Alternatives to AForge.NET
Compare AForge.NET alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to AForge.NET in 2026. Compare features, ratings, user reviews, pricing, and more from AForge.NET competitors and alternatives in order to make an informed decision for your business.
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Fido
Fido
Fido is a light-weight, open-source, and highly modular C++ machine learning library. The library is targeted towards embedded electronics and robotics. Fido includes implementations of trainable neural networks, reinforcement learning methods, genetic algorithms, and a full-fledged robotic simulator. Fido also comes packaged with a human-trainable robot control system as described in Truell and Gruenstein. While the simulator is not in the most recent release, it can be found for experimentation on the simulator branch. -
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Torch
Torch
Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner. -
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Accord.NET Framework
Accord.NET Framework
The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive documentation and wiki helps fill in the details. -
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Chainer
Chainer
A powerful, flexible, and intuitive framework for neural networks. Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort. Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures. Forward computation can include any control flow statements of Python without lacking the ability of backpropagation. It makes code intuitive and easy to debug. Comes with ChainerRLA, a library that implements various state-of-the-art deep reinforcement algorithms. Also, with ChainerCVA, a collection of tools to train and run neural networks for computer vision tasks. Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort. -
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Darknet
Darknet
Darknet is an open-source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. You can find the source on GitHub or you can read more about what Darknet can do. Darknet is easy to install with only two optional dependencies, OpenCV if you want a wider variety of supported image types, and CUDA if you want GPU computation. Darknet on the CPU is fast but it's like 500 times faster on GPU! You'll have to have an Nvidia GPU and you'll have to install CUDA. By default, Darknet uses stb_image.h for image loading. If you want more support for weird formats (like CMYK jpegs, thanks Obama) you can use OpenCV instead! OpenCV also allows you to view images and detections without having to save them to disk. Classify images with popular models like ResNet and ResNeXt. Recurrent neural networks are all the rage for time-series data and NLP. -
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ConvNetJS
ConvNetJS
ConvNetJS is a Javascript library for training deep learning models (neural networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. The library allows you to formulate and solve neural networks in Javascript, and was originally written by @karpathy. However, the library has since been extended by contributions from the community and more are warmly welcome. The fastest way to obtain the library in a plug-and-play way if you don't care about developing is through this link to convnet-min.js, which contains the minified library. Alternatively, you can also choose to download the latest release of the library from Github. The file you are probably most interested in is build/convnet-min.js, which contains the entire library. To use it, create a bare-bones index.html file in some folder and copy build/convnet-min.js to the same folder. -
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Zebra by Mipsology
Mipsology
Zebra by Mipsology is the ideal Deep Learning compute engine for neural network inference. Zebra seamlessly replaces or complements CPUs/GPUs, allowing any neural network to compute faster, with lower power consumption, at a lower cost. Zebra deploys swiftly, seamlessly, and painlessly without knowledge of underlying hardware technology, use of specific compilation tools, or changes to the neural network, the training, the framework, and the application. Zebra computes neural networks at world-class speed, setting a new standard for performance. Zebra runs on highest-throughput boards all the way to the smallest boards. The scaling provides the required throughput, in data centers, at the edge, or in the cloud. Zebra accelerates any neural network, including user-defined neural networks. Zebra processes the same CPU/GPU-based trained neural network with the same accuracy without any change. -
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ThirdAI
ThirdAI
ThirdAI (pronunciation: /THərd ī/ Third eye) is a cutting-edge Artificial intelligence startup carving scalable and sustainable AI. ThirdAI accelerator builds hash-based processing algorithms for training and inference with neural networks. The technology is a result of 10 years of innovation in finding efficient (beyond tensor) mathematics for deep learning. Our algorithmic innovation has demonstrated how we can make Commodity x86 CPUs 15x or faster than most potent NVIDIA GPUs for training large neural networks. The demonstration has shaken the common knowledge prevailing in the AI community that specialized processors like GPUs are significantly superior to CPUs for training neural networks. Our innovation would not only benefit current AI training by shifting to lower-cost CPUs, but it should also allow the “unlocking” of AI training workloads on GPUs that were not previously feasible. -
