Best Neural Network Software of 2024

NVIDIA Modulus, a neural network framework, combines the power of Physics in the form of governing partial differential equations (PDEs), with data to create high-fidelity surrogate models with near real-time latency. NVIDIA Modulus is a tool that can help you solve complex, nonlinear, multiphysics problems using AI. This tool provides the foundation for building physics machine learning surrogate models that combine physics and data. This framework can be applied to many domains and uses, including engineering simulations and life sciences. It can also be used to solve forward and inverse/data assimilation issues. Parameterized system representation that solves multiple scenarios in near real-time, allowing you to train once offline and infer in real-time repeatedly.

ThirdAI (pronunciation is /TH@rdi/ Third eye), is an Artificial Intelligence startup that specializes in scalable and sustainable AI. ThirdAI accelerator develops hash-based processing algorithms to train and infer with neural networks. This technology is the result of 10 years' worth of innovation in deep learning mathematics. Our algorithmic innovation has shown that Commodity x86 CPUs can be made 15x faster than the most powerful NVIDIA GPUs to train large neural networks. This demonstration has reaffirmed the belief that GPUs are superior to CPUs when it comes to training neural networks. Our innovation will not only benefit AI training currently by switching to cheaper CPUs but also allow for the "unlocking” of AI training workloads on GPUs previously not possible.

NVIDIA DeepLearning GPU Training System (DIGITS), puts deep learning in the hands of data scientists and engineers. DIGITS is a fast and accurate way to train deep neural networks (DNNs), for image classification, segmentation, and object detection tasks. DIGITS makes it easy to manage data, train neural networks on multi-GPU platforms, monitor performance with advanced visualizations and select the best model from the results browser for deployment. DIGITS is interactive, so data scientists can concentrate on designing and training networks and not programming and debugging. TensorFlow allows you to interactively train models and TensorBoard lets you visualize the model architecture. Integrate custom plugs to import special data formats, such as DICOM, used in medical imaging.

TFlearn, a modular and transparent deep-learning library built on top Tensorflow, is modular and transparent. It is a higher-level API for TensorFlow that allows experimentation to be accelerated and facilitated. However, it is fully compatible and transparent with TensorFlow. It is an easy-to-understand, high-level API to implement deep neural networks. There are tutorials and examples. Rapid prototyping with highly modular built-in neural networks layers, regularizers and optimizers. Tensorflow offers full transparency. All functions can be used without TFLearn and are built over Tensors. You can use these powerful helper functions to train any TensorFlow diagram. They are compatible with multiple inputs, outputs and optimizers. A beautiful graph visualization with details about weights and gradients, activations, and more. The API supports most of the latest deep learning models such as Convolutions and LSTM, BiRNN. BatchNorm, PReLU. Residual networks, Generate networks.

Torch is a scientific computing platform that supports machine learning algorithms and has wide support for them. It is simple to use and efficient thanks to a fast scripting language, LuaJIT and an underlying C/CUDA implementation. Torch's goal is to allow you maximum flexibility and speed when building your scientific algorithms, while keeping it simple. Torch includes a large number of community-driven packages for machine learning, signal processing and parallel processing. It also builds on the Lua community. The core of Torch is the popular optimization and neural network libraries. These libraries are easy to use while allowing for maximum flexibility when implementing complex neural networks topologies. You can create arbitrary graphs of neuro networks and parallelize them over CPUs or GPUs in an efficient way.

A powerful, flexible, intuitive framework for neural networks. Chainer supports CUDA computation. To leverage a GPU, it only takes a few lines. It can also be used on multiple GPUs without much effort. Chainer supports a variety of network architectures, including convnets, feed-forward nets, and recurrent nets. It also supports per batch architectures. Forward computation can include any control flow statement of Python without sacrificing the ability to backpropagate. It makes code easy to understand and debug. ChainerRLA is a library that implements several state-of-the art deep reinforcement algorithms. ChainerCVA is a collection that allows you to train and run neural network for computer vision tasks. Chainer supports CUDA computation. To leverage a GPU, it only takes a few lines. It can also be run on multiple GPUs without much effort.

