Search Results for "optimization" - Page 2
Sort By:
42 projects for "optimization" with 2 filters applied:
-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
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
Swift for TensorFlow
Swift for TensorFlow
...Swift for TensorFlow also introduces tools that allow developers to compute gradients automatically, which is essential for training neural networks through gradient-based optimization. -
2
Machine Learning cheatsheets Stanford
VIP cheatsheets for Stanford's CS 229 Machine Learning
...The project compiles concise explanations of important topics in machine learning and presents them in an accessible format that helps learners review complex ideas quickly. The repository includes summaries covering areas such as supervised learning, unsupervised learning, deep learning, and optimization techniques. In addition to machine learning algorithms, it also contains refresher materials on mathematical prerequisites including probability theory, statistics, linear algebra, and calculus. These cheat sheets are designed to serve as quick reference guides that students can use while studying or reviewing machine learning material. -
3
Weld
High-performance runtime for data analytics applications
Weld is a programming language and runtime designed to improve the performance of data-intensive applications by optimizing computations across multiple libraries. Instead of optimizing individual functions independently, Weld introduces an intermediate representation that allows different frameworks to share optimization opportunities. This approach reduces data movement between libraries and enables the system to generate highly optimized machine code for parallel execution. Weld is particularly useful for workloads involving large-scale data processing in frameworks such as NumPy, Spark, and TensorFlow. The language includes built-in constructs for expressing data-parallel operations, enabling efficient execution on modern hardware architectures. ... -
4
TensorFlow Machine Learning Cookbook
Code for Tensorflow Machine Learning Cookbook
...The repository contains numerous Python scripts and Jupyter notebooks that demonstrate how to implement machine learning algorithms and neural networks using the TensorFlow framework. Each section focuses on a different aspect of machine learning development, including tensor manipulation, model training, optimization strategies, and data processing techniques. The examples illustrate how TensorFlow operations and tensors can be used to build machine learning pipelines and perform tasks such as regression, classification, and clustering. By combining theoretical explanations with executable code, the project helps developers understand how TensorFlow algorithms operate internally while also providing working examples that can be adapted for real projects. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
5
Machine Learning From Scratch
Bare bones NumPy implementations of machine learning models
...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. Because the code avoids external machine learning libraries, it exposes the full logic behind model training, optimization, and prediction processes. The project also provides examples and explanations that illustrate how the algorithms behave and how different components interact during training. -
6
TensorFlow Docs
TensorFlow latest official documentation Chinese version
...Contributors from technology companies, universities, and the open-source community collaborate to maintain and update the translations so they stay aligned with new TensorFlow releases. The documentation covers fundamental concepts such as tensors, computational graphs, model training, optimization, and neural network APIs, along with advanced topics including distributed training and production deployment. -
7
spark-ml-source-analysis
Spark ml algorithm principle analysis and specific source code
spark-ml-source-analysis is a technical repository that analyzes the internal implementation of machine learning algorithms within Apache Spark’s MLlib library. The project aims to help developers and data scientists understand how distributed machine learning algorithms are implemented and optimized inside the Spark ecosystem. Instead of providing a runnable software system, the repository focuses on explaining algorithm principles and examining the underlying source code used in Spark’s... -
8
Computational Linear Algebra for Coders
Free online textbook of Jupyter notebooks
...Instead of emphasizing purely theoretical mathematics, the project takes a programming-oriented approach that helps developers understand how linear algebra algorithms are implemented in real computational systems. The course explores topics such as matrix decomposition, numerical stability, and optimization techniques that are essential for machine learning and data science applications. -
9
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... -
AI-powered service management for IT and enterprise teamsGive your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
-
10
GUAJE FUZZY
Free software for generating understandable and accurate fuzzy systems
GUAJE stands for Generating Understandable and Accurate fuzzy models in a Java Environment. Thus, it is a free software tool (licensed under GPL-v3) with the aim of supporting the design of interpretable and accurate fuzzy systems by means of combining several preexisting open source tools, taking profit from the main advantages of all of them. It is a user-friendly portable tool designed and developed in order to make easier knowledge extraction and representation for fuzzy systems, paying... -
11
OpenANN
An open source library for artificial neural networks.
OpenANN is a C++ library that provides implementations for modular artificial neural networks with only a few dependencies and additional Python bindings. -
12
Swarm Wars
Safety in numbers.
REPOSITORY MOVED TO GITHUB: https://github.com/happyjack27/SwarmWars video sample: http://youtu.be/s5mLNbdBQGY A game where you evolve & compete AI swarms. The organisms use swarm intelligence & ant colony optimization. The organisms can communicate through 3-color signaling as well as by laying beacons. They can attack and repair other organisms. They can select mates, and they can gather and distribute food and material. This behavior is controlled by a genetically evolved neural net augmented with online back propagation learning. ... -
13
Stanford Machine Learning Course
machine learning course programming exercise
The Stanford Machine Learning Course Exercises repository contains programming assignments from the well-known Stanford Machine Learning online course. It includes implementations of a variety of fundamental algorithms using Python and MATLAB/Octave. The repository covers a broad set of topics such as linear regression, logistic regression, neural networks, clustering, support vector machines, and recommender systems. Each folder corresponds to a specific algorithm or concept, making it easy... -
14
Optex Analyzer is a software to analyze and compare algorithms to solve approximately optimization problems. It has a GUI that allows select a set of input files containing raw algorithm results. The analysis is shown with tables and charts.
-
15
Program to performing the complete cycle of neural networks analysis: preparing data, choosing neural network (CasCor, MP, LogRegression, PNN), learning of network, monitoring learning state, ROC-analysis, optimization of network parameters using GA.
-
16
This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.
-
17
pySPACE
Signal Processing and Classification Environment in Python using YAML
pySPACE is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due...