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NVIDIA Modulus
NVIDIA
NVIDIA Modulus is a neural network framework that blends the power of physics in the form of governing partial differential equations (PDEs) with data to build high-fidelity, parameterized surrogate models with near-real-time latency. Whether you’re looking to get started with AI-driven physics problems or designing digital twin models for complex non-linear, multi-physics systems, NVIDIA Modulus can support your work. Offers building blocks for developing physics machine learning surrogate models that combine both physics and data. The framework is generalizable to different domains and use cases—from engineering simulations to life sciences and from forward simulations to inverse/data assimilation problems. Provides parameterized system representation that solves for multiple scenarios in near real time, letting you train once offline to infer in real time repeatedly. -
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Deeplearning4j
Deeplearning4j
DL4J takes advantage of the latest distributed computing frameworks including Apache Spark and Hadoop to accelerate training. On multi-GPUs, it is equal to Caffe in performance. The libraries are completely open-source, Apache 2.0, and maintained by the developer community and Konduit team. Deeplearning4j is written in Java and is compatible with any JVM language, such as Scala, Clojure, or Kotlin. The underlying computations are written in C, C++, and Cuda. Keras will serve as the Python API. Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs. There are a lot of parameters to adjust when you're training a deep-learning network. We've done our best to explain them, so that Deeplearning4j can serve as a DIY tool for Java, Scala, Clojure, and Kotlin programmers. -
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Latent AI
Latent AI
We take the hard work out of AI processing on the edge. The Latent AI Efficient Inference Platform (LEIP) enables adaptive AI at the edge by optimizing for compute, energy and memory without requiring changes to existing AI/ML infrastructure and frameworks. LEIP is a modular, fully-integrated workflow designed to train, quantize, adapt and deploy edge AI neural networks. LEIP is a modular, fully-integrated workflow designed to train, quantize and deploy edge AI neural networks. Latent AI believes in a vibrant and sustainable future driven by the power of AI and the promise of edge computing. Our mission is to deliver on the vast potential of edge AI with solutions that are efficient, practical, and useful. Latent AI helps a variety of federal and commercial organizations gain the most from their edge AI with an automated edge MLOps pipeline that creates ultra-efficient, compressed, and secured edge models at scale while also removing all maintenance and configuration concerns -
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SHARK
SHARK
SHARK is a fast, modular, feature-rich open-source C++ machine learning library. It provides methods for linear and nonlinear optimization, kernel-based learning algorithms, neural networks, and various other machine learning techniques. It serves as a powerful toolbox for real-world applications as well as research. Shark depends on Boost and CMake. It is compatible with Windows, Solaris, MacOS X, and Linux. Shark is licensed under the permissive GNU Lesser General Public License. Shark provides an excellent trade-off between flexibility and ease-of-use on the one hand, and computational efficiency on the other. Shark offers numerous algorithms from various machine learning and computational intelligence domains in a way that they can be easily combined and extended. Shark comes with a lot of powerful algorithms that are to our best knowledge not implemented in any other library. -
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DeepPy
DeepPy
DeepPy is a MIT licensed deep learning framework. DeepPy tries to add a touch of zen to deep learning as it. DeepPy relies on CUDArray for most of its calculations. Therefore, you must first install CUDArray. Note that you can choose to install CUDArray without the CUDA back-end which simplifies the installation process. -
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Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have contributed to the popularity of deep learning by reducing the effort and skills needed to design, train, and use deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) provides a consistent way to run these deep-learning frameworks as a service on Kubernetes. The FfDL platform uses a microservices architecture to reduce coupling between components, keep each component simple and as stateless as possible, isolate component failures, and allow each component to be developed, tested, deployed, scaled, and upgraded independently. Leveraging the power of Kubernetes, FfDL provides a scalable, resilient, and fault-tolerant deep-learning framework. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes.
