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Deep Learning Software
Deep learning software provides tools and frameworks for developing, training, and deploying artificial neural networks, particularly for complex tasks such as image and speech recognition, natural language processing (NLP), and autonomous systems. These platforms leverage large datasets and powerful computational resources to enable machines to learn patterns and make predictions. Popular deep learning software includes frameworks like TensorFlow, PyTorch, Keras, and Caffe, which offer pre-built models, libraries, and tools for designing custom models. Deep learning software is essential for industries that require advanced AI solutions, including healthcare, finance, automotive, and entertainment.
Machine Learning Software
Machine learning software enables developers and data scientists to build, train, and deploy models that can learn from data and make predictions or decisions without being explicitly programmed. These tools provide frameworks and algorithms for tasks such as classification, regression, clustering, and natural language processing. They often come with features like data preprocessing, model evaluation, and hyperparameter tuning, which help optimize the performance of machine learning models. With the ability to analyze large datasets and uncover patterns, machine learning software is widely used in industries like healthcare, finance, marketing, and autonomous systems. Overall, this software empowers organizations to leverage data for smarter decision-making and automation.
Language Learning Software
Language learning software offers structured programs designed to teach users new languages through interactive lessons and exercises. These platforms typically include modules for grammar, vocabulary, pronunciation, and listening comprehension, helping learners build foundational language skills. Many language learning software options utilize multimedia tools, such as audio, video, and quizzes, to reinforce lessons and keep users engaged. Some software also offers tracking features to monitor progress and set personalized learning goals. With the flexibility to learn at one's own pace, these tools make it possible to study languages from beginner to advanced levels, often providing support for multiple languages.
Online Learning Platforms
Online learning platforms enable users to learn about a wide variety of topics. Online learning platforms, sometimes known as MOOCs (Massive Open Online Courses) or EdTech platforms, allow anyone to learn new topics and skills. Online learning platforms can be used by individuals, or by companies and organizations that want to educate and upskill their employees.
Learning Management Systems (LMS)
A Learning Management System, or LMS, is a software platform used to create, deliver and manage online training programs. They provide instructors with the ability to design course content, track learner progress, assess performance and generate reports. Learners also benefit from self-paced learning options that are available through most systems. Additionally, an LMS can be tailored to meet specific organizational needs such as providing specialized learning paths for different job roles.
Mobile Learning Software
Mobile learning software enables learners to access educational content and training materials on mobile devices, such as smartphones and tablets, enhancing flexibility and accessibility. These platforms typically offer features like interactive lessons, quizzes, progress tracking, and multimedia content that can be accessed anytime, anywhere. Mobile learning software is widely used for both formal education and corporate training, providing users with a more engaging, personalized learning experience. By using this software, businesses and educational institutions can deliver content to learners on-the-go, increase engagement, and improve knowledge retention.
Learning Experience Platforms
Learning Experience Platforms (LXPs) are advanced digital platforms designed to deliver personalized, engaging, and learner-centric education and training experiences. They aggregate content from various sources—such as courses, videos, articles, and social learning—and use AI to recommend relevant learning paths based on individual needs and preferences. LXPs support interactive features like social collaboration, gamification, and assessments to boost learner motivation and retention. By integrating with Learning Management Systems (LMS) and other HR tools, they provide seamless tracking and analytics of learning progress. Ultimately, LXPs empower organizations to foster continuous skill development and adapt learning to the evolving needs of employees and learners.
Learning Analytics Software
Learning analytics software enables organizations to collect, measure, and analyze learning data from learning management systems, training software, and educational software.
Social Learning Platforms
Social learning platforms are online tools that facilitate the exchange of knowledge and information between individuals. These platforms enable users to connect with others who share similar interests or goals, allowing them to engage in meaningful discussions and collaborative learning activities. They often offer a variety of features such as discussion forums, virtual classrooms, and interactive content to enhance the learning experience. Users can also track their progress and receive feedback from peers and instructors on these platforms. Overall, social learning platforms provide a convenient and accessible way for individuals to learn new skills and expand their knowledge through interaction with others.