ConvNetJS is a Javascript library that allows you to train deep learning models (neural network) in your browser. You can train by simply opening a tab. No software requirements, no compilers, no installations, no GPUs, no sweat. The library was originally created by @karpathy and allows you to create and solve neural networks using Javascript. The library has been greatly expanded by the community, and new contributions are welcome. If you don't want to develop, this link to convnet.min.js will allow you to download the library as a plug-and play. You can also download the latest version 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 an index.html file with no content and copy build/convnet.min.js to that folder.

SHARK is an open-source C++ machine-learning library that is fast, modular, and feature-rich. It offers methods for linear and unlinear optimization, kernel-based algorithms, neural networks, as well as other machine learning techniques. It is a powerful toolbox that can be used in real-world applications and research. Shark relies on Boost, CMake. It is compatible with Windows and Solaris, MacOS X and Linux. Shark is licensed under the permissive GNU Lesser General Public License. Shark offers a great compromise between flexibility and ease of use and computational efficiency. Shark provides many algorithms from different domains of machine learning and computational intelligence that can be combined and extended easily. Shark contains many powerful algorithms that, to our best knowledge, are not available in any other library.

Darknet is an open-source framework for neural networks written in C and CUDA. It is easy to install and supports both CPU and GPU computation. The source code can be found on GitHub. You can also read more about Darknet's capabilities. Darknet is easy-to-install with only two dependencies: OpenCV if your preference is for a wider range of image types and CUDA if your preference is for GPU computation. Darknet is fast on the CPU, but it's about 500 times faster on the GPU. You will need an Nvidia GPU, and you'll need to install CUDA. Darknet defaults to using stb_image.h to load images. OpenCV is a better alternative to Darknet. It supports more formats, such as CMYK jpegs. Thanks to Obama! OpenCV allows you to view images, and detects without saving them to disk. You can classify images using popular models such as ResNet and ResNeXt. For NLP and time-series data, recurrent neural networks are a hot trend.

DL4J makes use of the most recent distributed computing frameworks, including Apache Spark and Hadoop, to accelerate training. It performs almost as well as Caffe on multi-GPUs. The libraries are open-source Apache 2.0 and maintained by Konduit and the developer community. Deeplearning4j is written entirely in Java and compatible with any JVM language like Scala, Clojure or Kotlin. The underlying computations are written using C, C++, or Cuda. Keras will be the Python API. Eclipse Deeplearning4j, a commercial-grade, open source, distributed deep-learning library, is available for Java and Scala. DL4J integrates with Apache Spark and Hadoop to bring AI to business environments. It can be used on distributed GPUs or CPUs. When training a deep-learning network, there are many parameters you need to adjust. We have tried to explain them so that Deeplearning4j can be used as a DIY tool by Java, Scala and Clojure programmers.

Deep learning frameworks like TensorFlow and PyTorch, Torch and Torch, Theano and MXNet have helped to increase the popularity of deep-learning by reducing the time and skills required to design, train and use deep learning models. Fabric for Deep Learning (pronounced "fiddle") is a consistent way of running these deep-learning frameworks on Kubernetes. FfDL uses microservices architecture to reduce the coupling between components. It isolates component failures and keeps each component as simple and stateless as possible. Each component can be developed, tested and deployed independently. FfDL leverages the power of Kubernetes to provide a resilient, scalable and fault-tolerant deep learning framework. The platform employs a distribution and orchestration layer to allow for learning from large amounts of data in a reasonable time across multiple compute nodes.

