Alternatives to Image Memorability

Qloo, the "Cultural AI", is capable of decoding and forecasting consumer tastes around the world. Privacy-first API that predicts global consumer preferences, catalogs hundreds of million of cultural entities, and is privacy-first. Our API provides contextualized personalization and insight based on deep understanding of consumer behavior. We have access to more than 575,000,000 people, places, and things. Our technology allows you to see beyond trends and discover the connections that underlie people's tastes in their world. Our vast library includes entities such as brands, music, film and fashion. We also have information about notable people. Results are delivered in milliseconds. They can be weighted with factors like regionalization and real time popularity. Companies who want to use best-in-class data to enhance their customer experiences. Our flagship recommendation API provides results based on demographics and preferences, cultural entities, metadata, geolocational factors, and metadata.

Reliable and adaptable computer vision systems that can be used in any factory. Every step of manufacturing is integrated with AI and image capture. Overview's inspection systems use deep learning technology, which allows us find errors more consistently in a wider range of situations. Remote access and support for enhanced traceability. Our solutions provide a visual record that can be traced back to every unit. It is easy to identify the root cause for production problems or quality issues. Overview can help you eliminate waste from your manufacturing operations, whether you're digitizing your inspections or have an underperforming vision system. See how the Snap platform can improve your factory efficiency. Deep learning automated inspection solutions dramatically improve defect detection. Superior yields, improved traceability, easier setup, and outstanding support.

Amazon Rekognition allows you to easily add image and video analysis into your applications using proven, highly-scalable, deep learning technology that does not require any machine learning expertise. Amazon Rekognition allows you to identify objects, people and text in images and videos. It also detects inappropriate content. Amazon Rekognition can also be used to perform facial analysis and facial searches. This is useful for many purposes, including user verification, people counting, public safety, and other uses. Amazon Rekognition Custom Labels allow you to identify objects and scenes in images that meet your business requirements. You can create a model to help you classify machine parts or detect plants that are sick. Amazon Rekognition Custom Labels does all the heavy lifting for you.

Infosys Nia™, an enterprise-grade AI platform, simplifies the AI adoption process for IT and Business. Infosys Nia supports the entire enterprise AI journey, from data management, digitization and image capture, model development, and operationalization. Nia's modular, scalable and advanced capabilities meet enterprise needs. Nia Data provides highly efficient tools and frameworks to support further ML experimentation using the Nia AML Workbench. The Nia DocAI platform automates all aspects of document processing, from ingestion to consumption. It uses AI capabilities like InfoExtractor and NLP, cognitive search, and computer vision.

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.

Autogon is an AI and machine-learning company that simplifies complex technologies to empower businesses and provide them with cutting-edge, accessible solutions for data-driven decision-making and global competitiveness. Discover the potential of Autogon's models to empower industries and harness the power of AI. They can foster innovation and drive growth in diverse sectors. Autogon Qore is your all-in one solution for image classification and text generation, visual Q&As, sentiment analysis, voice-cloning and more. Innovative AI capabilities will empower your business. You can make informed decisions, streamline your operations and drive growth with minimal technical expertise. Empower engineers, analysts and scientists to harness artificial intelligence and machine-learning for their projects and researchers. Create custom software with clear APIs and integrations SDKs.

The VLFeat open-source library implements popular computer visual algorithms, specializing in image comprehension and local features extraction and match. There are many algorithms available, including VLAD, Fisher Vector, SIFT and MSER, k–means, hierarchical K-means and agglomerative Information Bottleneck, SLIC Superpixels, quick shift Superpixels, large-scale SVM training, and many more. It is written in C to ensure efficiency and compatibility. There are interfaces in MATLAB that make it easy to use and detailed documentation. It is compatible with Windows, Mac OS X, Linux, and other platforms. MatConvNet is a MATLAB Toolbox that implements Convolutional Neural Networks for computer vision applications. It is easy to use, efficient, and can learn and run state-of the-art CNNs. There are many pre-trained CNNs available for image classification, segmentation and face recognition.