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Microsoft Cognitive Toolkit
Microsoft
The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK can be included as a library in your Python, C#, or C++ programs, or used as a standalone machine-learning tool through its own model description language (BrainScript). In addition you can use the CNTK model evaluation functionality from your Java programs. CNTK supports 64-bit Linux or 64-bit Windows operating systems. To install you can either choose pre-compiled binary packages, or compile the toolkit from the source provided in GitHub. -
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YandexART
Yandex
YandexART is a diffusion neural network by Yandex designed for image and video creation. This new neural network ranks as a global leader among generative models in terms of image generation quality. Integrated into Yandex services like Yandex Business and Shedevrum, it generates images and videos using the cascade diffusion method—initially creating images based on requests and progressively enhancing their resolution while infusing them with intricate details. The updated version of this neural network is already operational within the Shedevrum application, enhancing user experiences. YandexART fueling Shedevrum boasts an immense scale, with 5 billion parameters, and underwent training on an extensive dataset comprising 330 million pairs of images and corresponding text descriptions. Through the fusion of a refined dataset, a proprietary text encoder, and reinforcement learning, Shedevrum consistently delivers high-calibre content. -
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Cogniac
Cogniac
Cogniac’s no-code solution enables organizations to capitalize on the latest developments in Artificial Intelligence (AI) and convolutional neural networks to deliver superhuman operational performance. Cogniac’s AI machine vision platform enables enterprise customers to achieve Industry 4.0 standards through visual data management and automation. Cogniac helps organizations’ operations divisions deliver smart continuous improvement. The Cogniac user interface has been designed and built to be operated by a non-technical user. With simplicity at its heart, the drag and drop nature of the Cogniac platform allows subject matter experts to focus on the tasks that drive the most value. Cogniac’s platform can identify defects from as little as 100 labeled images. Once trained by 25 approved and 75 defective images, the Cogniac AI will deliver results that are comparable to a human subject matter expert within hours of set-up. -
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Neuri
Neuri
We conduct and implement cutting-edge research on artificial intelligence to create real advantage in financial investment. Illuminating the financial market with ground-breaking neuro-prediction. We combine novel deep reinforcement learning algorithms and graph-based learning with artificial neural networks for modeling and predicting time series. Neuri strives to generate synthetic data emulating the global financial markets, testing it with complex simulations of trading behavior. We bet on the future of quantum optimization in enabling our simulations to surpass the limits of classical supercomputing. Financial markets are highly fluid, with dynamics evolving over time. As such we build AI algorithms that adapt and learn continuously, in order to uncover the connections between different financial assets, classes and markets. The application of neuroscience-inspired models, quantum algorithms and machine learning to systematic trading at this point is underexplored. -
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NeuroIntelligence
ALYUDA
NeuroIntelligence is a neural networks software application designed to assist neural network, data mining, pattern recognition, and predictive modeling experts in solving real-world problems. NeuroIntelligence features only proven neural network modeling algorithms and neural net techniques; software is fast and easy-to-use. Visualized architecture search, neural network training and testing. Neural network architecture search, fitness bars, network training graphs comparison. Training graphs, dataset error, network error, weights and errors distribution, neural network input importance. Testing, actual vs. output graph, scatter plot, response graph, ROC curve, confusion matrix. The interface of NeuroIntelligence is optimized to solve data mining, forecasting, classification and pattern recognition problems. You can create a better solution much faster using the tool's easy-to-use GUI and unique time-saving capabilities.Starting Price: $497 per user -
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Keras
Keras
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides. Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win. Built on top of TensorFlow 2.0, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform. You can export Keras models to JavaScript to run directly in the browser, to TF Lite to run on iOS, Android, and embedded devices. It's also easy to serve Keras models as via a web API. -
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Caffe
BAIR
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU. -
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Neural Designer
Artelnics
Neural Designer is a powerful software tool for developing and deploying machine learning models. It provides a user-friendly interface that allows users to build, train, and evaluate neural networks without requiring extensive programming knowledge. With a wide range of features and algorithms, Neural Designer simplifies the entire machine learning workflow, from data preprocessing to model optimization. In addition, it supports various data types, including numerical, categorical, and text, making it versatile for domains. Additionally, Neural Designer offers automatic model selection and hyperparameter optimization, enabling users to find the best model for their data with minimal effort. Finally, its intuitive visualizations and comprehensive reports facilitate interpreting and understanding the model's performance.Starting Price: $2495/year (per user) -
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TFLearn
TFLearn
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations and more. The high-level API currently supports most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks. -
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Supervisely
Supervisely
The leading platform for entire computer vision lifecycle. Iterate from image annotation to accurate neural networks 10x faster. With our best-in-class data labeling tools transform your images / videos / 3d point cloud into high-quality training data. Train your models, track experiments, visualize and continuously improve model predictions, build custom solution within the single environment. Our self-hosted solution guaranties data privacy, powerful customization capabilities, and easy integration into your technology stack. A turnkey solution for Computer Vision: multi-format data annotation & management, quality control at scale and neural networks training in end-to-end platform. Inspired by professional video editing software, created by data scientists for data scientists — the most powerful video labeling tool for machine learning and more. -
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Sharky Neural Network
SharkTime Software
Sharky Neural Network is a Windows application providing a visual, interactive introduction to machine learning. This free software serves as a playground for experimenting with neural network classification in real-time. Instead of relying on static charts, Sharky offers a "live view" of the learning process. You can watch the network adjust its classification boundaries like a movie unfolding on your screen. Users can swap architectures and data shapes to see how topology affects results. The app uses the backpropagation algorithm with optional momentum to give you direct control over learning dynamics. Perfect for students and hobbyists, Sharky Neural Network makes hidden layers and data clustering intuitive. It is a lightweight tool that effectively bridges the gap between theory and practice.Starting Price: $0 -
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Build your deep learning project quickly on Google Cloud: Quickly prototype with a portable and consistent environment for developing, testing, and deploying your AI applications with Deep Learning Containers. These Docker images use popular frameworks and are performance optimized, compatibility tested, and ready to deploy. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises. You have the flexibility to deploy on Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm.
