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    • Neural Networks

    필터링 기준

    "neural networks"에 대한 355개의 결과

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      DeepLearning.AI

      Deep Learning

      획득할 기술: Advertising, Algorithms, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Big Data, Business Psychology, Communication, Computational Logic, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Networking, Computer Programming, Computer Vision, Data Management, Decision Making, 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 Optimization, Mathematical Theory & Analysis, Mathematics, Natural Language, Natural Language Processing, Network Architecture, Network Model, Object Detection, 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, Tensorflow, Theoretical Computer Science, User Experience

      4.8

      (134.3k개의 검토)

      Intermediate · Specialization · 3-6 Months

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      DeepLearning.AI

      Neural Networks and Deep Learning

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

      4.9

      (114.8k개의 검토)

      Intermediate · Course · 1-4 Weeks

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      DeepLearning.AI, Stanford University

      Machine Learning

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

      4.9

      (2.3k개의 검토)

      Beginner · Specialization · 1-3 Months

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      IBM Skills Network

      Advanced Data Science with IBM

      획득할 기술: Algorithms, Apache, Applied Machine Learning, Artificial Neural Networks, Basic Descriptive Statistics, Bayesian Statistics, Big Data, Change Management, Cloud Computing, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Correlation And Dependence, Data Analysis, Data Management, Data Model, Data Structures, Data Visualization, Databases, Deep Learning, Dimensionality Reduction, Distributed Computing Architecture, Econometrics, Estimation, Experiment, Extract, Transform, Load, General Statistics, IBM Cloud, Leadership and Management, Machine Learning, Machine Learning Algorithms, Natural Language Processing, Plot (Graphics), Probability & Statistics, Probability Distribution, Programming Principles, Python Programming, Regression, SQL, Statistical Machine Learning, Statistical Programming, Statistical Visualization, Strategy and Operations, Tensorflow, Theoretical Computer Science

      4.3

      (2.9k개의 검토)

      Advanced · Specialization · 3-6 Months

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      IBM Skills Network

      Introduction to Deep Learning & Neural Networks with Keras

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

      4.7

      (1.1k개의 검토)

      Intermediate · Course · 1-3 Months

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      DeepLearning.AI

      AI For Everyone

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

      4.8

      (36.3k개의 검토)

      Beginner · Course · 1-4 Weeks

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      DeepLearning.AI

      Convolutional Neural Networks

      획득할 기술: Deep Learning, Convolutional Neural Network, Keras, Tensorflow, Computer Networking, Object Detection, Python Programming, Machine Learning, Artificial Neural Networks, Computer Architecture, Computer Programming, Computer Graphics, Computer Graphic Techniques, Computer Vision, Network Architecture, Applied Machine Learning, Statistical Programming

      4.9

      (40.6k개의 검토)

      Intermediate · Course · 1-4 Weeks

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      Johns Hopkins University

      Advanced Statistics for Data Science

      획득할 기술: Algebra, Artificial Neural Networks, Bayesian Statistics, Biostatistics, Calculus, Communication, Dimensionality Reduction, Econometrics, Experiment, General Statistics, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematics, Probability & Statistics, Probability Distribution, Regression, Statistical Machine Learning, Statistical Tests

      4.4

      (660개의 검토)

      Advanced · Specialization · 3-6 Months

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      DeepLearning.AI

      DeepLearning.AI TensorFlow Developer

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

      4.7

      (22.2k개의 검토)

      Intermediate · Professional Certificate · 3-6 Months

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      University of Colorado Boulder

      Deep Learning Applications for Computer Vision

      획득할 기술: Machine Learning, Computer Vision, Deep Learning

      4.7

      (30개의 검토)

      Intermediate · Course · 1-3 Months

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      Coursera Project Network

      Predicting the Weather with Artificial Neural Networks

      획득할 기술: Artificial Neural Networks, Machine Learning

      3.9

      (10개의 검토)

      Intermediate · Guided Project · Less Than 2 Hours

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      Coursera Project Network

      Hyperparameter Tuning with Neural Network Intelligence

      획득할 기술: Artificial Neural Networks, Choice, Machine Learning

      4.7

      (37개의 검토)

      Intermediate · Guided Project · Less Than 2 Hours

    neural networks과(와) 관련된 검색

    neural networks and deep learning
    neural networks and random forests
    convolutional neural networks
    convolutional neural networks in tensorflow
    deep neural networks with pytorch
    improving deep neural networks: hyperparameter tuning, regularization and optimization
    introduction to deep learning & neural networks with keras
    predicting the weather with artificial neural networks
    1234…30

    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
    • Advanced Data Science with IBM: IBM Skills Network
    • Introduction to Deep Learning & Neural Networks with Keras: IBM Skills Network
    • AI For Everyone: DeepLearning.AI
    • Convolutional Neural Networks: DeepLearning.AI
    • Advanced Statistics for Data Science: Johns Hopkins University
    • DeepLearning.AI TensorFlow Developer: DeepLearning.AI
    • Deep Learning Applications for Computer Vision: University of Colorado Boulder

    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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