Active Learning Software
Active learning tools in artificial intelligence are systems designed to enhance the efficiency of machine learning by strategically selecting the most valuable data points for training.
eLearning Content Providers
eLearning content providers create and deliver educational materials and courses in digital formats, making learning accessible online. They offer a wide range of content, from professional training and certifications to academic subjects and personal development. These providers often use multimedia elements—such as videos, quizzes, and interactive exercises—to enhance engagement and understanding. With scalable platforms, eLearning content providers cater to diverse audiences, including corporations, educational institutions, and individual learners. Their goal is to make quality education flexible and accessible, supporting learning anywhere, anytime.
Machine Learning as a Service Software
Machine Learning as a Service (MLaaS) platforms provide users with cloud-based access to pre-trained models and algorithms for predictive analytics and data analysis. MLaaS makes machine learning accessible by eliminating the need to develop and maintain complex infrastructure, instead allowing users to leverage remote computing resources in order to generate insights quickly and easily. These platforms vary in terms of features, services and pricing, offering different combinations designed to cater to a range of user needs.
AI Language Learning Software
AI language learning apps use artificial intelligence to help users learn new languages more efficiently by personalizing the learning experience. These apps often offer interactive lessons, voice recognition, and real-time feedback to improve pronunciation, grammar, and vocabulary. The AI algorithms adapt to the user's learning pace, strengths, and weaknesses, providing tailored exercises and challenges.
Neural Network Software
Neural network software uses algorithms to simulate the human brain's ability to recognize patterns. It can be used for a variety of purposes, such as image and voice recognition, natural language processing, and decision making. The software is typically composed of a number of layers that contain parameters which are adjusted through training. Finally, it can be applied in various areas such as healthcare, finance, engineering and more.
eLearning Authoring Tools
eLearning authoring tools are software platforms that enable educators, instructional designers, and businesses to create interactive and engaging online learning content. These tools allow users to design courses, lessons, quizzes, and assessments without needing extensive programming knowledge. Features typically include drag-and-drop functionality, multimedia integration (text, images, videos, audio), and SCORM or xAPI compatibility for integration with Learning Management Systems (LMS). Some eLearning authoring tools also support responsive design, enabling content to be viewed on various devices like desktops, tablets, and smartphones. They are commonly used to develop courses for corporate training, academic programs, and professional development.
Corporate Learning Management Systems Software
Corporate learning management systems (LMS) are software platforms that help companies manage, track and deliver employee training. They provide a centralized repository for all the educational resources a company makes available, as well as tracking and reporting tools to measure progress. An LMS can also be used to create customized individual learning plans or onboarding experiences.
Healthcare Learning Management Systems (LMS)
Healthcare learning management systems (LMS) are digital platforms used to help healthcare organizations improve their education and training processes. They provide features such as course and content authoring, user tracking and reporting, mobile device accessibility and more. With these tools, healthcare providers can optimize their training methods for better patient outcomes.
Hospitality Learning Management Systems (LMS) Software
Hospitality Learning Management Systems (LMS) provide a comprehensive solution to the challenges of staff training and compliance in the hospitality industry.
Learning Content Management Systems (LCMS) Software
A learning content management system (LCMS) is a software application designed to facilitate the creation, organization, and delivery of educational materials. It allows users to upload, organize, and manage course content such as videos, documents, and interactive quizzes.
AI-Based Learning Management Systems Software
AI-based learning management systems (LMS) leverage artificial intelligence to enhance the educational experience by personalizing learning paths for each student. These systems use algorithms to analyze learners' progress and adapt content, making recommendations based on individual strengths, weaknesses, and preferences. AI-powered LMS can automate administrative tasks, such as grading and feedback, allowing instructors to focus more on interaction and engagement. Additionally, they provide real-time analytics, offering insights into student performance, engagement levels, and overall learning trends.
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34 Products for "deep learning with python" with 3 filters applied:

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  • 1
    JarvisLabs.ai

    JarvisLabs.ai

    JarvisLabs.ai

    We have set up all the infrastructure, computing, and software (Cuda, Frameworks) required for you to train and deploy your favorite deep-learning models. You can spin up GPU/CPU-powered instances directly from your browser or automate it through our Python API.
    Starting Price: $1,440 per month
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  • 2
    AWS Deep Learning AMIs
    AWS Deep Learning AMIs (DLAMI) provides ML practitioners and researchers with a curated and secure set of frameworks, dependencies, and tools to accelerate deep learning in the cloud. Built for Amazon Linux and Ubuntu, Amazon Machine Images (AMIs) come preconfigured with TensorFlow, PyTorch, Apache MXNet, Chainer, Microsoft Cognitive Toolkit (CNTK), Gluon, Horovod, and Keras, allowing you to quickly deploy and run these frameworks and tools at scale. ...
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  • 3
    SynapseAI

    SynapseAI

    Habana Labs

    ...Habana-based deep learning processors, preserve software investments, and make it easy to build new models— for both training and deployment of the numerous and growing models defining deep learning, generative AI and large language models.
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  • 4
    Google Cloud Deep Learning VM Image
    Deep Learning VM Image delivers a seamless notebook experience with integrated support for JupyterLab.
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  • 5
    Azure Data Science Virtual Machines
    ...Pay only for what you use, when you use it. Readily available GPU clusters with Deep Learning tools already pre-configured. Examples, templates and sample notebooks built or tested by Microsoft are provided on the VMs to enable easy onboarding to the various tools and capabilities such as Neural Networks (PYTorch, Tensorflow, etc.), Data Wrangling, R, Python, Julia, and SQL Server.
    Starting Price: $0.005
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  • 6
    AWS Deep Learning Containers
    Deep Learning Containers are Docker images that are preinstalled and tested with the latest versions of popular deep learning frameworks. Deep Learning Containers lets you deploy custom ML environments quickly without building and optimizing your environments from scratch. Deploy deep learning environments in minutes using prepackaged and fully tested Docker images.
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  • 7
    Google Deep Learning Containers
    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. ...
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  • 8
    Vertex AI
    Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex...
    743 Ratings
    Starting Price: Free ($300 in free credits)
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  • 9
    NetApp AIPod
    NetApp AIPod is a comprehensive AI infrastructure solution designed to streamline the deployment and management of artificial intelligence workloads. By integrating NVIDIA-validated turnkey solutions, such as NVIDIA DGX BasePOD™ and NetApp's cloud-connected all-flash storage, AIPod consolidates analytics, training, and inference capabilities into a single, scalable system. This convergence enables organizations to rapidly implement AI workflows, from model training to fine-tuning and...
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  • 10
    AWS Trainium

    AWS Trainium

    Amazon Web Services

    AWS Trainium is the second-generation Machine Learning (ML) accelerator that AWS purpose built for deep learning training of 100B+ parameter models. Each Amazon Elastic Compute Cloud (EC2) Trn1 instance deploys up to 16 AWS Trainium accelerators to deliver a high-performance, low-cost solution for deep learning (DL) training in the cloud. Although the use of deep learning is accelerating, many development teams are limited by fixed budgets, which puts a cap on the scope and frequency of training needed to improve their models and applications. ...
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  • 11
    NVIDIA NGC
    NVIDIA GPU Cloud (NGC) is a GPU-accelerated cloud platform optimized for deep learning and scientific computing. NGC manages a catalog of fully integrated and optimized deep learning framework containers that take full advantage of NVIDIA GPUs in both single GPU and multi-GPU configurations. NVIDIA train, adapt, and optimize (TAO) is an AI-model-adaptation platform that simplifies and accelerates the creation of enterprise AI applications and services. ...
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  • 12
    RunPod