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Neuralhub
Neuralhub
Neuralhub is a system that makes working with neural networks easier, helping AI enthusiasts, researchers, and engineers to create, experiment, and innovate in the AI space. Our mission extends beyond providing tools; we're also creating a community, a place to share and work together. We aim to simplify the way we do deep learning today by bringing all the tools, research, and models into a single collaborative space, making AI research, learning, and development more accessible. Build a neural network from scratch or use our library of common network components, layers, architectures, novel research, and pre-trained models to experiment and build something of your own. Construct your neural network with one click. Visually see and interact with every component in the network. Easily tune hyperparameters such as epochs, features, labels and much more. -
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DeePhi Quantization Tool
DeePhi Quantization Tool
This is a model quantization tool for convolution neural networks(CNN). This tool could quantize both weights/biases and activations from 32-bit floating-point (FP32) format to 8-bit integer(INT8) format or any other bit depths. With this tool, you can boost the inference performance and efficiency significantly, while maintaining the accuracy. This tool supports common layer types in neural networks, including convolution, pooling, fully-connected, batch normalization and so on. The quantization tool does not need the retraining of the network or labeled datasets, only one batch of pictures are needed. The process time ranges from a few seconds to several minutes depending on the size of neural network, which makes rapid model update possible. This tool is collaborative optimized for DeePhi DPU and could generate INT8 format model files required by DNNC.Starting Price: $0.90 per hour -
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DeepCube
DeepCube
DeepCube focuses on the research and development of deep learning technologies that result in improved real-world deployment of AI systems. The company’s numerous patented innovations include methods for faster and more accurate training of deep learning models and drastically improved inference performance. DeepCube’s proprietary framework can be deployed on top of any existing hardware in both datacenters and edge devices, resulting in over 10x speed improvement and memory reduction. DeepCube provides the only technology that allows efficient deployment of deep learning models on intelligent edge devices. After the deep learning training phase, the resulting model typically requires huge amounts of processing and consumes lots of memory. Due to the significant amount of memory and processing requirements, today’s deep learning deployments are limited mostly to the cloud. -
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Robot Framework
Robot Framework
Robot Framework is a generic open-source automation framework. It can be used for test automation and robotic process automation (RPA). Robot Framework is supported by Robot Framework Foundation. Many industry-leading companies use the tool in their software development. Robot Framework is open and extensible. Robot Framework can be integrated with virtually any other tool to create powerful and flexible automation solutions. Robot Framework is free to use without licensing costs. Robot Framework has an easy syntax, utilizing human-readable keywords. Its capabilities can be extended by libraries implemented with Python, Java or many other programming languages. Robot Framework has a rich ecosystem around it, consisting of libraries and tools that are developed as separate projects.Starting Price: Free -
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Krayin CRM
Webkul
Krayin CRM is a hand-tailored CRM framework built on some of the hottest open-source technologies such as Laravel (a PHP framework) and Vue.js a progressive Javascript framework. Free & Opensource Laravel CRM solution for SMEs and Enterprises for complete customer lifecycle management. We also have a forum for any type of concerns, feature requests, or discussions. Please visit: Krayin CRM Forums. Features: 1) Descriptive and Simple Admin Panel 2) Built on Modular Approach 3) Email parsing via Sendgrid 4) Krayin CRM is a truly open source CRM framework which will always be free under the MIT License 5) Documentation available 6) Admin Dashboard 7) Create leads through various sources/channels like email marketing, website and social media etc 8) Build an actionable roadmap for amazing custom experience 9) Perform action that will bring status processing into motionStarting Price: $0 -
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NVIDIA DIGITS
NVIDIA DIGITS
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. Interactively train models using TensorFlow and visualize model architecture using TensorBoard. Integrate custom plug-ins for importing special data formats such as DICOM used in medical imaging. -
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Deci
Deci AI
