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    • Neural Networks

    필터링 기준

    "neural networks"에 대한 490개의 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

      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, 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

      (7.8k개의 검토)

      Beginner · Specialization · 1-3 Months

    • 무료

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      Sungkyunkwan University

      Fundamentals of CNNs and RNNs

      획득할 기술: Deep Learning, Machine Learning

      4.3

      (14개의 검토)

      Beginner · Course · 1-3 Months

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      University of Colorado Boulder

      Master of Science in Electrical Engineering

      획득할 기술: Theoretical Computer Science, Probability & Statistics, General Statistics, Algorithms, Data Management, Mathematics, Computer Architecture, Strategy and Operations, Databases, Leadership and Management, Communication, Hardware Design, Statistical Programming, Machine Learning, Operating Systems, Research and Design, Finance, Data Analysis, Computer Programming, SQL, Writing, Regression, Business Analysis, Data Structures, Project Management, Software Engineering, Business Communication, Probability Distribution, Database Design, Entrepreneurship, Computer Graphics, Computer Networking, Design and Product, Statistical Analysis, Statistical Tests, Accounting, Systems Design, Human Computer Interaction, Data Model, Database Administration, Database Application, Database Theory, Machine Learning Algorithms, Statistical Machine Learning, Estimation, Data Visualization, Problem Solving, Operations Research, Internet Of Things, Network Architecture, Applied Mathematics, Computer Vision, Graph Theory, PostgreSQL, Deep Learning, Geometry, User Experience, Marketing, Computer Graphic Techniques, Mathematical Theory & Analysis, Programming Principles, Python Programming, Supply Chain and Logistics, Algebra, Cryptography, Business Psychology, Interactive Design, Critical Thinking, Security Engineering, Data Mining, Correlation And Dependence, Distributed Computing Architecture, Financial Analysis, Linear Algebra, User Experience Design, Differential Equations, Linux, Cloud Computing, Cyberattacks, Computer Programming Tools, Computational Logic, Scrum (Software Development), Applied Machine Learning, Calculus, Cost Accounting, Econometrics, Feature Engineering, Graphic Design, Investment Management, Other Programming Languages, Sales, Software Architecture, Software Testing, System Programming, Visual Design, Artificial Neural Networks, Emotional Intelligence, Market Analysis, NoSQL, Statistical Visualization, Data Warehousing, Strategy, Basic Descriptive Statistics, Budget Management, Computational Thinking, Data Analysis Software, Experiment, Exploratory Data Analysis, Material Handling, Organizational Development, Product Lifecycle, Risk Management, Amazon Web Services, Big Data, Cloud Platforms, Culture, Decision Making, Graphics Software, Human Resources, Microarchitecture, Security Strategy, Application Development, Computer Security Models, Network Model, Operational Analysis, Product Design, Reinforcement Learning, Software Security, System Security, User Research, Plot (Graphics), R Programming, Account Management, Banking, BlockChain, Business Process Management, C Programming Language Family, Data Architecture, FinTech, Financial Accounting, Financial Management, Geovisualization, Inventory Management, Management Accounting, Markov Model, Matlab, Natural Language Processing, Operations Management, Planning, Product Management, Spreadsheet Software, Storytelling, Supplier Relationship Management, Computer Science, Computer Security Incident Management, Data Science, Dimensionality Reduction, Forecasting, Journalism, Leadership Development, Network Analysis, Network Security, System Software

      학위 취득

      Degree · 1-4 Years

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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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      무료

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      University of Washington

      Computational Neuroscience

      획득할 기술: Data Science, Machine Learning, Artificial Neural Networks, Python Programming, Statistical Programming, Matlab, Reinforcement Learning, Business Psychology, Communication, Computer Networking, Computer Programming, Data Analysis, Data Analysis Software, Deep Learning, Entrepreneurship, General Statistics, Linear Algebra, Machine Learning Algorithms, Mathematics, Network Model, Probability & Statistics, Probability Distribution

      4.6

      (1k개의 검토)

      Beginner · Course · 1-3 Months

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      무료

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      Edge Impulse

      Introduction to Embedded Machine Learning

      획득할 기술: Applied Machine Learning, Machine Learning, Machine Learning Algorithms, Computer Programming

      4.8

      (477개의 검토)

      Intermediate · Course · 1-4 Weeks

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      DeepLearning.AI

      AI For Everyone

      획득할 기술: Machine Learning, Business Analysis, Applied Machine Learning, Business Transformation, Data Analysis, Data Model, Deep Learning, Exploratory Data Analysis, Forecasting, Human Computer Interaction, Natural Language Processing, People Analysis, Probability & Statistics, Reinforcement Learning, Statistical Analysis, Artificial Neural Networks, Machine Learning Algorithms

      4.8

      (37.6k개의 검토)

      Beginner · Course · 1-4 Weeks

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      CertNexus

      Build Decision Trees, SVMs, and Artificial Neural Networks

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

      5.0

      (7개의 검토)

      Intermediate · Course · 1-3 Months

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      IBM Skills Network

      Introduction to Deep Learning & Neural Networks with Keras

      획득할 기술: Artificial Neural Networks, Computer Programming, Deep Learning, Machine Learning, Statistical Programming, Algorithms, Mathematics, Probability & Statistics, Python Programming, Theoretical Computer Science

      4.7

      (1.2k개의 검토)