    RunPod

    RunPod

    RunPod offers a cloud-based platform designed for running AI workloads, focusing on providing scalable, on-demand GPU resources to accelerate machine learning (ML) model training and inference. With its diverse selection of powerful GPUs like the NVIDIA A100, RTX 3090, and H100, RunPod supports a wide range of AI applications, from deep learning to data processing. The platform is designed to minimize startup time, providing near-instant access to GPU pods, and ensures scalability with autoscaling capabilities for real-time AI model deployment. ...
    180 Ratings
    Starting Price: $0.40 per hour
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  • 13
    NVIDIA RAPIDS
    ...Accelerate your Python data science toolchain with minimal code changes and no new tools to learn. Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.
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  • 14
    Amazon EC2 Trn1 Instances
    Amazon Elastic Compute Cloud (EC2) Trn1 instances, powered by AWS Trainium chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and latent diffusion models. Trn1 instances offer up to 50% cost-to-train savings over other comparable Amazon EC2 instances. You can use Trn1 instances to train 100B+ parameter DL and generative AI models across a broad set of applications, such as text summarization, code generation, question answering, image and video generation, recommendation, and fraud detection. ...
    Starting Price: $1.34 per hour
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  • 15
    Deep Infra

    Deep Infra

    Deep Infra

    Powerful, self-serve machine learning platform where you can turn models into scalable APIs in just a few clicks. Sign up for Deep Infra account using GitHub or log in using GitHub. Choose among hundreds of the most popular ML models. Use a simple rest API to call your model. Deploy models to production faster and cheaper with our serverless GPUs than developing the infrastructure yourself.
    1 Rating
    Starting Price: $0.70 per 1M input tokens
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  • 16
    Amazon EC2 Trn2 Instances
    Amazon EC2 Trn2 instances, powered by AWS Trainium2 chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and diffusion models. They offer up to 50% cost-to-train savings over comparable Amazon EC2 instances. Trn2 instances support up to 16 Trainium2 accelerators, providing up to 3 petaflops of FP16/BF16 compute power and 512 GB of high-bandwidth memory. To facilitate efficient data and model parallelism, Trn2 instances feature NeuronLink, a high-speed, nonblocking interconnect, and support up to 1600 Gbps of second-generation Elastic Fabric Adapter (EFAv2) network bandwidth. ...
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  • 17
    AWS Inferentia
    AWS Inferentia accelerators are designed by AWS to deliver high performance at the lowest cost for your deep learning (DL) inference applications. The first-generation AWS Inferentia accelerator powers Amazon Elastic Compute Cloud (Amazon EC2) Inf1 instances, which deliver up to 2.3x higher throughput and up to 70% lower cost per inference than comparable GPU-based Amazon EC2 instances. Many customers, including Airbnb, Snap, Sprinklr, Money Forward, and Amazon Alexa, have adopted Inf1 instances and realized its performance and cost benefits. ...
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  • 18
    Azure Machine Learning
    Accelerate the end-to-end machine learning lifecycle. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML.
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  • 19
    Amazon SageMaker Model Training
    ...To train deep learning models faster, SageMaker distributed training libraries can automatically split large models and training datasets across AWS GPU instances, or you can use third-party libraries, such as DeepSpeed, Horovod, or Megatron. Efficiently manage system resources with a wide choice of GPUs and CPUs including P4d.24xl instances, which are the fastest training instances currently available in the cloud.
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  • 20
    AWS Neuron

    AWS Neuron

    Amazon Web Services

    It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal...
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  • 21
    Amazon SageMaker JumpStart
    ...SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis.
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  • 22
    Google Cloud AI Infrastructure
    Options for every business to train deep learning and machine learning models cost-effectively. AI accelerators for every use case, from low-cost inference to high-performance training. Simple to get started with a range of services for development and deployment. Tensor Processing Units (TPUs) are custom-built ASIC to train and execute deep neural networks. Train and run more powerful and accurate models cost-effectively with faster speed and scale. ...
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  • 23
    DataRobot

