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    Coursera
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    • Deep Learning

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    「deep learning」の690件の結果

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      CertNexus

      CertNexus Certified Artificial Intelligence Practitioner

      習得できるスキル: Machine Learning, Probability & Statistics, Machine Learning Algorithms, Data Analysis, Cloud Computing, Computer Graphic Techniques, Computer Graphics, Data Mining, Finance, Human Computer Interaction, Other Cloud Platforms and Tools, Project Management, Regulations and Compliance, Strategy and Operations, Virtual Reality, Artificial Neural Networks, Business Analysis, Feature Engineering, Statistical Analysis, Deep Learning, Marketing

      4.7

      (134件のレビュー)

      Intermediate · Professional Certificate · 3-6 Months

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      Microsoft

      Microsoft Azure Data Scientist Associate (DP-100)

      習得できるスキル: Machine Learning, Cloud Computing, Microsoft Azure, Machine Learning Algorithms, Probability & Statistics, Theoretical Computer Science, Algorithms, Apache, Big Data, Data Management, General Statistics, Computer Programming, Statistical Programming, Python Programming, Regression, Applied Machine Learning, Artificial Neural Networks, Computer Vision, Deep Learning, Bayesian Statistics, Business Analysis, Data Analysis, Exploratory Data Analysis, Extract, Transform, Load, Statistical Machine Learning, Strategy and Operations

      4.5

      (158件のレビュー)

      Intermediate · Professional Certificate · 3-6 Months

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      DeepLearning.AI

      TensorFlow: Data and Deployment

      習得できるスキル: Machine Learning, Tensorflow, Applied Machine Learning, Deep Learning, Computer Programming, Python Programming, Data Management, Extract, Transform, Load, Mobile Development, Data Science, Marketing, Statistical Programming, Computer Vision, Mobile Development Tools, Artificial Neural Networks, Computer Science, iOS Development, Javascript, Machine Learning Software, Data Model, Android Development, Application Development, Computer Architecture, Entrepreneurship, Leadership and Management, Microarchitecture, Problem Solving, Research and Design, Software Engineering, Swift Programming, Theoretical Computer Science, Cross Platform Development, Data Visualization, HTML and CSS, Java Programming, Machine Learning Algorithms, Security Engineering, Visualization (Computer Graphics), Web Development

      4.6

      (1.3k件のレビュー)

      Intermediate · Specialization · 3-6 Months

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      University of Washington

      Machine Learning

      習得できるスキル: Machine Learning, Machine Learning Algorithms, Data Science, Statistical Machine Learning, Linear Algebra, Statistical Analysis, Data Mining, Regression, Applied Machine Learning, Feature Engineering, General Statistics, Natural Language Processing, Python Programming, Machine Learning Software, Statistical Tests, Data Analysis, Dimensionality Reduction, Statistical Programming, Deep Learning, Basic Descriptive Statistics, Probability & Statistics, Computer Vision, Statistical Visualization, Estimation, Probability Distribution, Correlation And Dependence, Forecasting, Big Data, Data Management, Algorithms, Bayesian Statistics, Business Analysis, Business Psychology, Computational Logic, Computational Thinking, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Programming, Data Structures, Distributed Computing Architecture, Entrepreneurship, Exploratory Data Analysis, Markov Model, Mathematical Theory & Analysis, Mathematics, Theoretical Computer Science

      4.6

      (15.8k件のレビュー)

      Intermediate · Specialization · 3-6 Months

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      University of Illinois at Urbana-Champaign

      Deep Learning for Healthcare

      2.4

      (21件のレビュー)

      Advanced · Specialization · 1-3 Months

    • 無料

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      DeepLearning.AI

      الذكاء الاصطناعي للجميع

      習得できるスキル: Machine Learning

      4.8

      (756件のレビュー)

      Beginner · Course · 1-4 Weeks

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      無料

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      Yonsei University

      Deep Learning for Business

      習得できるスキル: Deep Learning, Machine Learning, Entrepreneurship, Artificial Neural Networks, Applied Machine Learning, Business Development, Sales

      4.4

      (640件のレビュー)

      Beginner · Course · 1-3 Months

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      Korea Advanced Institute of Science and Technology(KAIST)

      Math for AI beginner part 1 Linear Algebra

      習得できるスキル: Algebra, Linear Algebra, Mathematics

      4.8

      (13件のレビュー)

      Beginner · Course · 1-3 Months

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      NVIDIA

      Introduction to AI in the Data Center

      習得できるスキル: Deep Learning, Machine Learning

      Beginner · Course · 1-4 Weeks

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      Copenhagen Business School

      AI and the Illusion of Intelligence

      Beginner · Course · 1-4 Weeks

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      Coursera Project Network

      Tweet Emotion Recognition with TensorFlow

      習得できるスキル: Applied Machine Learning, Computer Programming, Deep Learning, Machine Learning, Natural Language Processing, Python Programming, Statistical Programming, Tensorflow

      4.6

      (131件のレビュー)

      Intermediate · Guided Project · Less Than 2 Hours

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      Coursera Project Network

      Generate Synthetic Images with DCGANs in Keras

      習得できるスキル: Deep Learning, Machine Learning, Computer Vision, Tensorflow

      4.5

      (247件のレビュー)

