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온라인 학위경력 찾기기업용 Coursera대학교용
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    • Deep Learning

    필터링 기준

    "deep learning"에 대한 434개의 결과

    • DeepLearning.AI

      DeepLearning.AI

      Deep Learning

      획득할 기술: Advertising, Algorithms, Analysis, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Business Psychology, Communication, Computational Logic, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Networking, Computer Programming, Computer Vision, Deep Learning, Entrepreneurship, General Statistics, Hardware Design, Human Computer Interaction, Interactive Design, Leadership and Management, Linear Algebra, Machine Learning, Machine Learning Algorithms, Marketing, Markov Model, Mathematical Theory & Analysis, Mathematics, Natural Language Processing, Network Architecture, Probability & Statistics, Project, Project Management, Python Programming, Regression, Sales, Statistical Machine Learning, Statistical Programming, Strategy, Strategy and Operations, Supply Chain Systems, Supply Chain and Logistics, Theoretical Computer Science

      4.8

      (133.2k개의 검토)

      Intermediate · Specialization

    • DeepLearning.AI

      DeepLearning.AI

      Neural Networks and Deep Learning

      획득할 기술: Deep Learning, Mathematical Theory & Analysis, Computer Programming, Logistic Regression, Business Psychology, Algorithms, Python Programming, Computer Architecture, Markov Model, Mathematics, Theoretical Computer Science, Entrepreneurship, Probability & Statistics, General Statistics, Bayesian Statistics, Supply Chain and Logistics, Numpy, Artificial Neural Networks, Machine Learning Algorithms, Computational Logic, Supply Chain Systems, Applied Machine Learning, Machine Learning, Hardware Design, Regression, Supply Chain, Linear Algebra

      4.9

      (114.1k개의 검토)

      Intermediate · Course

    • DeepLearning.AI

      DeepLearning.AI

      DeepLearning.AI TensorFlow Developer

      획득할 기술: Analysis, Applied Machine Learning, Artificial Neural Networks, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Deep Learning, Entrepreneurship, Forecasting, General Statistics, Machine Learning, Machine Learning Algorithms, Natural Language Processing, Probability & Statistics, Programming Principles, Python Programming, Statistical Machine Learning, Statistical Programming, Time Series

      4.7

      (21.9k개의 검토)

      Intermediate · Professional Certificate

    • DeepLearning.AI

      DeepLearning.AI

      Natural Language Processing

      획득할 기술: Algorithms, Artificial Neural Networks, Bayesian Statistics, Communication, Computer Graphics, Computer Programming, Deep Learning, Dimensionality Reduction, Experiment, General Statistics, Human Computer Interaction, Logistic Regression, Machine Learning, Machine Learning Algorithms, Markov Model, Mathematics, Modeling, Natural Language, Natural Language Processing, Operations Research, Probability, Probability & Statistics, Python Programming, Regression, Research and Design, Statistical Programming, Strategy and Operations, Supply Chain, Theoretical Computer Science, User Experience

      4.6

      (4.5k개의 검토)

      Intermediate · Specialization

    • DeepLearning.AI

      DeepLearning.AI

      Machine Learning Engineering for Production (MLOps)

      획득할 기술: Applied Machine Learning, Business Analysis, Change Management, Cloud Computing, Computer Networking, Computer Programming, Data Analysis, Data Management, Data Visualization, Deep Learning, DevOps, Estimation, Exploratory Data Analysis, Extract, Transform, Load, Feature Engineering, General Statistics, Leadership and Management, Machine Learning, Machine Learning Algorithms, Modeling, Network Security, Probability & Statistics, Python Programming, Security Engineering, Security Strategy, Statistical Programming, Statistical Visualization, Strategy and Operations

      4.7

      (2k개의 검토)

      Advanced · Specialization

    • DeepLearning.AI

      DeepLearning.AI

      Generative Adversarial Networks (GANs)

      획득할 기술: Applied Machine Learning, Artificial Neural Networks, Computer Programming, Computer Vision, Deep Learning, Machine Learning, Machine Learning Algorithms, Modeling, Python Programming, Statistical Programming

      4.7

      (1.8k개의 검토)

      Intermediate · Specialization

    • Placeholder
      Imperial College London

      Imperial College London

      TensorFlow 2 for Deep Learning

      획득할 기술: Applied Machine Learning, Artificial Neural Networks, Computer Programming, Computer Vision, Deep Learning, Machine Learning, Python Programming, Statistical Programming

      4.8

      (564개의 검토)

      Intermediate · Specialization

    • Placeholder
      Imperial College London

      Imperial College London

      Mathematics for Machine Learning

      획득할 기술: Algebra, Algorithms, Analysis, Artificial Neural Networks, Basic Descriptive Statistics, Calculus, Computer Programming, Data Analysis, Deep Learning, Differential Equations, General Statistics, Linear Algebra, Linear Regression, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Probability Distribution, Python Programming, Regression, Statistical Programming, Theoretical Computer Science

      4.6

      (12.8k개의 검토)

      Beginner · Specialization

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      IBM

      IBM

      IBM AI Engineering

      획득할 기술: Algorithms, Analysis, Apache, Applied Machine Learning, Artificial Neural Networks, Basic Descriptive Statistics, Big Data, Business Analysis, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Correlation And Dependence, Data Analysis, Data Clustering Algorithms, Data Management, Data Structures, Databases, Deep Learning, Dimensionality Reduction, Econometrics, General Statistics, Logistic Regression, Machine Learning, Machine Learning Algorithms, Mathematics, NoSQL, Probability & Statistics, Probability Distribution, Python Programming, Regression, SQL, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Supply Chain Systems, Supply Chain and Logistics, Theoretical Computer Science

      4.6

      (14.6k개의 검토)

      Intermediate · Professional Certificate

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      University of Illinois at Urbana-Champaign

      University of Illinois at Urbana-Champaign

      Deep Learning for Healthcare

      2.2

      (9개의 검토)

      Advanced · Specialization

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      University of Colorado Boulder

      University of Colorado Boulder

      Introduction to Deep Learning

      획득할 기술: Artificial Neural Networks, Deep Learning, Reinforcement Learning, Machine Learning, Applied Machine Learning

      Intermediate · Course

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      University of Colorado Boulder

      University of Colorado Boulder

      Machine Learning: Theory and Hands-on Practice with Python

      획득할 기술: Applied Machine Learning, Artificial Neural Networks, Correlation And Dependence, Data Management, Data Structures, Deep Learning, Machine Learning, Probability & Statistics, Regression, Reinforcement Learning, Statistical Machine Learning, Theoretical Computer Science

      3.4

      (13개의 검토)

      Intermediate · Specialization

    deep learning과(와) 관련된 검색

    deep learning specialization
    deep learning andrew ng
    deep learning with pytorch : image segmentation
    deep learning and reinforcement learning
    deep learning for healthcare
    deep learning with pytorch : build an autoencoder
    deep learning for business
    deep learning with pytorch : siamese network
    1234…37

    요약하자면, 여기에 가장 인기 있는 deep learning 강좌 10개가 있습니다.

    • Deep Learning: DeepLearning.AI
    • Neural Networks and Deep Learning: DeepLearning.AI
    • DeepLearning.AI TensorFlow Developer: DeepLearning.AI
    • Natural Language Processing: DeepLearning.AI
    • Machine Learning Engineering for Production (MLOps): DeepLearning.AI
    • Generative Adversarial Networks (GANs): DeepLearning.AI
    • TensorFlow 2 for Deep Learning: Imperial College London
    • Mathematics for Machine Learning: Imperial College London
    • IBM AI Engineering: IBM
    • Deep Learning for Healthcare: University of Illinois at Urbana-Champaign

    Machine Learning에서 학습할 수 있는 스킬

    Python 프로그래밍 (33)
    TensorFlow (32)
    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)

    심층 학습에 대한 자주 묻는 질문

    • 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.‎

    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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