    DataRobot

    DataRobot

    AI Cloud is a new approach built for the demands, challenges and opportunities of AI today. A single system of record, accelerating the delivery of AI to production for every organization. All users collaborate in a unified environment built for continuous optimization across the entire AI lifecycle. The AI Catalog enables seamlessly finding, sharing, tagging, and reusing data, helping to speed time to production and increase collaboration. The catalog provides easy access to the data needed...
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  • 24
    NVIDIA Brev
    ...NVIDIA Brev provides a seamless GPU sandbox with support for CUDA, Python, and Jupyter Lab accessible via browser or CLI. This enables developers to fine-tune, train, and deploy AI models with minimal friction and maximum flexibility.
    Starting Price: $0.04 per hour
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  • 25
    Predibase

    Predibase

    Predibase

    ...Choose from our menu of prebuilt data connectors that support your databases, data warehouses, lakehouses, and object storage. Train state-of-the-art deep learning models without the pain of managing infrastructure. Automated Machine Learning that strikes the balance of flexibility and control, all in a declarative fashion. With a declarative approach, finally train and deploy models as quickly as you want.
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  • 26
    Lambda

    Lambda

    Lambda

    Lambda provides high-performance supercomputing infrastructure built specifically for training and deploying advanced AI systems at massive scale. Its Superintelligence Cloud integrates high-density power, liquid cooling, and state-of-the-art NVIDIA GPUs to deliver peak performance for demanding AI workloads. Teams can spin up individual GPU instances, deploy production-ready clusters, or operate full superclusters designed for secure, single-tenant use. Lambda’s architecture emphasizes...
    1 Rating
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  • 27
    NVIDIA DGX Cloud
    NVIDIA DGX Cloud offers a fully managed, end-to-end AI platform that leverages the power of NVIDIA’s advanced hardware and cloud computing services. This platform allows businesses and organizations to scale AI workloads seamlessly, providing tools for machine learning, deep learning, and high-performance computing (HPC). DGX Cloud integrates seamlessly with leading cloud providers, delivering the performance and flexibility required to handle the most demanding AI applications. This service is ideal for businesses looking to enhance their AI capabilities without the need to manage physical infrastructure.
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  • 28
    WhiteFiber

    WhiteFiber

    WhiteFiber

    WhiteFiber is a vertically integrated AI infrastructure platform offering high-performance GPU cloud and HPC colocation solutions tailored for AI/ML workloads. Its cloud platform is purpose-built for machine learning, large language models, and deep learning, featuring NVIDIA H200, B200, and GB200 GPUs, ultra-fast Ethernet and InfiniBand networking, and up to 3.2 Tb/s GPU fabric bandwidth. WhiteFiber's infrastructure supports seamless scaling from hundreds to tens of thousands of GPUs, with flexible deployment options including bare metal, containers, and virtualized environments. ...
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  • 29
    NVIDIA Run:ai
    NVIDIA Run:ai is an enterprise platform designed to optimize AI workloads and orchestrate GPU resources efficiently. It dynamically allocates and manages GPU compute across hybrid, multi-cloud, and on-premises environments, maximizing utilization and scaling AI training and inference. The platform offers centralized AI infrastructure management, enabling seamless resource pooling and workload distribution. Built with an API-first approach, Run:ai integrates with major AI frameworks and...
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  • 30
    NVIDIA NIM
    ...NVIDIA NIM is a set of easy-to-use inference microservices that facilitate the deployment of foundation models across any cloud or data center, ensuring data security and streamlined AI integration. Additionally, NVIDIA AI provides access to the Deep Learning Institute (DLI), offering technical training to gain in-demand skills, hands-on experience, and expert knowledge in AI, data science, and accelerated computing. AI models generate responses and outputs based on complex algorithms and machine learning techniques, and those responses or outputs may be inaccurate, harmful, biased, or indecent. ...
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