Easily build, optimize, and deploy fast & accurate models with Deci’s deep learning development platform powered by Neural Architecture Search. Instantly achieve accuracy & runtime performance that outperform SoTA models for any use case and inference hardware. Reach production faster with automated tools. No more endless iterations and dozens of different libraries. Enable new use cases on resource-constrained devices or cut up to 80% of your cloud compute costs. Automatically find accurate & fast architectures tailored for your application, hardware and performance targets with Deci’s NAS based AutoNAC engine. Automatically compile and quantize your models using best-of-breed compilers and quickly evaluate different production settings. Automatically compile and quantize your models using best-of-breed compilers and quickly evaluate different production settings. -
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PyTorch
PyTorch
Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies. -
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Predictive Suite
Predictive Dynamix
Automated variable selection identifies key variables & variable interactions. Insightful visualization of data and model dynamics. Execution of batch commands. SQL queries and dataset browsing. Pre & post-processing for creating variables, constraining outputs, etc. Models easily deployed via ActiveX (i.e., OCX) controls or DLLs. Powerful modeling algorithms include regression, neural networks, self-organizing maps, dynamic clustering, decision trees, fuzzy logic, genetic algorithms. Predictive Dynamix provides computational intelligence software for forecasting, predictive modeling, pattern recognition, classification, and optimization applications, across all industries. Modern neural network technology are powerful computational structure for solving difficult problems involving forecasting and pattern recognition. Multi-layer perceptron neural networks have an architecture that allows multiple coefficients per input variable. -
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Neuton AutoML
Neuton.AI
Neuton, a no-code AutoML solution, makes Machine Learning available to everyone. Explore data insights and make predictions leveraging Automated Artificial Intelligence. • NO coding • NO need for technical skills • NO need for data science knowledge Neuton provides comprehensive Explainability Office©, a unique set of tools that allow users to evaluate model quality at every stage, identify the logic behind the model analysis, understand why certain predictions have been made. • Exploratory Data Analysis • Feature Importance Matrix with class granularity • Model Interpreter • Feature Influence Matrix • Model-to-Data Relevance Indicators historical and for every prediction • Model Quality Index • Confidence Interval • Extensive list of supported metrics with Radar Diagram Neuton enables users to implement ML in days instead of months.Starting Price: $0 -
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Synaptic
Synaptic
Neurons are the basic unit of the neural network. They can be connected to another neuron or gate connections between other neurons. This allows you to create complex and flexible architectures. Trainers can take any given network regardless of its architecture and use any training set. It includes built-in tasks to test networks, like learning an XOR, completing a Discrete Sequence Recall task or an Embeded Reber Grammar test. Networks can be imported/exported to JSON, converted to workers or standalone functions. They can be connected to other networks or gate connections. The Architect includes built-in useful architectures such as multilayer perceptrons, multilayer long short-term memory networks (LSTM), liquid state machines and Hopfield networks. Networks can also be optimized, extended, exported to JSON, converted to Workers or standalone Functions, and cloned. A network can project a connection to another, or gate a connection between two others networks. -
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DataMelt
jWork.ORG
DataMelt (or "DMelt") is an environment for numeric computation, data analysis, data mining, computational statistics, and data visualization. DataMelt can be used to plot functions and data in 2D and 3D, perform statistical tests, data mining, numeric computations, function minimization, linear algebra, solving systems of linear and differential equations. Linear, non-linear and symbolic regression are also available. Neural networks and various data-manipulation methods are integrated using Java API. Elements of symbolic computations using Octave/Matlab scripting are supported. DataMelt is a computational environment for Java platform. It can be used with different programming languages on different operating systems. Unlike other statistical programs, it is not limited to a single programming language. This software combines the world's most-popular enterprise language, Java, with the most popular scripting language used in data science, such as Jython (Python), Groovy, JRuby.Starting Price: $0 -
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GPT-4o
OpenAI
GPT-4o (“o” for “omni”) is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, image, and video and generates any combination of text, audio, and image outputs. It can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time (opens in a new window) in a conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models.Starting Price: $5.00 / 1M tokens -