Clarifai is a leading AI platform for modeling image, video, text and audio data at scale. Our platform combines computer vision, natural language processing and audio recognition as building blocks for building better, faster and stronger AI. We help enterprises and public sector organizations transform their data into actionable insights. Our technology is used across many industries including Defense, Retail, Manufacturing, Media and Entertainment, and more. We help our customers create innovative AI solutions for visual search, content moderation, aerial surveillance, visual inspection, intelligent document analysis, and more. Founded in 2013 by Matt Zeiler, Ph.D., Clarifai has been a market leader in computer vision AI since winning the top five places in image classification at the 2013 ImageNet Challenge. Clarifai is headquartered in Delaware

The ABEJA platform, an innovative AI platform, consists of cutting-edge AI technologies such as IoT and Big Data. The 2013 data circulation was 4.4 zettabytes. By 2020, the data circulation will be 44 zettabytes. How can we gather and use the diverse data sets? How can we extract new value from the data? ABEJA Platform, the world's most advanced AI platform technology allows for the use of all types of data and tackles technological problems that will only get more complex and serious in the future. Deep Learning is used to provide high-level image analysis functions. Advanced decentralized processing speeds up large-scale data processing. Deep Learning and Machine Learning are used to analyze accumulated data. API allows you to easily output analysis results at any system.

The GPUs are fast at transferring data, but they have very limited locality of reference due to their small caches. They are designed to apply a lot compute to little data, and not a lot compute to a lot data. They are designed to run full layers of computation in order to fully fill their computational pipeline. (See Figure 1 below). Because large models have small memory sizes (tens to gigabytes), GPUs are placed together and models are distributed across them. This creates a complicated and painful software stack. It also requires synchronization and communication between multiple machines. The CPUs on the other side have much larger caches than GPUs and a lot of memory (terabytes). A typical CPU server may have memory equivalent to hundreds or even tens of GPUs. The CPU is ideal for a brain-like ML environment in which pieces of a large network are executed as needed.

Alfi, Inc. engages in creating interactive digital out-of-home advertising experiences. Alfi uses artificial intelligence and computer vision in order to better serve ads. Alfi's Ai algorithm, which is proprietary to the company, can detect subtle facial cues and perceptual details in order to determine if potential customers are a good candidate for a product. The automation is completely anonymous and does not track, store cookies or use identifiable personal information. Ad agencies can access real-time analytics data, including interactive experiences, engagement, sentiment and click-through rates that are otherwise unavailable for out-of-home advertisers. Alfi, powered AI and machine learning, collects data that allows for better analytics and relevant content to improve the consumer experience.

Mintrics is the ultimate social media analytics dashboard with market and competitor intelligence. It allows brands, agencies, content creators, and marketers to see which videos are performing well and which aren’t and why. Mintrics allows you to analyze all your videos on YouTube and Facebook in one place. It connects to various APIs using users' tokens to collect data that isn't available publicly. It runs all calculations and displays unique metrics with historical information. Mintrics provides benchmarks, monthly reports and personalized recommendations, as metrics can be useless by themselves. First, at a page/channel-level to clearly show how a video is performing against others. Then, industry benchmarks that show performance compared to the competition. Mintrics Live Leaderboard allows you to track and group your competitors, as well as view market insights.

AI can be slow, confusing and expensive. Brain empowers wealth managers with hyper-personalized insights quickly and easily. Brain helps you serve your clients better and make your clients smarter. In just a few clicks, turn your data into actionable insights to guide your next best move. Brain's AI explains every recommendation to advisors. Brain's CX app can be integrated on your CX platform or cloud provider. You can increase your revenue by anticipating which clients will be most responsive to cross-sell or upsell opportunities. You can improve the performance of your campaigns by identifying potential clients that might be interested in a product. You can retain your clients by quickly identifying who is most likely and why. Brain's AI-based explanations make hyper-personalized insights more understandable and easier to use by advisors. Brain automates the creation of and maintenance insights, which saves you time and money.

You can quickly provision a VM with everything you need for your deep learning project on Google Cloud. Deep Learning VM Image makes it simple and quick to create a VM image containing all the most popular AI frameworks for a Google Compute Engine instance. Compute Engine instances can be launched pre-installed in TensorFlow and PyTorch. Cloud GPU and Cloud TPU support can be easily added. Deep Learning VM Image supports all the most popular and current machine learning frameworks like TensorFlow, PyTorch, and more. Deep Learning VM Images can be used to accelerate model training and deployment. They are optimized with the most recent NVIDIA®, CUDA-X AI drivers and libraries, and the Intel®, Math Kernel Library. All the necessary frameworks, libraries and drivers are pre-installed, tested and approved for compatibility. Deep Learning VM Image provides seamless notebook experience with integrated JupyterLab support.

