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    • Speech Recognition

    フィルター

    「speech recognition」の32件の結果

    • IIT Roorkee

      IIT Roorkee

      Post Graduate Certificate in Data Science & Machine Learning

      習得できるスキル: Computer Programming, Statistical Programming, Machine Learning, Python Programming

      University Certificate

    • IIT Roorkee

      IIT Roorkee

      Post Graduate Certificate in Advanced Machine Learning & AI

      University Certificate

    • DeepLearning.AI

      DeepLearning.AI

      Deep Learning

      習得できるスキル: Algorithms, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Big Data, Communication, Computational Logic, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Convolutional Neural Network, Data Management, Deep Learning, Entrepreneurship, General Statistics, Hardware Design, Human Computer Interaction, Interactive Design, Linear Algebra, Machine Learning, Machine Learning Algorithms, Markov Model, Mathematical Optimization, Mathematical Theory & Analysis, Mathematics, Natural Language Processing, Probability & Statistics, Python Programming, Regression, Statistical Machine Learning, Statistical Programming, Strategy and Operations, Theoretical Computer Science

      4.8

      (133.1k件のレビュー)

      Intermediate · Specialization

    • Stanford University

      Stanford University

      Probabilistic Graphical Models

      習得できるスキル: Advertising, Algebra, Algorithms, Bayesian, Bayesian Network, Bayesian Statistics, Behavioral Economics, Business Psychology, Communication, Computer Architecture, Computer Programming, Data Analysis, Decision Making, Distributed Computing Architecture, Entrepreneurship, Feature Engineering, General Statistics, Graph Theory, Leadership and Management, Machine Learning, Marketing, Mathematics, Modeling, Other Programming Languages, Probability, Probability & Statistics, Probability Distribution, Theoretical Computer Science

      4.6

      (1.5k件のレビュー)

      Advanced · Specialization

    • DeepLearning.AI

      DeepLearning.AI

      Sequence Models

      習得できるスキル: Translation, Theoretical Computer Science, Language, Speech, Artificial Neural Networks, Linear Algebra, Modeling, Algorithms, Deep Learning, Communication, Big Data, Natural Language Processing, Computer Graphics, Interactive Design, Human Computer Interaction, Machine Learning, Data Management

      4.8

      (27.9k件のレビュー)

      Intermediate · Course

    • Stanford University

      Stanford University

      Probabilistic Graphical Models 1: Representation

      習得できるスキル: Probability Distribution, Advertising, Behavioral Economics, Mathematics, Modeling, Probability, General Statistics, Computer Programming, Communication, Probability & Statistics, Data Analysis, Marketing, Other Programming Languages, Feature Engineering, Decision Making, Business Psychology, Entrepreneurship, Leadership and Management, Graph Theory, Machine Learning, Bayesian

      4.6

      (1.4k件のレビュー)

      Advanced · Course

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      Stanford University

      Stanford University

      Probabilistic Graphical Models 2: Inference

      習得できるスキル: Approximation, Computer Architecture, Mathematics, Distributed Computing Architecture, General Statistics, Probability & Statistics, Inference, Machine Learning

      4.6

      (469件のレビュー)

      Advanced · Course

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      Stanford University

      Stanford University

      Probabilistic Graphical Models 3: Learning

      習得できるスキル: Bayesian Statistics, General Statistics, Theoretical Computer Science, Mathematics, Probability & Statistics, Bayesian Network, Algorithms, Algebra, Machine Learning

      4.6

      (292件のレビュー)

      Advanced · Course

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      University of California San Diego

      University of California San Diego

      Teaching Impacts of Technology: Data Collection, Use, and Privacy

      習得できるスキル: Cryptography, Big Data, Algorithms, Financial Analysis, Theoretical Computer Science, Security Engineering, Data Management

      4.6

      (10件のレビュー)

      Beginner · Course

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      Google Cloud

      Google Cloud

      Google Cloud Speech API: Qwik Start

      Beginner · Project

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      DeepLearning.AI

      DeepLearning.AI

      DeepLearning.AI TensorFlow Developer

      習得できるスキル: Applied Machine Learning, Artificial Neural Networks, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Convolutional Neural Network, Deep Learning, Forecasting, General Statistics, Language, Machine Learning, Machine Learning Algorithms, Natural Language, Natural Language Processing, Probability & Statistics, Programming Principles, Python Programming, Statistical Machine Learning, Statistical Programming, Tensorflow

      4.7

      (21.9k件のレビュー)

      Intermediate · Professional Certificate

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      DeepLearning.AI

      DeepLearning.AI

      Natural Language Processing

      習得できるスキル: Artificial Neural Networks, Computer Graphics, Computer Programming, Deep Learning, General Statistics, Human Computer Interaction, Language, Machine Learning, Machine Learning Algorithms, Mathematics, Modeling, Natural Language Processing, Operations Research, Probability & Statistics, Python Programming, Regression, Research and Design, Statistical Programming, Strategy and Operations, User Experience