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MXNet
The Apache Software Foundation
A hybrid front-end seamlessly transitions between Gluon eager imperative mode and symbolic mode to provide both flexibility and speed. Scalable distributed training and performance optimization in research and production is enabled by the dual parameter server and Horovod support. Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl. A thriving ecosystem of tools and libraries extends MXNet and enables use-cases in computer vision, NLP, time series and more. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision-making process have stabilized in a manner consistent with other successful ASF projects. Join the MXNet scientific community to contribute, learn, and get answers to your questions. -
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Framework7
Framework7
Framework7 is a free and open-source framework to develop mobile, desktop, or web apps with a native look and feel. It is also an indispensable prototyping tool to show a working app prototype as soon as possible in case you need to. With such a stunning set of UI components that Framework7 provides right from the box, it allows to create web apps, progressive web apps (PWA), and iOS and Android apps with a native look and feel. Framework7 paired with extra tools like Electron and NW.js allows to build native desktop apps. You can use any tools you love when working with Framework7. It doesn’t force you to use anything except plain HTML, CSS, and JavaScript. Creating apps with Framework7 is easy as creating a website. And in addition to vanilla JavaScript library, Framework7 comes with Vue.js, React, and Svelte components to bring components-syntax, structured data, and data bindings with the power and simplicity of Vue.js, React, or Svelte.Starting Price: Free -
42
HtmlUnit
HtmlUnit
HtmlUnit is a "GUI-Less browser for Java programs" that models HTML documents and provides an API to interact with web pages, such as invoking pages, filling out forms, and clicking links, similar to a standard web browser. It offers fairly good JavaScript support, which is constantly improving and is capable of handling complex AJAX libraries, simulating browsers like Chrome, Firefox, or Edge depending on the configuration used. Typically used for testing purposes or retrieving information from websites, HtmlUnit is not a generic unit testing framework but is intended to simulate a browser within another testing framework such as JUnit or TestNG. It is utilized as the underlying "browser" by various open source tools like WebDriver, Arquillian Drone, and Serenity BDD, and is employed by many projects for automated web testing, including Apache Shiro, Apache Struts, and Quarkus.Starting Price: Free -
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SimpleCV
SimpleCV
SimpleCV is an open-source framework for building computer vision applications. With it, you get access to several high-powered computer vision libraries such as OpenCV, without having to first learn about bit depths, file formats, color spaces, buffer management, eigenvalues, or matrix versus bitmap storage. This is computer vision made easy. These are just a small number of things you can do with SimpleCV. If you would like to learn more please refer to our tutorial. There are also many examples included in the SimpleCV directory under the examples folder which can also be downloaded from here. SimpleCV is an open-source framework, meaning that it is a collection of libraries and software that you can use to develop vision applications. It lets you work with the images or video streams that come from webcams, Kinects, FireWire and IP cameras, or mobile phones. It helps you build software to make your various technologies not only see the world but understand it too. -
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NVIDIA Isaac Lab
NVIDIA
NVIDIA Isaac Lab is a GPU‑accelerated, open source robot learning framework built on top of Isaac Sim, designed to unify and simplify robotics research workflows such as reinforcement learning, imitation learning, and motion planning. It leverages realistic sensor and physics simulation to support accurate training of embodied agents, providing ready‑to‑use environments, spanning manipulators, quadrupeds, and humanoids—with support for 30+ benchmark tasks and integration with popular RL libraries like RL Games, Stable Baselines, RSL RL, and SKRL. Isaac Lab features a modular, configuration‑driven design that enables developers to easily create, modify, and scale learning environments; it also supports collecting demonstrations via peripherals (gamepads, keyboards) and allows custom actuator models to facilitate sim‑to‑real transfer. The framework is built for both local and cloud deployment, accommodating flexible scaling of compute resources.Starting Price: Free -
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DroidAR
DroidAR