      Advanced · Guided Project · Less Than 2 Hours

    deep learningに関連する検索

    deep learning specialization
    deep learning andrew ng
    deep learning with pytorch : image segmentation
    deep learning for healthcare
    deep learning with pytorch : siamese network
    deep learning for business
    deep learning with pytorch : object localization
    deep learning with pytorch : generative adversarial network
    1234…58

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

    • CertNexus Certified Artificial Intelligence Practitioner: CertNexus
    • Microsoft Azure Data Scientist Associate (DP-100): Microsoft
    • TensorFlow: Data and Deployment: DeepLearning.AI
    • Machine Learning: University of Washington
    • Deep Learning for Healthcare: University of Illinois at Urbana-Champaign
    • الذكاء الاصطناعي للجميع: DeepLearning.AI
    • Deep Learning for Business: Yonsei University
    • Math for AI beginner part 1 Linear Algebra: Korea Advanced Institute of Science and Technology(KAIST)
    • Introduction to AI in the Data Center: NVIDIA
    • AI and the Illusion of Intelligence: Copenhagen Business School

    Machine Learningで学べるスキル

    Pythonプログラミング (33)
    TensorFlow (32)
    ディープラーニング (30)
    人工ニューラルネットワーク (24)
    ビッグデータ (18)
    統計的分類 (17)
    強化学習 (13)
    代数 (10)
    ベイズ (10)
    線型代数学 (10)
    線形回帰 (9)
    NumPy (9)

    ディープラーニングに関するよくある質問

    • Deep learning is a powerful application of machine learning (ML) algorithms modeled after biological systems of information processing called artificial neural networks (ANN). Machine learning is an artificial intelligence (AI) technique that allows computers to automatically learn from data without explicit programming, and deep learning harnesses multiple layers of interconnected neural networks to generate more sophisticated insights.

      While this field of computer science is quite new, it is already being used in a growing range of important applications. Deep learning excels at automated image recognition, also known as computer vision, which is used for creating accurate facial recognition systems and safely driving autonomous vehicles. This approach is also used for speech recognition and natural language processing (NLP) applications, which allow for computers to interact with human users via voice commands.

      Machine learning algorithms such as logistic regression are key to creating deep learning applications, along with commonly used programming languages such as Tensorflow and Python. These programming languages are generally preferred for teaching and learning in this field due to their flexibility and relative accessibility - an important priority given the relevance of deep learning to a wide range of professionals without a computer science background.‎

    • A familiarity with the capabilities and development process for deep learning applications can be an asset in a growing number of careers. For example, the use of deep learning is being explored in healthcare for automatic reading of radiology images, as well as searching for patterns in genes and pharmaceutical interactions that can aid in the discovery of new types of medicines. In many fields, even a basic understanding of deep learning can help professionals identify new potential applications of this powerful technology.

      Those with a deeper expertise in deep learning may become computer research scientists in this field, responsible for inventing new algorithms and finding new applications for these techniques. Given the wide range of uses for deep learning, computer scientists in this field are in high demand for jobs at private companies as well as government agencies and research universities. According to the Bureau of Labor Statistics, computer research scientists earned a median annual salary of $122,840 as of 2019, and these jobs are expected to grow much faster than average.‎

    • Certainly - in fact, Coursera is one of the best places to learn about deep learning. Through partnerships with deeplearning.ai and Stanford University, Coursera offers courses as well as Specializations taught by some of the pioneering thinkers and educators in this field. You can also learn via courses and Specializations from industry leaders such as Google Cloud and Intel, or get a professional certificate from IBM. Guided Projects also offer an opportunity to build skills in deep learning through hands-on tutorials led by experienced instructors, allowing you to learn with confidence.‎

    • The skills or experience you may need to have before studying deep learning, and which can help you better understand an advanced concept such as deep learning, can include sign language reading, music generation, and natural language processing (NLP), in addition to many others. If you have knowledge of Python 3 and understand the basic concepts of general machine-learning algorithms and deep learning, you may have the necessary skills to learn this specialization. You may also want to know about probability and statistics to study deep learning concepts. Basic math, such as algebra and calculus, is also an important prerequisite to deep learning because it relates to machine learning and data science. Also, if you have worked in the tech or artificial intelligence (AI) fields, you may have the necessary experience to study deep learning.‎

    • The type of person who is best suited to study deep learning is someone comfortable working with statistics, programming, advanced calculus, advanced algebra, and engineering. Deep learning benefits someone passionate about working in the AI fields which can create types of deep learning networks that help machines perform human functions. A person best suited to learn about deep learning has a vested interest in understanding how the intelligence is built to run everything from driverless cars, mobile devices, stock trading systems, and robotic surgery equipment, for example. Deep learning benefits someone with a goal of working with systems such as computer vision, speech recognition, NLP, audio recognition bioinformatics systems, and medical image analysis.‎

    • Deep learning may be right for you if you want to break into AI. The specialization may benefit you if you are a machine learning researcher or practitioner who is seeking to learn the next generation of machine learning, and you want to develop practical skills in the popular deep learning framework TensorFlow. Deep learning is one of the most highly sought-after skills in tech, and mastering it may lead you to many opportunities in the field of AI. It may also benefit you if you want to learn how to build neural networks and how to lead successful machine learning projects, and if you have a passion for learning about convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and how to master concepts in Python and TensorFlow.‎

    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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