Build and deploy deep-learning models for custom use scenarios. Our automated machine-learning process allows customers create powerful AI solutions based on our best-in class models and tailored to their specific challenges. Digital platforms can quickly create custom models that fit their guidelines and requirements. Build large language models to support specialized use cases, such as bots for customer and technical service. Create image classification models for better understanding image libraries, including search, organization and more.

A class-agnostic, pixel-perfect automated annotation platform. Built for teams that have a lot of data and strict quality requirements but little time. Ground truth creation can be scaled up 10x. Collaborate with unlimited team members, annotators and seamlessly integrate into your deep learning pipeline. Create ground truth 10x faster with pixel-perfect annotations. Use V7's intuitive tools for labeling data and automating your ML pipelines. The ultimate image and Video Annotation Solution

Your deep learning workload can be accelerated. AI model training and inference can speed up your time to value. Deep learning is becoming more popular as enterprises adopt it to gain and scale insight through speech recognition and natural language processing. Deep learning can read text, images and video at scale and generate patterns for recommendation engines. It can also model financial risk and detect anomalies. Due to the sheer number of layers and volumes of data required to train neural networks, it has been necessary to use high computational power. Businesses are finding it difficult to demonstrate results from deep learning experiments that were implemented in silos.

Caffe is a deep-learning framework that focuses on expression, speed and modularity. It was developed by Berkeley AI Research (BAIR), and community contributors. The project was created by Yangqing Jia during his PhD at UC Berkeley. Caffe is available under the BSD 2-Clause License. Check out our web image classification demo! Expressive architecture encourages innovation and application. Configuration is all that is required to define models and optimize them. You can switch between CPU and GPU by setting one flag to train on a GPU, then deploy to commodity clusters of mobile devices. Extensible code fosters active development. Caffe was forked by more than 1,000 developers in its first year. Many significant changes were also made back. These contributors helped to track the state of the art in code and models. Caffe's speed makes it ideal for industry deployment and research experiments. Caffe can process more than 60M images per hour using a single NVIDIA GPU K40.

DATAGYM allows data scientists and machine-learning experts to label images up 10x faster than before. AI-assisted annotators reduce manual labeling, give you more time for fine tuning ML models, and speed up your product launch. Reduce data preparation time by up to half and accelerate your computer vision projects

Public video data collection is becoming more risky due to the increasing capabilities of facial recognition technology. Brighter AI's Precision Blur allows for the most precise face redaction in the world. Deep Natural Anonymization, a privacy solution that uses generative AI, is unique. It creates synthetic facial overlays to protect individuals against recognition while maintaining data quality for machine-learning. You can use the Selective Redaction user interface to anonymize specific information in videos. Some use cases, such as media or law enforcement, do not require all faces to be blurred. After the automatic detections, it is possible to (de)select individual objects. Our Analytics Endpoint provides relevant metadata such as the original objects' bounding box locations, facial landmarks, and person attributes. You can retrieve relevant information using JSON outputs, while also having compliant, anonymized photos or videos.

Google Cloud allows you to quickly build your deep learning project. You can quickly prototype your AI applications using Deep Learning Containers. These Docker images are compatible with popular frameworks, optimized for performance, and ready to be deployed. Deep Learning Containers create a consistent environment across Google Cloud Services, making it easy for you to scale in the cloud and shift from on-premises. You can deploy on Google Kubernetes Engine, AI Platform, Cloud Run and Compute Engine as well as Docker Swarm and Kubernetes Engine.

AWS Deep Learning AMIs are a secure and curated set of frameworks, dependencies and tools that ML practitioners and researchers can use to accelerate deep learning in cloud. Amazon Machine Images (AMIs), designed for Amazon Linux and Ubuntu, come preconfigured to include TensorFlow and PyTorch. To develop advanced ML models at scale, you can validate models with millions supported virtual tests. You can speed up the installation and configuration process of AWS instances and accelerate experimentation and evaluation by using up-to-date frameworks, libraries, and Hugging Face Transformers. Advanced analytics, ML and deep learning capabilities are used to identify trends and make forecasts from disparate health data.

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.

