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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.
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.
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.
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.
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.
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.
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21 Products for "deep learning with python" with 1 filter applied:

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  • 1
    MXNet

    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. ...
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  • 2
    Horovod

    Horovod

    Horovod

    Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks.
    Starting Price: Free
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  • 3
    Modelbit

    Modelbit

    Modelbit

    ...GitHub, GitLab, or home grown. Code review. CI/CD pipelines. PRs and merge requests. Bring your whole git workflow to your Python ML models. Modelbit integrates seamlessly with Hex, DeepNote, Noteable and more. Take your model straight from your favorite cloud notebook into production. Sick of VPC configurations and IAM roles? Seamlessly redeploy your SageMaker models to Modelbit. Immediately reap the benefits of Modelbit's platform with the models you've already built.
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  • 4
    MindSpore

    MindSpore

    MindSpore

    ​MindSpore is an open source deep learning framework developed by Huawei, designed to facilitate easy development, efficient execution, and deployment across cloud, edge, and device environments. It supports multiple programming paradigms, including both object-oriented and functional programming, allowing users to define AI networks using native Python syntax. MindSpore offers a unified programming experience that seamlessly integrates dynamic and static graphs, enhancing compatibility and performance. ...
    Starting Price: Free
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  • 5
    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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  • 6
    DeepSpeed

    DeepSpeed

    Microsoft

    DeepSpeed is an open source deep learning optimization library for PyTorch. It's designed to reduce computing power and memory use, and to train large distributed models with better parallelism on existing computer hardware. DeepSpeed is optimized for low latency, high throughput training. DeepSpeed can train DL models with over a hundred billion parameters on the current generation of GPU clusters.
    Starting Price: Free
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  • 7
    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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  • 8
    JAX

    JAX

    JAX

    ​JAX is a Python library designed for high-performance numerical computing and machine learning research. It offers a NumPy-like API, facilitating seamless adoption for those familiar with NumPy. Key features of JAX include automatic differentiation, just-in-time compilation, vectorization, and parallelization, all optimized for execution on CPUs, GPUs, and TPUs.
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  • 9
    IBM Watson Machine Learning Accelerator
    Accelerate your deep learning workload. Speed your time to value with AI model training and inference. With advancements in compute, algorithm and data access, enterprises are adopting deep learning more widely to extract and scale insight through speech recognition, natural language processing and image classification. Deep learning can interpret text, images, audio and video at scale, generating patterns for recommendation engines, sentiment analysis, financial risk modeling and anomaly detection. ...
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  • 10
    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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  • 11
    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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  • 12
    Caffe

    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.
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  • 13
    ML Console

    ML Console

    ML Console

    ...By leveraging modern web technologies like WebAssembly and WebGL, ML Console achieves training speeds comparable to traditional Python-based methods. Its user-friendly interface simplifies the machine learning process, making it approachable for users with no advanced AI expertise. Additionally, ML Console is free to use, eliminating barriers to entry for those interested in exploring machine learning solutions. ​
    Starting Price: Free
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  • 14
    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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  • 15
    Gensim

    Gensim

    Radim Řehůřek

    Gensim is a free, open source Python library designed for unsupervised topic modeling and natural language processing, focusing on large-scale semantic modeling. It enables the training of models like Word2Vec, FastText, Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA), facilitating the representation of documents as semantic vectors and the discovery of semantically related documents. Gensim is optimized for performance with highly efficient implementations in Python...
    Starting Price: Free
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  • 16
    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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  • 17
    Chainer

    Chainer

    Chainer

    ...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. ...
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  • 18
    V7 Darwin
    V7 Darwin is a powerful AI-driven platform for labeling and training data that streamlines the process of annotating images, videos, and other data types. By using AI-assisted tools, V7 Darwin enables faster, more accurate labeling for a variety of use cases such as machine learning model training, object detection, and medical imaging. The platform supports multiple types of annotations, including keypoints, bounding boxes, and segmentation masks. It integrates with various workflows...
    Starting Price: $150
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  • 19
    Tinker

    Tinker

    Thinking Machines Lab

    Tinker is a training API designed for researchers and developers that allows full control over model fine-tuning while abstracting away the infrastructure complexity. It supports primitives and enables users to build custom training loops, supervision logic, and reinforcement learning flows. It currently supports LoRA fine-tuning on open-weight models across both LLama and Qwen families, ranging from small models to large mixture-of-experts architectures. Users write Python code to handle data, loss functions, and algorithmic logic; Tinker handles scheduling, resource allocation, distributed training, and failure recovery behind the scenes. ...
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  • 20
    Deepgram

    Deepgram

    Deepgram

    ...Our platform recognizes multiple languages, accents, and words, dynamically tuning to the needs of your business with every training session. The fastest, most accurate, most reliable, most scalable speech transcription, with understanding — rebuilt just for enterprise. We’ve reinvented ASR with 100% deep learning that allows companies to continuously improve accuracy. Stop waiting for the big tech players to improve their software and forcing your developers to manually boost accuracy with keywords in every API call. Start training your speech model and reaping the benefits in weeks, not months or years.
    Starting Price: $0
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  • 21
    alwaysAI

    alwaysAI

    alwaysAI

    alwaysAI provides developers with a simple and flexible way to build, train, and deploy computer vision applications to a wide variety of IoT devices. Select from a catalog of deep learning models or upload your own. Use our flexible and customizable APIs to quickly enable core computer vision services. Quickly prototype, test and iterate with a variety of camera-enabled ARM-32, ARM-64 and x86 devices. Identify objects in an image by name or classification. Identify and count objects appearing in a real-time video feed. ...
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