networks and distributed computing topics, helping you bridge from basics to production-adjacent workflows. The collection is suitable for self-paced study, quick reference, or as teaching materials in workshops. By combining narrative explanations with executable code, it shortens the path from theory to working prototypes. -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
10
Video Nonlocal Net
Non-local Neural Networks for Video Classification
video-nonlocal-net implements Non-local Neural Networks for video understanding, adding long-range dependency modeling to 2D/3D ConvNet backbones. Non-local blocks compute attention-like responses across all positions in space-time, allowing a feature at one frame and location to aggregate information from distant frames and regions. This formulation improves action recognition and spatiotemporal reasoning, especially for classes requiring context beyond short temporal windows. The repo... -
11
Tacotron-2
DeepMind's Tacotron-2 Tensorflow implementation
Tacotron-2 is a TensorFlow implementation of DeepMind’s Tacotron-2 end-to-end text-to-speech architecture, which predicts mel spectrograms from raw text and then feeds them to a neural vocoder such as WaveNet. It reproduces the original paper’s hyperparameters exactly via paper_hparams.py, while also offering a tuned hparams.py with extra improvements that often yield better audio quality in practice. The repository is structured as a full training pipeline: dataset preparation,... -
12
Dynamic Routing Between Capsules
A PyTorch implementation of the NIPS 2017 paper
Dynamic Routing Between Capsules is a PyTorch implementation of the Capsule Network architecture originally proposed to address limitations in traditional convolutional neural networks. Capsule networks aim to improve how neural models represent spatial hierarchies and relationships between objects within images. Instead of scalar neuron activations, capsules output vectors that encode both the presence of features and their spatial properties such as orientation or pose. The repository... -
13
LUMINOTH
Deep Learning toolkit for Computer Vision
LUMINOTH is an open-source deep learning toolkit designed for computer vision tasks, particularly object detection. The framework is implemented in Python and built on top of TensorFlow and the Sonnet neural network library, providing a modular environment for training and deploying detection models. It was created to simplify the process of building and experimenting with deep learning models capable of identifying objects within images. Luminoth includes support for popular object detection architectures such as Faster R-CNN and SSD, enabling developers to train models on datasets like COCO and Pascal VOC. ... -
14
OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
OpenSeq2Seq is a TensorFlow-based toolkit for efficient experimentation with sequence-to-sequence models across speech and NLP tasks. Its core goal is to give researchers a flexible, modular framework for building and training encoder–decoder architectures while fully leveraging distributed and mixed-precision training. The toolkit includes ready-made models for neural machine translation, automatic speech recognition, speech synthesis, language modeling, and additional NLP tasks such as... -
15
cnn-text-classification-tf
Convolutional Neural Network for Text Classification in Tensorflow
The cnn-text-classification-tf repository by Denny Britz is a well-known educational implementation of convolutional neural networks for text classification using TensorFlow, aimed at helping developers and researchers understand how CNNs can be applied to natural language processing tasks. Based loosely on Kim’s influential paper on CNNs for sentence classification, this codebase demonstrates how to preprocess text data, convert words into learned embeddings, and apply multiple convolution... -
16
Generative Models
Collection of generative models, e.g. GAN, VAE in Pytorch
This project is a comprehensive open-source collection of implementations of various generative machine learning models designed to help researchers and developers experiment with deep generative techniques. The repository contains practical implementations of well-known architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Restricted Boltzmann Machines, and Helmholtz Machines, implemented primarily using modern deep learning frameworks like PyTorch... -
17
Compare GAN
Compare GAN code
compare_gan is a research codebase that standardizes how Generative Adversarial Networks are trained and evaluated so results are comparable across papers and datasets. It offers reference implementations for popular GAN architectures and losses, plus a consistent training harness to remove confounding differences in optimization or preprocessing. The library’s evaluation suite includes widely used metrics and diagnostics that quantify sample quality, diversity, and mode coverage. With... -
18
Deep Reinforcement Learning TensorFlow
TensorFlow implementation of Deep Reinforcement Learning papers
Deep Reinforcement Learning TensorFlow is a comprehensive TensorFlow codebase that implements several foundational deep reinforcement learning algorithms for educational and experimental use. The repository focuses on clarity and modularity so users can study how different RL approaches are built and compare their behavior across environments. It includes implementations of well-known algorithms such as Deep Q-Networks (DQN), policy gradients, and related variants, demonstrating how neural... -
19
Siamese and triplet learning
Siamese and triplet networks with online triplet mining in PyTorch
Siamese and triplet learning is a PyTorch implementation of Siamese and triplet neural network architectures designed for learning embedding representations in machine learning tasks. These types of networks learn to map images into a compact feature space where the distance between vectors reflects the similarity between inputs. Such embeddings are commonly used in applications like face recognition, image similarity search, and few-shot learning. The repository demonstrates how to train... -
20
cnn-benchmarks
Benchmarks for popular CNN models
The cnn-benchmarks project is a collection of benchmarking scripts designed to evaluate the performance of convolutional neural networks across different hardware and configurations. It provides standardized implementations of popular CNN architectures, enabling developers to measure training speed, memory usage, and computational efficiency. The project focuses on reproducibility, allowing consistent comparisons between models and environments. It is particularly useful for testing GPUs and... -
21
Neural Photo Editor
A simple interface for editing natural photos
Neural Photo Editor is an experimental machine learning application that demonstrates how generative neural networks can be used as an interactive photo editing tool. The project implements the system described in the research paper Neural Photo Editing with Introspective Adversarial Networks, which introduces a generative model capable of modifying images in semantically meaningful ways. Instead of editing images by directly manipulating pixels, the software allows users to influence... -
22
bulbea
Deep Learning based Python Library for Stock Market Prediction
bulbea is an open-source Python library designed for financial analysis and stock market prediction using machine learning and deep learning techniques. The library provides tools for retrieving financial time series data, preprocessing market data, and training predictive models that estimate future price movements. bulbea integrates common machine learning frameworks such as TensorFlow and Keras to build neural network models capable of learning patterns in historical financial data. ... -
23
seq2seq
A general-purpose encoder-decoder framework for Tensorflow
seq2seq is an early, influential TensorFlow reference implementation for sequence-to-sequence learning with attention, covering tasks like neural machine translation, summarization, and dialogue. It packaged encoders, decoders, attention mechanisms, and beam search into a modular training and inference framework. The codebase showcased best practices for batching, bucketing by sequence length, and handling variable-length sequences efficiently on GPUs. Researchers used it as a baseline to... -
24
Retrieval-Based Conversational Model
Dual LSTM Encoder for Dialog Response Generation
Retrieval-Based Conversational Model in Tensorflow is a project implementing a retrieval-based conversational model using a dual LSTM encoder architecture in TensorFlow, illustrating how neural networks can be trained to select appropriate responses from a fixed set of candidate replies rather than generate them from scratch. The core idea is to embed both the conversation context and potential replies into vector representations, then score how well each candidate fits the current dialogue,... -
25
PyCNN
Image Processing with Cellular Neural Networks in Python
Image Processing with Cellular Neural Networks in Python. Cellular Neural Networks (CNN) are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its applications. CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate (>10,000 frame/s) processing unachievable by digital processors.