The NVIDIA GPU Optimized AMI is a virtual image that accelerates your GPU-accelerated Machine Learning and Deep Learning workloads. This AMI allows you to spin up a GPU accelerated EC2 VM in minutes, with a preinstalled Ubuntu OS and GPU driver. Docker, NVIDIA container toolkit, and Docker are also included. This AMI provides access to NVIDIA’s NGC Catalog. It is a hub of GPU-optimized software for pulling and running performance-tuned docker containers that have been tested and certified by NVIDIA. The NGC Catalog provides free access to containerized AI and HPC applications. It also includes pre-trained AI models, AI SDKs, and other resources. This GPU-optimized AMI comes free, but you can purchase enterprise support through NVIDIA Enterprise. Scroll down to the 'Support information' section to find out how to get support for AMI.

VisionPro Deep Learning is the best deep learning-based image analysis program for factory automation. Its field-tested algorithms have been optimized for machine vision. The graphical user interface makes it easy to train neural networks without sacrificing performance. VisionPro Deep Learning solves complex problems that are too difficult for traditional machine vision. It also provides consistency and speed that can't be achieved with human inspection. Automation engineers can quickly choose the right tool for the job by combining VisionPro's rule-based visual libraries. VisionPro Deep Learning is a combination of a comprehensive machine vision tool collection with advanced deep learning tools within a common development-deployment framework. It makes it easy to develop highly variable vision applications.

Brodmann17's artificial intelligence is revolutionizing safety and mobility. The company's computer-vision-centered technology saves 95% of computing power. This significant cost savings has allowed AI to be used in new verticals such as mass-market passenger cars, video telematics and micro-mobility. Brodmann17's Vision AI is based upon deep learning neural networks, which extract all information from a video in order to make the entire ADAS system smarter. The patent-pending perception software is hardware independent and highly scalable. It is easy-to-integrate and deploy, as it can be used on any processor, from the low-power edge to cloud. Brodmann17 is the world's most efficient solution, with state-of–the-art accuracy for dashcams that use any low-power commodity processor. The solution is based upon patented deep learning neural network technology, specifically designed for automotive use. It offers top accuracy.

The definitive system for modern technical computing. Mathematica is the global standard for technical computing. It has been the main computing environment for millions of students, educators, and innovators around the globe for over three decades. Mathematica is widely admired for its technical prowess as well as its elegant ease-of-use. It seamlessly integrates all aspects of technical computing and is available in the cloud via any web browser as well as natively on any modern desktop system. Mathematica is a pioneer in technical computing support and workflows, thanks to its energetic development and consistent vision over three decades.

The Intel®, Deep Learning SDK is a collection of tools that allows data scientists and software developers alike to create, train, and then deploy deep learning solutions. The SDK includes a training tool as well as a deployment tool. These tools can be used together or separately to create a complete deep-learning workflow. You can easily prepare training data, design models, train models with automated experiments, advanced visualizations, and conduct experiments. It is easy to install and use popular deep learning frameworks that are optimized for Intel®. You can easily prepare training data, design models, train models with automated experiments, advanced visualizations, and prepare training data. It makes it easier to install and use popular deep learning frameworks that are optimized for Intel®. The web interface features an easy-to-use wizard for creating deep learning models. There are also tooltips to help you navigate the process.

DeepCube is a company that focuses on deep learning technologies. This technology can be used to improve the deployment of AI systems in real-world situations. The company's many patent innovations include faster, more accurate training of deep-learning models and significantly improved inference performance. DeepCube's proprietary framework is compatible with any hardware, datacenters or edge devices. This allows for over 10x speed improvements and memory reductions. DeepCube is the only technology that allows for efficient deployment of deep-learning models on intelligent edge devices. The model is typically very complex and requires a lot of memory. Deep learning deployments today are restricted to the cloud because of the large amount of memory and processing requirements.

Neural Designer is a data-science and machine learning platform that allows you to build, train, deploy, and maintain neural network models. This tool was created to allow innovative companies and research centres to focus on their applications, not on programming algorithms or programming techniques. Neural Designer does not require you to code or create block diagrams. Instead, the interface guides users through a series of clearly defined steps. Machine Learning can be applied in different industries. These are some examples of machine learning solutions: - In engineering: Performance optimization, quality improvement and fault detection - In banking, insurance: churn prevention and customer targeting. - In healthcare: medical diagnosis, prognosis and activity recognition, microarray analysis and drug design. Neural Designer's strength is its ability to intuitively build predictive models and perform complex operations.

