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    • Tensorflow
    Related topics:신경망응용 통계학심층 학습nlpdeeplearning.aiGoogle

    필터링 기준

    "tensorflow"에 대한 169개의 결과

    • DeepLearning.AI

      DeepLearning.AI

      DeepLearning.AI TensorFlow Developer

      획득할 기술: 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

      (21.7k개의 검토)

      Intermediate · Professional Certificate · 3+ Months

    • Imperial College London

      Imperial College London

      TensorFlow 2 for Deep Learning

      획득할 기술: Applied Machine Learning, Computer Programming, Deep Learning, Machine Learning, Probability & Statistics, Python Programming, Statistical Programming, Tensorflow

      4.8

      (554개의 검토)

      Intermediate · Specialization · 3+ Months

    • DeepLearning.AI

      DeepLearning.AI

      TensorFlow: Advanced Techniques

      획득할 기술: Applied Machine Learning, Artificial Neural Networks, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Deep Learning, Distributed Computing Architecture, Machine Learning, Machine Learning Algorithms, Mathematics, Python Programming, Statistical Programming, Tensorflow

      4.8

      (1k개의 검토)

      Intermediate · Specialization · 3+ Months

    • DeepLearning.AI

      DeepLearning.AI

      Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

      획득할 기술: Computer Graphic Techniques, Convolutional Neural Network, Statistical Programming, Programming Principles, Applied Machine Learning, Computer Programming, Deep Learning, Artificial Neural Networks, Computer Graphics, Machine Learning, Python Programming, Computer Vision, Tensorflow

      4.7

      (17.1k개의 검토)

      Intermediate · Course · 1-4 Weeks

    • DeepLearning.AI

      DeepLearning.AI

      TensorFlow: Data and Deployment

      획득할 기술: Algebra, Android Development, Applied Machine Learning, Computer Architecture, Computer Programming, Data Management, Deep Learning, Human Computer Interaction, Javascript, Machine Learning, Mathematics, Microarchitecture, Mobile Development, Python Programming, Statistical Programming, Tensorflow, Theoretical Computer Science, User Experience, Web Development

      4.6

      (1.2k개의 검토)

      Intermediate · Specialization · 3+ Months

    • Imperial College London

      Imperial College London

      Getting started with TensorFlow 2

      획득할 기술: Keras, Statistical Programming, Deep Learning, Machine Learning, Computer Programming, Tensorflow, Applied Machine Learning, Python Programming

      4.9

      (451개의 검토)

      Intermediate · Course · 1-3 Months

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      Google Cloud

      Google Cloud

      Learning TensorFlow: the Hello World of Machine Learning

      Beginner · Project · Less Than 2 Hours

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      Coursera Project Network

      Coursera Project Network

      Basic Image Classification with TensorFlow

      획득할 기술: Keras, Mathematics, Statistical Programming, Python Programming, Statistical Classification, Machine Learning, Deep Learning, Algebra, Computer Programming, Tensorflow, Applied Machine Learning

      4.6

      (707개의 검토)

      Beginner · Rhyme Project · Less Than 2 Hours

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      University of London

      University of London

      Machine Learning for All

      획득할 기술: Machine Learning

      4.7

      (2.9k개의 검토)

      Beginner · Course · 1-4 Weeks

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      Google Cloud

      Google Cloud

      Preparing for Google Cloud Certification: Machine Learning Engineer

      획득할 기술: Agile Software Development, Algorithms, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Big Data, Business Psychology, Cloud API, Cloud Computing, Cloud Storage, Computational Thinking, Computer Architecture, Computer Networking, Computer Programming, Computer Programming Tools, Data Management, Data Structures, Databases, Deep Learning, DevOps, Distributed Computing Architecture, Econometrics, Entrepreneurship, Extract, Transform, Load, Feature Engineering, Full-Stack Web Development, General Statistics, Geostatistics, Google Cloud Platform, Hardware Design, Kubernetes, Machine Learning, Machine Learning Algorithms, Network Security, Performance Management, Probability & Statistics, Python Programming, Regression, Security Engineering, Security Strategy, Software Architecture, Software Engineering, Statistical Machine Learning, Statistical Programming, Strategy and Operations, Tensorflow, Theoretical Computer Science, Web Development

      4.6

      (23.9k개의 검토)

      Intermediate · Professional Certificate · 3+ Months

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      DeepLearning.AI

      DeepLearning.AI

      Deep Learning

      획득할 기술: Algorithms, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Big Data, Communication, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Convolutional Neural Network, Data Management, Deep Learning, Entrepreneurship, General Statistics, Human Computer Interaction, Interactive Design, Linear Algebra, Machine Learning, Machine Learning Algorithms, 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

      (132.4k개의 검토)

      Intermediate · Specialization · 3+ Months

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      Coursera Project Network

      Coursera Project Network

      Cifar-10 Image Classification with Keras and Tensorflow 2.0

      Beginner · Rhyme Project · Less Than 2 Hours

    tensorflow과(와) 관련된 검색

    tensorflow 2 for deep learning
    tensorflow: advanced techniques
    tensorflow for ai: get to know tensorflow
    tensorflow on google cloud
    tensorflow: data and deployment
    tensorflow serving with docker for model deployment
    tensorflow 2 시작하기
    tensorflow for cnns: learn and practice cnns
    1234…15

    요약하자면, 여기에 가장 인기 있는 tensorflow 강좌 10개가 있습니다.

    • DeepLearning.AI TensorFlow Developer: DeepLearning.AI
    • TensorFlow 2 for Deep Learning: Imperial College London
    • TensorFlow: Advanced Techniques: DeepLearning.AI
    • Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning: DeepLearning.AI
    • TensorFlow: Data and Deployment: DeepLearning.AI
    • Getting started with TensorFlow 2: Imperial College London
    • Learning TensorFlow: the Hello World of Machine Learning: Google Cloud
    • Basic Image Classification with TensorFlow: Coursera Project Network
    • Machine Learning for All: University of London
    • Preparing for Google Cloud Certification: Machine Learning Engineer: Google Cloud

    Machine Learning에서 학습할 수 있는 스킬

    Python 프로그래밍 (33)
    심층 학습 (30)
    인공 신경 회로망 (24)
    빅 데이터 (18)
    통계 분류 (17)
    강화 학습 (13)
    대수학 (10)
    베이지안 (10)
    선형 대수 (10)
    선형 회귀 (9)
    Numpy (9)

    TensorFlow에 대한 자주 묻는 질문

    • TensorFlow is an open-source framework for machine learning (ML) programming originally created by Google Brain, Google’s deep learning and artificial intelligence (AI) research team. It has become one of the most popular software platforms for machine learning due to its flexibility and a comprehensive ecosystem of tools and resources. For example, TensorFlow.js allows for JavaScript-based ML applications that can run in browsers; TensorFlow Lite can run on mobile devices for federated learning applications; and TensorFlow Hub provides an extensive library of reusable ML models.

      The flexibility of TensorFlow and breadth of its machine learning applications have been important in enabling a wide range of uses. TensorFlow is frequently used for computer vision applications, including facial recognition in social media, automatic X-ray scanning in healthcare, and autonomous vehicle driving. Similarly, natural language processing (NLP) applications can understand and respond to spoken and written text, making possible the creation of helpful chatbots and other digital agents as well as the automatic reading and summarization of text. Recommendation engines used by music streaming services and online retailers may also be built in TensorFlow.

      These are all just a few examples of the power of machine learning applications and the ways that TensorFlow can be leveraged to enable them. If you’re interested in pushing the boundaries of this fast-changing field even further, learning TensorFlow is essential.‎

    • Expertise in TensorFlow is an extremely valuable addition to your skillset, and can open the door to many exciting careers. As one of the most popular and useful platforms for machine learning and deep learning applications, TensorFlow skills are in demand from companies throughout the tech world, as well as in the automotive industry, medicine, robotics, and other fields. This high level of demand for skills in TensorFlow and machine learning translates into high levels of pay; according to Glassdoor, machine learning engineers in America earn an average salary of $114,121.‎

    • Absolutely - in fact, Coursera is one of the best places to learn TensorFlow skills online. You can take individual courses as well as Specializations spanning multiple courses from deeplearning.ai, one of the pioneers in the field, or Google Cloud, an industry leader. You can also take courses from top-ranked universities from around the world, including Imperial College London and National Research University Higher School of Economics. Guided Projects from Coursera offer another way to learn, with hands-on Tensorflow tutorials presented by experienced instructors.‎

    • You need to have a basic understanding of Python before starting to learn TensorFlow, so it's best to start with an introductory course to this programming language first. Python is the language used to design TensorFlow. It's also helpful to have knowledge of artificial intelligence (AI) concepts as well. You should have strong math skills, especially in algebra so that you'll be familiar with the calculations and algorithms required in TensorFlow. Foundational knowledge of vectors, scalars, and matrices is also very helpful as you start learning TensorFlow, as well as basic statistics. And it's important to know the basics of machine learning as well.‎

    • People who are best suited for roles in TensorFlow have an interest in machine learning or deep learning. Important soft skills include communication skills, problem-solving skills, time management, teamwork, and a thirst for learning. Someone who uses TensorFlow in their job likely works with a team of professionals like software engineers, research scientists, marketing teams, data scientists, and product teams, so they must be able to communicate clearly, prioritize tasks, and work toward a common goal. And since fields that use TensorFlow—such as AI, machine learning, and deep learning—are constantly evolving, people who adapt well to change and are eager to learn or develop the next new technology are well suited for these roles.‎

    • If you are currently in the machine learning field or aspire to be, learning about TensorFlow is most likely right for you. The same applies if you want to enter the deep learning field in positions like deep learning scientist, deep learning software engineer, or deep learning researcher since TensorFlow is a good starting point for deep learning. If you're in a deep learning internship, learning TensorFlow is right for you as well.‎

    이 FAQ 콘텐츠는 정보 전달 목적만으로 사용할 수 있습니다. 학습자는 과정 및 기타 학점 정보가 개인적, 직업적 및 재정적 목표에 부합하는지 확인하기 위해 추가 조사를 수행하는 것이 좋습니다.
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