DroidAR is a framework for Augmented Reality on Android. Location based AR and marker based AR are both possible. This framework can be used for many different scenarios, there are already some demo applications available (check the videos below). For the latest news about DroidAR also visit our blog. DroidAR can be freely downloaded and used under the GNU GPL v3 license from this GitHub repository and is also available under a commercial license. Since the initial creation of the prototype of an augmented reality development tool in 2010, it was continuously improved and new features were added. The created development tool (SDK) enables other programmers to integrate augmented reality in their own applications. We provided the SDK without charge with an Open Source License for non-commercial projects. It has since evolved into one of the most used open source augmented reality SDKs for the Android platform. -
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Automaton AI
Automaton AI
With Automaton AI’s ADVIT, create, manage and develop high-quality training data and DNN models all in one place. Optimize the data automatically and prepare it for each phase of the computer vision pipeline. Automate the data labeling processes and streamline data pipelines in-house. Manage the structured and unstructured video/image/text datasets in runtime and perform automatic functions that refine your data in preparation for each step of the deep learning pipeline. Upon accurate data labeling and QA, you can train your own model. DNN training needs hyperparameter tuning like batch size, learning, rate, etc. Optimize and transfer learning on trained models to increase accuracy. Post-training, take the model to production. ADVIT also does model versioning. Model development and accuracy parameters can be tracked in run-time. Increase the model accuracy with a pre-trained DNN model for auto-labeling. -
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ChatGPT Plus
OpenAI
We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response. ChatGPT Plus is a subscription plan for ChatGPT a conversational AI. ChatGPT Plus costs $20/month, and subscribers will receive a number of benefits: - General access to ChatGPT, even during peak times - Faster response times - GPT-4 access - ChatGPT plugins - Web-browsing with ChatGPT - Priority access to new features and improvements ChatGPT Plus is available to customers in the United States, and we will begin the process of inviting people from our waitlist over the coming weeks. We plan to expand access and support to additional countries and regions soon.Starting Price: $20 per month -
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Connecting Steps
BSquared
Connecting Steps is our inclusive assessment software that can be used with pupils working at Age-Related Expectations (ARE). Connecting Steps assessment software makes teachers’ lives easier and saves them time. We have multiple frameworks for different ages and different abilities. One system for all pupils. Connecting Steps also supports meaningful communication with parents, giving clear information about progress and attainment. Connecting Steps is our market-leading pupils tracking software that makes showing progress for all pupils easy. Connecting Steps is widely recommended by advisors and local authorities and receives great feedback from inspectors. Over the last 25 years, we have constantly updated our assessment frameworks, our assessment software and released new products. Connecting Steps V5, our latest version is designed to be faster, simpler, and easier. At the same time, it offers even more ways to show progress, so that it can capture all progress.Starting Price: $3.92 per student per year -
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Webix
Webix
JavaScript UI library and framework for speeding up web development. JS Framework for cross-platform web Apps development 102 UI widgets and feature-rich CSS / HTML5 JavaScript controls. Save at least 3000+ development hours by using ready-made widgets and UI controls. Develop Web UI 30% faster. We have accumulated the best design ideas. We have meticulously considered UX of each Webix component for five conceptual designs. Our support service specialists know everything about our library and can help you solve any problem. We also have an official support forum where you can discuss issues with our developers. By using Webix JS framework you receive an elegant and lightweight code based on object-oriented programming concepts. Associate your project with jQuery JavaScript library, MVC frameworks AngularJS, React, Vue.js, Backbone.js, third party UI extensions.Starting Price: $848 -
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GPT-4 Turbo
OpenAI
GPT-4 is a large multimodal model (accepting text or image inputs and outputting text) that can solve difficult problems with greater accuracy than any of our previous models, thanks to its broader general knowledge and advanced reasoning capabilities. GPT-4 is available in the OpenAI API to paying customers. Like gpt-3.5-turbo, GPT-4 is optimized for chat but works well for traditional completions tasks using the Chat Completions API. GPT-4 is the latest GPT-4 model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Returns a maximum of 4,096 output tokens. This preview model is not yet suited for production traffic.Starting Price: $0.0200 per 1000 tokens