Mipsology's Zebra is the ideal Deep Learning compute platform for neural network inference. Zebra seamlessly replaces or supplements CPUs/GPUs, allowing any type of neural network to compute more quickly, with lower power consumption and at a lower price. Zebra deploys quickly, seamlessly, without any knowledge of the underlying hardware technology, use specific compilation tools, or modifications to the neural network training, framework, or application. Zebra computes neural network at world-class speeds, setting a new standard in performance. Zebra can run on the highest throughput boards, all the way down to the smallest boards. The scaling allows for the required throughput in data centers, at edge or in the cloud. Zebra can accelerate any neural network, even user-defined ones. Zebra can process the same CPU/GPU-based neural network with the exact same accuracy and without any changes.

The hybrid front-end seamlessly switches between Gluon eager symbolic mode and Gluon imperative mode, providing flexibility and speed. The dual parameter server and Horovod support enable scaleable distributed training and performance optimization for research and production. Deep integration into Python, support for Scala and Julia, Clojure and Java, C++ and R. MXNet is supported by a wide range of tools and libraries that allow for use-cases in NLP, computer vision, time series, and other areas. Apache MXNet is an Apache Software Foundation (ASF) initiative currently incubating. It is sponsored by the Apache Incubator. All accepted projects must be incubated until further review determines that infrastructure, communications, decision-making, and decision-making processes have stabilized in a way consistent with other successful ASF projects. Join the MXNet scientific network to share, learn, and receive answers to your questions.

We conduct cutting-edge research in artificial intelligence and implement it to give financial investors an advantage. Transforming the financial market through groundbreaking neuro-prediction. Our algorithms combine graph-based learning and deep reinforcement learning algorithms to model and predict time series. Neuri aims to generate synthetic data that mimics the global financial markets and test it with complex simulations. Quantum optimization is the future of supercomputing. Our simulations will be able to exceed the limits of classical supercomputing. Financial markets are dynamic and change over time. We develop AI algorithms that learn and adapt continuously to discover 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.

The basic unit of the neural system is the neuron. They can be connected to other neurons or gate connections between neurons. This allows you to create flexible and complex architectures. Trainers can use any training set and take any network, regardless of its architecture. It also includes tasks to test networks such as learning an XOR or completing a Discrete Sequence Recall task. You can import/export networks to JSON, convert them to workers, or use standalone functions. They can be connected with other networks or gate connections. The Architect has built-in useful architectures like multilayer perceptrons and multilayer long-term memory networks (LSTM), liquid states machines, and Hopfield networks. You can also optimize, extend, export to JSON, convert to Workers or standalone Functions, and even clone networks. A network can be used to project a connection to another or to gate a connection between two networks.

Automaton AI's Automaton AI's DNN model and training data management tool, ADVIT, allows you to create, manage, and maintain high-quality models and training data in one place. Automated optimization of data and preparation for each stage of the computer vision pipeline. Automate data labeling and streamline data pipelines in house Automate the management of structured and unstructured video/image/text data and perform automated functions to refine your data before each step in the deep learning pipeline. You can train your own model with accurate data labeling and quality assurance. DNN training requires hyperparameter tuning such as batch size, learning rate, and so on. To improve accuracy, optimize and transfer the learning from trained models. After training, the model can be put into production. ADVIT also does model versioning. Run-time can track model development and accuracy parameters. A pre-trained DNN model can be used to increase the accuracy of your model for auto-labeling.

DeepPy is a MIT licensed deep-learning framework. DeepPy is an attempt to bring a little zen to deep-learning. DeepPy uses CUDArray to perform most of its calculations. You must first install CUDArray. You can install CUDArray without the CUDA Back-end, which simplifies the installation process.

Cogniac's no code solution allows organizations to take advantage of the latest developments in Artificial Intelligence and convolutional neural network technology to deliver extraordinary operational performance. Cogniac's AI platform for machine vision enables enterprises to reach Industry 4.0 standards via visual data management and automated automation. Cogniac helps organizations' operations divisions deliver smart continuous improvement. Cogniac's user interface was designed to be used by non-technical users. The Cogniac platform's drag-and-drop nature allows subject matter experts and other specialists to concentrate on the tasks that are most important. Cogniac can detect defects in as few as 100 images. After being trained with 25 approved images and 75 deficient images, Cogniac AI can deliver results comparable to human subject matter experts within hours.