      4.6

      (4.5k件のレビュー)

      Intermediate · Specialization

    123

    要約して、speech recognition の人気コース10選をご紹介します。

    • Post Graduate Certificate in Data Science & Machine Learning: IIT Roorkee
    • Post Graduate Certificate in Advanced Machine Learning & AI: IIT Roorkee
    • Deep Learning: DeepLearning.AI
    • Probabilistic Graphical Models: Stanford University
    • Sequence Models: DeepLearning.AI
    • Probabilistic Graphical Models 1: Representation: Stanford University
    • Probabilistic Graphical Models 2: Inference: Stanford University
    • Probabilistic Graphical Models 3: Learning: Stanford University
    • Teaching Impacts of Technology: Data Collection, Use, and Privacy: University of California San Diego
    • Google Cloud Speech API: Qwik Start: Google Cloud

    Design And Productで学べるスキル

    ユーザーインターフェイス (18)
    ユーザーエクスペリエンス (16)
    ソフトウェアテスト (13)
    ゲームデザイン (11)
    アジャイルソフトウェア開発 (10)
    グラフィックス (10)
    バーチャルリアリティ(仮想現実) (9)
    デザイン思考 (8)
    Web (8)
    テレビゲーム開発 (7)
    Webデザイン (7)
    Adobe Photoshop (6)

    Speech Recognitionに関するよくある質問

    • Speech recognition refers to the process by which computer software translates human speech to a written, machine-readable format. This capability is used increasingly widely, in applications ranging from simple dictation and question-answering programs to tools for real-time foreign language translation and full-featured chatbots. Advanced speech recognition capabilities will be an important part of the future of computer software and mobile apps, allowing users to interact with software in an intuitive and hands-free way.

      While the history of speech recognition dates back to the 1960s, progress in this field has accelerated greatly in the past decade with the advent of machine learning and deep learning. The use of these algorithmic approaches have opened the door for robust natural language processing (NLP), which goes beyond the simple conversion of spoken words into text and allows programs to understand the meaning of those words - and respond appropriately, if desired. NLP will be at the forefront of artificial intelligence (AI) applications, playing a key role in the functionality of helpful digital assistants and virtual agents as well as advanced robotics.‎

    • Speech recognition and natural language processing (NLP) programming skills are in high demand from a growing range of companies seeking to tap into this technology to create new products and services. Typically using Python programming and TensorFlow, machine learning and deep learning engineers with expertise in this field build models that analyze speech and language, discover contextual patterns, and produce insights and situationally appropriate responses. According to Glassdoor, NLP engineers earned an average annual salary of $114,121 as of November 2020.‎

    • Yes! Coursera has a variety of opportunities to learn about topics in machine learning and deep learning, including courses specifically on speech recognition and natural language processing (NLP). You can learn from top-ranked institutions in the field, like Stanford University and deeplearning.ai, leading companies like Google Cloud, or even by completing step-by-step tutorials alongside experienced instructors as part of Coursera’s Guided Projects. No matter how you choose to learn, you’ll be able to view course materials and complete assignments on a flexible schedule, which allows you to fit this valuable education in speech recognition into your existing studies, work, or family life.‎

    • The skills and experience you might need to already have before starting to learn speech recognition are right in front of you, in the form of your mobile device. You may already be using speech to text your friends and family. This is one of the clearest examples of speech recognition, and voice-to-text is an experience you are likely familiar with. You may also want to learn about voice-activated assistants, like Alexa, Siri, and Google Assistant. These three offer the latest technologies that may help you learn speech recognition.‎

    • The kind of people that are best suited for roles in speech recognition are those technology professionals who understand how software drives speech recognition. Software creators write programs to process human speech into written words on a screen. Speech recognition works as it focuses on translating speech from a verbal format to a text-based format. These people who are best suited for roles in speech recognition likely are also well versed in or want to learn about programming languages and artificial intelligence.‎

    • How do I know if learning speech recognition is right for me? You may know if learning speech recognition is right for you if you have a deep passion to work on the cutting edge of technology. Speech recognition technology has fast changed the way modern society communicates, and there is likely a great deal more learning that will come in this area. You may also want to use your software skills to write code to train computers in similar ways to how we train our own brains, using fundamental processes, innovative thinking, and data research. If you’re interested in learning speech recognition as a career, you might know that this is a field that is right for you.‎

    • Some topics that are related to speech recognition may include natural language processing (NLP), deep learning neural networks, machine learning, and artificial intelligence. You may also want to dive into each of the main speech recognition technologies offered by the major tech companies like Amazon, Google, Apple, Facebook, and others.‎

    このFAQの内容は、情報提供のみを目的としています。受講生は、自分の個人的、職業的、経済的な目標に合ったコースやその他の資格を取得するために、さらに調べることをお勧めします。
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