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    • Neural Networks

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    494 résultats pour ''neural networks'

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      DeepLearning.AI

      Deep Learning

      Compétences que vous acquerrez : 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

      (137.8k avis)

      Intermediate · Specialization · 3-6 Months

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      DeepLearning.AI, Stanford University

      Machine Learning

      Compétences que vous acquerrez : 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

      (8.1k avis)

      Beginner · Specialization · 1-3 Months

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      DeepLearning.AI

      Neural Networks and Deep Learning

      Compétences que vous acquerrez : 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

      (117.4k avis)

      Intermediate · Course · 1-4 Weeks

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      IBM Skills Network

      IBM AI Engineering

      Compétences que vous acquerrez : 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

      (15.8k avis)

      Intermediate · Professional Certificate · 3-6 Months

    • Gratuit

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      Sungkyunkwan University

      Fundamentals of CNNs and RNNs

      Compétences que vous acquerrez : Deep Learning, Machine Learning

      4.3

      (14 avis)

      Beginner · Course · 1-3 Months

    • Gratuit

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      University of Washington

      Computational Neuroscience

      Compétences que vous acquerrez : 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

      (1k avis)

      Beginner · Course · 1-3 Months

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      CertNexus

      Build Decision Trees, SVMs, and Artificial Neural Networks

      Compétences que vous acquerrez : Machine Learning, Artificial Neural Networks, Machine Learning Algorithms, Deep Learning

      5.0

      (7 avis)

      Intermediate · Course · 1-3 Months

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      Gratuit

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      Edge Impulse

      Introduction to Embedded Machine Learning

      Compétences que vous acquerrez : Applied Machine Learning, Machine Learning, Machine Learning Algorithms, Computer Programming

      4.8

      (479 avis)

      Intermediate · Course · 1-4 Weeks

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      IBM Skills Network

      Introduction to Deep Learning & Neural Networks with Keras

      Compétences que vous acquerrez : Artificial Neural Networks, Computer Programming, Deep Learning, Machine Learning, Statistical Programming, Algorithms, Mathematics, Probability & Statistics, Python Programming, Theoretical Computer Science

      4.7

      (1.2k avis)

      Intermediate · Course · 1-3 Months

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      DeepLearning.AI

      AI For Everyone

      Compétences que vous acquerrez : 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

      (37.6k avis)

      Beginner · Course · 1-4 Weeks

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      DeepLearning.AI

      Convolutional Neural Networks

      Compétences que vous acquerrez : 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

      (41.2k avis)

      Intermediate · Course · 1-4 Weeks

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      DeepLearning.AI

      Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

      Compétences que vous acquerrez : Deep Learning, Machine Learning, Probability & Statistics, Statistical Machine Learning, Statistical Programming, Artificial Neural Networks, Machine Learning Algorithms, Python Programming, General Statistics, Linear Algebra, Applied Machine Learning, Applied Mathematics, Dimensionality Reduction, Feature Engineering, Machine Learning Software, Mathematical Theory & Analysis, Mathematics, Statistical Visualization, Computer Programming, Tensorflow, Algorithms, Calculus, Probability Distribution, Regression, Theoretical Computer Science

      4.9

      (61.8k avis)

      Intermediate · Course · 1-4 Weeks

    Recherches liées à neural networks

    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
    predicting the weather with artificial neural networks
    1234…42

    En résumé, voici 10 de nos cours neural networks les plus populaires

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

    Compétences que vous avez acquises en Machine Learning

    Programmation En Python (33)
    TensorFlow (32)
    Deep Learning (30)
    Réseau De Neurones Artificiels (24)
    Big Data (18)
    Classification Statistique (17)
    Apprentissage Par Renforcement (13)
    Algèbre (10)
    Bayésien (10)
    Algèbre Linéaire (10)
    Régression Linéaire (9)
    NumPy (9)

    Questions fréquentes sur Réseaux de neurones

    • 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.‎

    Le contenu de cette FAQ a été mis à disposition à des fins d'information uniquement. Il est conseillé aux étudiants d'effectuer des recherches supplémentaires afin de s'assurer que les cours et autres qualifications suivis correspondent à leurs objectifs personnels, professionnels et financiers.
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