AForge.NET is an open-source C# framework for researchers and developers in the fields of Computer Vision, Artificial Intelligence - image processors, neural networks, genetic algorithms and fuzzy logic, as well as machine learning and robotics. The framework's development is ongoing, which means that new features and namespaces are being added constantly. You can track the source repository's log to keep track of its progress or visit the project discussion group to receive the most recent information. The framework comes with many examples of applications that demonstrate how to use it, as well as different libraries and their source.

Fido is an open-source, lightweight, modular C++ machine-learning library. The library is geared towards embedded electronics and robotics. Fido contains implementations of reinforcement learning methods, genetic algorithms and trainable neural networks. It also includes a full-fledged robot simulator. Fido also includes a human-trainable robot controller system, as described by Truell and Gruenstein. Although the simulator is not available in the latest release, it can still be downloaded to experiment on the simulator branch.

The Accord.NET Framework combines a.NET machine-learning framework with audio and image processing library completely written in C#. It provides a complete framework to build production-grade computer vision, signal processing, and statistics applications, even for commercial use. The extensive set of sample applications provides a quick start for getting up and running quickly. A detailed documentation and wiki help fill in the details.

We take the hard work out of AI processing on the edge. The Latent AI Efficient Inference Platform (LEIP) enables adaptive AI at edge by optimizing compute, energy, and memory without requiring modifications to existing AI/ML infrastructure or frameworks. LEIP is a fully-integrated modular workflow that can be used to build, quantify, and deploy edge AI neural network. Latent AI believes in a vibrant and sustainable future driven by the power of AI. Our mission is to enable the vast potential of AI that is efficient, practical and useful. We reduce the time to market with a Robust, Repeatable, and Reproducible workflow for edge AI. We help companies transform into an AI factory to make better products and services.

We have developed and are open-sourcing Whisper, a neural network that approximates human-level robustness in English speech recognition. Whisper is an automated speech recognition (ASR), system that was trained using 680,000 hours of multilingual, multitask supervised data from the internet. The use of such a diverse dataset results in a better resistance to accents, background noise, technical language, and other linguistic issues. It also allows transcription in multiple languages and translation from these languages into English. We provide inference code and open-sourcing models to help you build useful applications and further research on robust speech processing. The Whisper architecture is an end-to-end, simple approach that can be used as an encoder/decoder Transformer. The input audio is divided into 30-second chunks and converted into a log Mel spectrogram. This then goes into an encoder.

ChatGPT Enterprise is the most powerful version yet, with enterprise-grade security and privacy. 1. Training models do not use customer prompts or data 2. Data encryption in transit and at rest (TLS 1.2+). 3. SOC 2 compliant 4. Easy bulk member management and dedicated admin console 5. SSO and Domain Verification 6. Use the analytics dashboard to understand usage 7. Access to GPT-4 Advanced Data Analysis and GPT-4 at high speed is unlimited 8. 32k token context window for 4X longer inputs, memory and inputs 9. Shareable chat templates to help your company collaborate

Neuralhub is an AI system that simplifies the creation, experimentation, and innovation of neural networks. It helps AI enthusiasts, researchers, engineers, and other AI professionals. Our mission goes beyond just providing tools. We're creating a community where people can share and collaborate. We want to simplify deep learning by bringing together all the tools, models, and research into a collaborative space. This will make AI research, development, and learning more accessible. Create a neural network by starting from scratch, or use our library to experiment and create something new. Construct your neural networks with just one click. Visualize and interact with each component of the network. Tune hyperparameters like epochs and features, labels, and more.