RZT aiOS provides all the benefits of a unified AI platform, and more. It's not just a platform, it's an Operating System that connects, manages, and unifies all your AI initiatives. AI developers can now do what used to take months in days thanks to aiOS process management which dramatically increases their productivity. This Operating System provides an intuitive environment for AI development. It allows you to visually build models, explore data and create processing pipelines. You can also run experiments and view analytics. It's easy to do all of this without any advanced software engineering skills.

Keras is an API that is designed for humans, not machines. Keras follows best practices to reduce cognitive load. It offers consistent and simple APIs, minimizes the number required for common use cases, provides clear and actionable error messages, as well as providing clear and actionable error messages. It also includes extensive documentation and developer guides. Keras is the most popular deep learning framework among top-5 Kaggle winning teams. Keras makes it easy to run experiments and allows you to test more ideas than your competitors, faster. This is how you win. Keras, built on top of TensorFlow2.0, is an industry-strength platform that can scale to large clusters (or entire TPU pods) of GPUs. It's possible and easy. TensorFlow's full deployment capabilities are available to you. Keras models can be exported to JavaScript to run in the browser or to TF Lite for embedded devices on iOS, Android and embedded devices. Keras models can also be served via a web API.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

DataMelt, or "DMelt", is an environment for numeric computations, data analysis, data mining and computational statistics. DataMelt allows you to plot functions and data in 2D or 3D, perform statistical testing, data mining, data analysis, numeric computations and function minimization. It also solves systems of linear and differential equations. There are also options for symbolic, non-linear, and linear regression. Java API integrates neural networks and data-manipulation techniques using various data-manipulation algorithms. Support is provided for elements of symbolic computations using Octave/Matlab programming. DataMelt provides a Java platform-based computational environment. It can be used on different operating systems and programming languages. It is not limited to one programming language, unlike other statistical programs. This software combines Java, the most widely used enterprise language in the world, with the most popular data science scripting languages, Jython (Python), Groovy and JRuby.

Distributed training is possible without changing the model code. Determined takes care of provisioning, networking, data load, and fault tolerance. Our open-source deep-learning platform allows you to train your models in minutes and hours, not days or weeks. You can avoid tedious tasks such as manual hyperparameter tweaking, re-running failed jobs, or worrying about hardware resources. Our distributed training implementation is more efficient than the industry standard. It requires no code changes and is fully integrated into our state-ofthe-art platform. With its built-in experiment tracker and visualization, Determined records metrics and makes your ML project reproducible. It also allows your team to work together more easily. Instead of worrying about infrastructure and errors, your researchers can focus on their domain and build upon the progress made by their team.

Our MIND platform will help you solve your problem. We then train your staff to maintain the solution, and re-initialize the underlying models if necessary. Our products and services are used by businesses in the industrial, medical, consumer service, and consumer service industries to automate processes that were previously only possible with human intervention. Quality assurance in the food industry. Counting and classifying cells in biomedicine. Analyzing gaming performance. Measuring geometrical characteristics (position, size, profile, distance, angle. Tracking objects in agriculture. Time series analysis in sport and healthcare. Our MInD platform allows you to build AI solutions for your business. It provides all the tools you need to develop deep learning solutions in each of the five stages.

We work closely with companies to identify the issues that prevent them from realising their potential. We create AI platforms that allow corporations to take full control and empowerment of their data. Adopting AI platforms at a reasonable cost will help you to protect your data and ensure that your privacy is protected. Enhance efficiency in businesses and increase the quality of the work done by humans. AI is used to automate repetitive or dangerous tasks. It also bypasses human intervention. This allows for faster tasks that are creative and compassionate. Machine Learning allows applications to easily provide predictive capabilities. It can create regression and classification models. It can also visualize and do clustering. It supports multiple ML libraries, including Scikit-Learn and Tensorflow. It contains around 22 algorithms for building classifications, regressions and clustering models.

Virtualization Software for AI Infrastructure. Increase GPU utilization by having visibility and control over AI workloads. Run:AI has created the first virtualization layer in the world for deep learning training models. Run:AI abstracts workloads from the underlying infrastructure and creates a pool of resources that can dynamically provisioned. This allows for full utilization of costly GPU resources. You can control the allocation of costly GPU resources. The scheduling mechanism in Run:AI allows IT to manage, prioritize and align data science computing requirements with business goals. IT has full control over GPU utilization thanks to Run:AI's advanced monitoring tools and queueing mechanisms. IT leaders can visualize their entire infrastructure capacity and utilization across sites by creating a flexible virtual pool of compute resources.