      Intermediate · Course · 1-3 Months

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      DeepLearning.AI

      Convolutional Neural Networks

      획득할 기술: Artificial Neural Networks, Computer Vision, Deep Learning, Machine Learning, Statistical Programming, Python Programming, Applied Machine Learning, Linear Algebra, Machine Learning Algorithms, Machine Learning Software, Statistical Machine Learning, Dimensionality Reduction, Feature Engineering, Computer Programming, Tensorflow, Computer Architecture, Computer Networking, Network Architecture, Computer Graphic Techniques, Computer Graphics, Data Visualization

      4.9

      (41.2k개의 검토)

      Intermediate · Course · 1-4 Weeks

    neural networks과(와) 관련된 검색

    neural networks and deep learning
    neural networks and random forests
    convolutional neural networks
    deep neural networks with pytorch
    convolutional neural networks in tensorflow
    improving deep neural networks: hyperparameter tuning, regularization and optimization
    introduction to deep learning & neural networks with keras
    classify images with tensorflow convolutional neural networks
    1234…41

    요약하자면, 여기에 가장 인기 있는 neural networks 강좌 10개가 있습니다.

    • Deep Learning: DeepLearning.AI
    • Neural Networks and Deep Learning: DeepLearning.AI
    • Machine Learning: DeepLearning.AI
    • Fundamentals of CNNs and RNNs: Sungkyunkwan University
    • Master of Science in Electrical Engineering: University of Colorado Boulder
    • IBM AI Engineering: IBM Skills Network
    • Computational Neuroscience: University of Washington
    • Introduction to Embedded Machine Learning: Edge Impulse
    • AI For Everyone: DeepLearning.AI
    • Build Decision Trees, SVMs, and Artificial Neural Networks: CertNexus

    Machine Learning에서 학습할 수 있는 스킬

    Python 프로그래밍 (33)
    TensorFlow (32)
    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)

    신경망에 대한 자주 묻는 질문

    • Neural networks, also known as neural nets or artificial neural networks (ANN), are machine learning algorithms organized in networks that mimic the functioning of neurons in the human brain. Using this biological neuron model, these systems are capable of unsupervised learning from massive datasets.

      This is an important enabler for artificial intelligence (AI) applications, which are used across a growing range of tasks including image recognition, natural language processing (NLP), and medical diagnosis. The related field of deep learning also relies on neural networks, typically using a convolutional neural network (CNN) architecture that connects multiple layers of neural networks in order to enable more sophisticated applications.

      For example, using deep learning, a facial recognition system can be created without specifying features such as eye and hair color; instead, the program can simply be fed thousands of images of faces and it will learn what to look for to identify different individuals over time, in much the same way that humans learn. Regardless of the end-use application, neural networks are typically created in TensorFlow and/or with Python programming skills.‎

    • Neural networks are a fundamental concept to understand for jobs in artificial intelligence (AI) and deep learning. And, as the number of industries seeking to leverage these approaches continues to grow, so do career opportunities for professionals with expertise in neural networks. For instance, these skills could lead to jobs in healthcare creating tools to automate X-ray scans or assist in drug discovery, or a job in the automotive industry developing autonomous vehicles.

      Professionals dedicating their careers to cutting-edge work in neural networks typically pursue a master’s degree or even a doctorate in computer science. This high-level expertise in neural networks and artificial intelligence are in high demand; according to the Bureau of Labor Statistics, computer research scientists earn a median annual salary of $122,840 per year, and these jobs are projected to grow much faster than average over the next decade.‎

    • Absolutely - in fact, Coursera is one of the best places to learn about neural networks, online or otherwise. You can take courses and Specializations spanning multiple courses in topics like neural networks, artificial intelligence, and deep learning from pioneers in the field - including deeplearning.ai and Stanford University. Coursera has also partnered with industry leaders such as IBM, Google Cloud, and Amazon Web Services to offer courses that can lead to professional certificates in applied AI and other areas. You can even learn about neural networks with hands-on Guided Projects, a way to learn on Coursera by completing step-by-step tutorials led by experienced instructors.‎

    • Before starting to learn neural networks, it's important to have experience creating and using algorithms since neural networks run on complicated algorithms. You should also have fundamental math skills at least, but you'll be at a better advantage if you have knowledge of linear algebra, calculus, statistics, and probability. Being proficient at problem-solving is also important before starting to learn neural networks. An understanding of how the human brain processes information is helpful since artificial neural networks are patterned after how the brain works. You'll also benefit from having experience using any programming language, in particular Java, R, Python, or C++. This includes experience using these languages' libraries, which you'll access to apply the algorithms used in neural networks.‎

    • People who are best suited for roles in neural networks are innovative, interested in technology, and have the ability to identify patterns in large amounts of data and draw conclusions from them. People who have a desire to make life and work easier for human beings through artificial technology are well suited for roles in neural networks too. Also, people who have good programming skills and data engineering skills like SQL, data analysis, ETL, and data visualization are likely well suited for roles in neural networks.‎

    • If you are interested in the field of artificial intelligence, learning about neural networks is right for you. If your current or future position involves data analysis, pattern recognition, optimization, forecasting, or decision-making, you might also benefit from learning neural networks. Neural networks are also used in image recognition software, speech synthesis, self-driving vehicles, navigation systems, industrial robots, and algorithms for protecting information systems, so if you're interested in these technologies, learning neural networks may be helpful to you.‎

    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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