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Neural Networks 课程

在 Coursera 上探索 100% 在线学位和证书

DeepLearning.AI
DeepLearning.AI TensorFlow DeveloperDeepLearning.AI
Arizona State University
AI and Machine Learning MasterTrack CertificateArizona State University
HSE University
Master of Data and Network AnalyticsHSE University
Arizona State University
Master of Computer ScienceArizona State University
University of Illinois
Master of Computer Science in Data ScienceUniversity of Illinois
Imperial College London
Master of Machine Learning and Data ScienceImperial College London
IBM
IBM AI EngineeringIBM
IBM
IBM Applied AIIBM
CertNexus
CertNexus Certified Artificial Intelligence PractitionerCertNexus
DeepLearning.AI
DeepLearning.AI TensorFlow DeveloperDeepLearning.AI
Arizona State University
AI and Machine Learning MasterTrack CertificateArizona State University
HSE University
Master of Data and Network AnalyticsHSE University
Arizona State University
Master of Computer ScienceArizona State University
University of Illinois
Master of Computer Science in Data ScienceUniversity of Illinois
Imperial College London
Master of Machine Learning and Data ScienceImperial College London
IBM
IBM AI EngineeringIBM
IBM
IBM Applied AIIBM
CertNexus
CertNexus Certified Artificial Intelligence PractitionerCertNexus
DeepLearning.AI
DeepLearning.AI TensorFlow DeveloperDeepLearning.AI
Arizona State University
AI and Machine Learning MasterTrack CertificateArizona State University
HSE University
Master of Data and Network AnalyticsHSE University

“neural networks”共返回 316 条结果

  • Placeholder
    Deep Learning
    DeepLearning.AI
    专项课程
    评分为 4.8(满分 5 星)。116938 条评论
    4.8(116,938)
    990k 名学生
    Intermediate LevelIntermediate
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    Neural Networks and Deep Learning
    DeepLearning.AI
    课程
    评分为 4.9(满分 5 星)。101290 条评论
    4.9(101,290)
    900k 名学生
    Intermediate LevelIntermediate
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    Machine Learning
    Stanford University
    课程
    评分为 4.9(满分 5 星)。154232 条评论
    4.9(154,232)
    3.9 分钟 名学生
    Mixed LevelMixed
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    Advanced Machine Learning
    National Research University Higher School of Economics
    专项课程
    评分为 4.4(满分 5 星)。3679 条评论
    4.4(3,679)
    300k 名学生
    Advanced LevelAdvanced
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    TensorFlow 2 for Deep Learning
    Imperial College London
    专项课程
    评分为 4.9(满分 5 星)。256 条评论
    4.9(256)
    18k 名学生
    Intermediate LevelIntermediate
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    Mathematics for Machine Learning
    Imperial College London
    专项课程
    评分为 4.6(满分 5 星)。10292 条评论
    4.6(10,292)
    250k 名学生
    Beginner LevelBeginner
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    Basic Image Classification with TensorFlow
    Coursera Project Network

    新

    指导项目
    评分为 4.6(满分 5 星)。650 条评论
    4.6(650)
    14k 名学生
    Beginner LevelBeginner
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    Introduction to Machine Learning
    Duke University
    课程
    评分为 4.6(满分 5 星)。998 条评论
    4.6(998)
    49k 名学生
    Intermediate LevelIntermediate
  • Placeholder
    DeepLearning.AI TensorFlow Developer
    DeepLearning.AI
    PROFESSIONAL CERTIFICATE
    评分为 4.7(满分 5 星)。17371 条评论
    4.7(17,371)
    270k 名学生
    Intermediate LevelIntermediate
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    An Introduction to Practical Deep Learning
    Intel
    课程
    评分为 4.3(满分 5 星)。123 条评论
    4.3(123)
    21k 名学生
    Intermediate LevelIntermediate
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    Introduction to Deep Learning & Neural Networks with Keras
    IBM
    课程
    评分为 4.7(满分 5 星)。796 条评论
    4.7(796)
    15k 名学生
    Intermediate LevelIntermediate
  • Placeholder
    AI For Everyone
    DeepLearning.AI
    课程
    评分为 4.8(满分 5 星)。30046 条评论
    4.8(30,046)
    600k 名学生
    Beginner LevelBeginner
  • Placeholder
    Computational Neuroscience
    University of Washington
    课程
    评分为 4.6(满分 5 星)。810 条评论
    4.6(810)
    81k 名学生
    Beginner LevelBeginner
  • Placeholder
    Customising your models with TensorFlow 2
    Imperial College London
    课程
    评分为 5(满分 5 星)。67 条评论
    5(67)
    6.5k 名学生
    Intermediate LevelIntermediate
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    Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
    DeepLearning.AI
    课程
    评分为 4.9(满分 5 星)。56727 条评论
    4.9(56,727)
    380k 名学生
    Beginner LevelBeginner
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    Bayesian Methods for Machine Learning
    National Research University Higher School of Economics
    课程
    评分为 4.5(满分 5 星)。638 条评论
    4.5(638)
    63k 名学生
    Advanced LevelAdvanced
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    Synapses, Neurons and Brains
    Hebrew University of Jerusalem
    课程
    评分为 4.8(满分 5 星)。964 条评论
    4.8(964)
    61k 名学生
    Mixed LevelMixed
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    Probabilistic Graphical Models 1: Representation
    Stanford University
    课程
    评分为 4.7(满分 5 星)。1315 条评论
    4.7(1,315)
    75k 名学生
    Advanced LevelAdvanced
  • Placeholder
    Machine Learning for All
    University of London
    课程
    评分为 4.7(满分 5 星)。2273 条评论
    4.7(2,273)
    79k 名学生
    Beginner LevelBeginner
  • Placeholder
    Convolutional Neural Networks
    DeepLearning.AI
    课程
    评分为 4.9(满分 5 星)。37803 条评论
    4.9(37,803)
    340k 名学生
    Intermediate LevelIntermediate

Searches related to neural networks

neural networks and deep learning

neural networks for machine learning

convolutional neural networks

deep neural networks with pytorch

convolutional neural networks in tensorflow

tensorflow neural networks using deep q-learning techniques

basic artificial neural networks in python

improving deep neural networks: hyperparameter tuning, regularization and optimization

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总之,这是我们最受欢迎的 neural networks 门课程中的 10 门

  • Deep Learning: DeepLearning.AI
  • Neural Networks and Deep Learning: DeepLearning.AI
  • Machine Learning: Stanford University
  • Advanced Machine Learning: National Research University Higher School of Economics
  • TensorFlow 2 for Deep Learning: Imperial College London
  • Mathematics for Machine Learning: Imperial College London
  • Basic Image Classification with TensorFlow: Coursera Project Network
  • Introduction to Machine Learning: Duke University
  • DeepLearning.AI TensorFlow Developer: DeepLearning.AI
  • An Introduction to Practical Deep Learning: Intel

您可以在 Machine Learning 中学到的技能

Python 程序设计 (33)
Tensorflow (32)
深度学习 (30)
人工神经网络 (24)
大数据 (18)
统计分类 (17)
强化学习 (13)
代数 (10)
贝叶斯定理 (10)
线性代数 (10)
线性回归 (9)
Numpy (9)

关于 Neural Networks 的常见问题

  • 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.

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