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.

Auger.AI offers the best solution to ensure accuracy of machine learning models. Our MLRAM tool (Machine Learning Review & Monitoring) ensures that your models are always accurate. It even calculates the ROI for your predictive model! MLRAM can be used with any machine-learning technology stack. Inaccurate predictions can cost you money if your ML system's lifecycle doesn't include consistent measurement. Frequent retraining models can be costly and may not solve the problem if they are experiencing concept drift. MLRAM is a valuable tool for both data scientists and business users. It includes features such as accuracy visualization graphs and performance alerts. It also allows for anomaly detection and automated optimized retraining. It takes only one line of code to connect your predictive model with MLRAM. Qualified users can get a one-month free trial of MLRAM. Auger.AI is the most accurate AutoML platform.

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.

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.

RapidMiner is redefining enterprise AI so anyone can positively shape the future. RapidMiner empowers data-loving people from all levels to quickly create and implement AI solutions that drive immediate business impact. Our platform unites data prep, machine-learning, and model operations. This provides a user experience that is both rich in data science and simplified for all others. Customers are guaranteed success with our Center of Excellence methodology, RapidMiner Academy and no matter what level of experience or resources they have.

DeepSpeed is a deep learning optimization library that is open source for PyTorch. It is designed to reduce memory and computing power, and to train large distributed model with better parallelism using existing computer hardware. DeepSpeed is optimized to provide high throughput and low latency training. DeepSpeed can train DL-models with more than 100 billion parameters using the current generation GPU clusters. It can also train as many as 13 billion parameters on a single GPU. DeepSpeed, developed by Microsoft, aims to provide distributed training for large models. It's built using PyTorch which is a data parallelism specialist.

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.

Abacus.AI is the first global end-to-end autonomous AI platform. It enables real-time deep-learning at scale for common enterprise use cases. Our innovative neural architecture search methods allow you to create custom deep learning models and then deploy them on our end-to-end DLOps platform. Our AI engine will increase user engagement by at least 30% through personalized recommendations. Our recommendations are tailored to each user's preferences, which leads to more interaction and conversions. Don't waste your time dealing with data issues. We will automatically set up your data pipelines and retrain the models. To generate recommendations, we use generative modeling. This means that even if you have very little information about a user/item, you won't have a cold start.

AWS Inferentia Accelerators are designed by AWS for high performance and low cost for deep learning (DL), inference applications. The first-generation AWS Inferentia accelerator powers Amazon Elastic Compute Cloud, Amazon EC2 Inf1 instances. These instances deliver up to 2.3x more throughput and up 70% lower cost per input than comparable GPU-based Amazon EC2 instances. Inf1 instances have been adopted by many customers including Snap, Sprinklr and Money Forward. They have seen the performance and cost savings. The first-generation Inferentia features 8 GB of DDR4 memory per accelerator, as well as a large amount on-chip memory. Inferentia2 has 32 GB of HBM2e, which increases the total memory by 4x and memory bandwidth 10x more than Inferentia.

Our platforms are a solid foundation for custom machine learning and artificial Intelligence solutions. Build next-best-action applications that learn, adapt, and optimize using reinforcement-learning based algorithms. Custom, continuously-improving deep learning vision models to solve your unique challenges. Forecasts that are up-to-date will help you predict the future. Cloud-based tools that monitor and analyze cloud data will help you make better decisions for your company. Experienced data scientists and engineers face a challenge in transforming a machine learning application from research and ad hoc code to a robust, scalable platform. With a comprehensive suite of tools to manage and deploy your machine learning applications, Strong ML makes this easier.

The most complex AI, ML, Deep Learning models can be trained. With just a few clicks, you can scale from a single machine up to a whole fleet of VMs. Lambda Cloud makes it easy to scale up or start your Deep Learning project. You can get started quickly, save compute costs, and scale up to hundreds of GPUs. Every VM is pre-installed with the most recent version of Lambda Stack. This includes major deep learning frameworks as well as CUDA®. drivers. You can access the cloud dashboard to instantly access a Jupyter Notebook development environment on each machine. You can connect directly via the Web Terminal or use SSH directly using one of your SSH keys. Lambda can make significant savings by building scaled compute infrastructure to meet the needs of deep learning researchers. Cloud computing allows you to be flexible and save money, even when your workloads increase rapidly.

It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP).