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    • Deep Learning

    필터링 기준

    "deep learning"에 대한 684개의 결과

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      DeepLearning.AI

      Deep Learning

      획득할 기술: Deep Learning, Machine Learning, Artificial Neural Networks, Python Programming, Statistical Programming, Machine Learning Algorithms, Linear Algebra, Applied Machine Learning, Statistical Machine Learning, Dimensionality Reduction, Feature Engineering, Probability & Statistics, Business Psychology, Entrepreneurship, Machine Learning Software, Computer Vision, Marketing, General Statistics, Natural Language Processing, Computer Programming, Leadership and Management, Project Management, Regression, Sales, Strategy, Strategy and Operations, Tensorflow, Differential Equations, Mathematics, Applied Mathematics, Decision Making, Supply Chain Systems, Supply Chain and Logistics, Advertising, Communication, Estimation, Forecasting, Mathematical Theory & Analysis, Statistical Visualization, Algorithms, Theoretical Computer Science, Bayesian Statistics, Calculus, Probability Distribution, Statistical Tests, Big Data, Computer Architecture, Computer Networking, Data Management, Human Computer Interaction, Network Architecture, User Experience, Algebra, Computational Logic, Computer Graphic Techniques, Computer Graphics, Data Structures, Data Visualization, Hardware Design, Interactive Design, Markov Model, Network Model

      4.8

      (137.7k개의 검토)

      Intermediate · Specialization · 3-6 Months

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      DeepLearning.AI, Stanford University

      Machine Learning

      획득할 기술: Machine Learning, Probability & Statistics, Machine Learning Algorithms, General Statistics, Theoretical Computer Science, Algorithms, Applied Machine Learning, Artificial Neural Networks, Regression, Econometrics, Computer Programming, Deep Learning, Python Programming, Statistical Programming, Mathematics, Tensorflow, Data Management, Data Structures, Statistical Machine Learning, Reinforcement Learning, Probability Distribution, Mathematical Theory & Analysis, Data Analysis, Data Mining, Linear Algebra, Computer Vision, Calculus, Feature Engineering, Bayesian Statistics, Operations Research, Research and Design, Strategy and Operations, Computational Logic, Accounting, Communication

      4.9

      (7.7k개의 검토)

      Beginner · Specialization · 1-3 Months

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      DeepLearning.AI

      Neural Networks and Deep Learning

      획득할 기술: Artificial Neural Networks, Deep Learning, Machine Learning, Machine Learning Algorithms, Python Programming, Linear Algebra, Regression, General Statistics, Probability & Statistics, Business Psychology, Computer Programming, Dimensionality Reduction, Entrepreneurship, Feature Engineering, Statistical Programming, Supply Chain Systems, Supply Chain and Logistics, Applied Machine Learning, Mathematics, Statistical Machine Learning, Machine Learning Software, Bayesian Statistics, Statistical Tests, Algebra, Algorithms, Computational Logic, Computer Architecture, Computer Networking, Data Structures, Estimation, Hardware Design, Markov Model, Mathematical Theory & Analysis, Network Model, Theoretical Computer Science

      4.9

      (117.3k개의 검토)

      Intermediate · Course · 1-4 Weeks

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      DeepLearning.AI

      Natural Language Processing

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

      4.6

      (4.9k개의 검토)

      Intermediate · Specialization · 3-6 Months

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      DeepLearning.AI

      DeepLearning.AI TensorFlow Developer

      획득할 기술: Machine Learning, Deep Learning, Tensorflow, Artificial Neural Networks, Data Science, Computer Vision, Computer Programming, General Statistics, Natural Language Processing, Probability & Statistics, Python Programming, Statistical Programming, Business Psychology, Entrepreneurship, Forecasting, Machine Learning Algorithms, Communication, Marketing, Applied Machine Learning, Programming Principles, Computer Graphic Techniques, Computer Graphics, Machine Learning Software, Statistical Machine Learning

      4.7

      (23.1k개의 검토)

      Intermediate · Professional Certificate · 3-6 Months

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      Imperial College London

      TensorFlow 2 for Deep Learning

      획득할 기술: Machine Learning, Tensorflow, Deep Learning, Computer Programming, Python Programming, Statistical Programming, Applied Machine Learning, Artificial Neural Networks, Computer Vision, Machine Learning Algorithms, Probability & Statistics, Data Visualization, Bayesian Statistics, Natural Language Processing, Probability Distribution, Advertising, Communication, Marketing, Operations Research, Research and Design

      4.8

      (627개의 검토)

      Intermediate · Specialization · 3-6 Months

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      DeepLearning.AI

      Machine Learning Engineering for Production (MLOps)

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

      4.7

      (2.7k개의 검토)

      Advanced · Specialization · 3-6 Months

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      Imperial College London

      Mathematics for Machine Learning

      획득할 기술: Mathematics, Algebra, Linear Algebra, Machine Learning, Python Programming, Probability & Statistics, General Statistics, Calculus, Computer Programming, Applied Mathematics, Mathematical Theory & Analysis, Statistical Programming, Algorithms, Dimensionality Reduction, Regression, Theoretical Computer Science, Basic Descriptive Statistics, Data Analysis, Probability Distribution, Artificial Neural Networks, Computer Graphic Techniques, Computer Graphics, Computer Networking, Deep Learning, Differential Equations, Experiment, Machine Learning Algorithms, Network Model

      4.6

      (13.4k개의 검토)

      Beginner · Specialization · 3-6 Months

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      IBM Skills Network

      IBM AI Engineering

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

      4.6

      (15.8k개의 검토)

      Intermediate · Professional Certificate · 3-6 Months

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      University of Alberta

      Reinforcement Learning

      획득할 기술: Machine Learning, Reinforcement Learning, Artificial Neural Networks, Entrepreneurship, Mathematics, Machine Learning Algorithms, Python Programming, Statistical Programming, Business Psychology, Computer Programming, Markov Model, Theoretical Computer Science, Algorithms, Deep Learning, Leadership and Management, Planning, Software Architecture, Software Engineering, Supply Chain and Logistics, Operations Research, Research and Design, Strategy and Operations

      4.7

      (3.1k개의 검토)

      Intermediate · Specialization · 3-6 Months

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      DeepLearning.AI

      Generative Adversarial Networks (GANs)

      획득할 기술: Machine Learning, Artificial Neural Networks, Deep Learning, Statistical Programming, Machine Learning Algorithms, Python Programming, Machine Learning Software, Computer Vision, Computer Programming, Probability & Statistics, Applied Machine Learning

      4.7

      (1.9k개의 검토)

      Intermediate · Specialization · 1-3 Months

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      University of Colorado Boulder

      Introduction to Deep Learning

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

      3.3

      (6개의 검토)

      Intermediate · Course · 1-3 Months

    deep learning과(와) 관련된 검색

    deep learning specialization
    deep learning andrew ng
    deep learning with pytorch : image segmentation
    deep learning for healthcare
    deep learning for business
    deep learning with pytorch : siamese network
    deep learning with pytorch : generative adversarial network
    deep learning and reinforcement learning
    1234…57

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

    • Deep Learning: DeepLearning.AI
    • Machine Learning: DeepLearning.AI
    • Neural Networks and Deep Learning: DeepLearning.AI
    • Natural Language Processing: DeepLearning.AI
    • DeepLearning.AI TensorFlow Developer: DeepLearning.AI
    • TensorFlow 2 for Deep Learning: Imperial College London
    • Machine Learning Engineering for Production (MLOps): DeepLearning.AI
    • Mathematics for Machine Learning: Imperial College London
    • IBM AI Engineering: IBM Skills Network
    • Reinforcement Learning: University of Alberta

    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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