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    348 Ergebnisse für „neural networks“

    • DeepLearning.AI

      DeepLearning.AI

      Deep Learning

      Kompetenzen, die Sie erwerben: Algorithms, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Big Data, Communication, Computational Logic, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Convolutional Neural Network, Data Management, Deep Learning, Entrepreneurship, General Statistics, Hardware Design, Human Computer Interaction, Interactive Design, Linear Algebra, Machine Learning, Machine Learning Algorithms, Markov Model, Mathematical Optimization, Mathematical Theory & Analysis, Mathematics, Modeling, Natural Language Processing, Probability & Statistics, Python Programming, Regression, Statistical Machine Learning, Statistical Programming, Strategy and Operations, Theoretical Computer Science

      4.8

      (133k Bewertungen)

      Intermediate · Specialization

    • DeepLearning.AI

      DeepLearning.AI

      Neural Networks and Deep Learning

      Kompetenzen, die Sie erwerben: General Statistics, Algorithms, Python Programming, Bayesian Statistics, Computational Logic, Mathematics, Artificial Neural Networks, Regression, Mathematical Theory & Analysis, Computer Programming, Markov Model, Deep Learning, Computer Architecture, Linear Algebra, Hardware Design, Machine Learning Algorithms, Machine Learning, Probability & Statistics, Theoretical Computer Science

      4.9

      (113.9k Bewertungen)

      Intermediate · Course

    • Coursera Project Network

      Coursera Project Network

      Diabetes Disease Detection with XG-Boost and Neural Networks

      Beginner · Guided Project

    • IBM

      IBM

      Introduction to Deep Learning & Neural Networks with Keras

      Kompetenzen, die Sie erwerben: Convolutional Neural Network, Statistical Programming, Artificial Neural Networks, Python Programming, Computer Programming, Machine Learning, Algorithms, Deep Learning, Keras, Theoretical Computer Science

      4.7

      (1.1k Bewertungen)

      Intermediate · Course

    • DeepLearning.AI

      DeepLearning.AI

      AI For Everyone

      Kompetenzen, die Sie erwerben: Artificial Neural Networks, Machine Learning, Deep Learning, Machine Learning Algorithms

      4.8

      (35.9k Bewertungen)

      Beginner · Course

    • DeepLearning.AI

      DeepLearning.AI

      Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

      Kompetenzen, die Sie erwerben: Statistical Machine Learning, Mathematics, Data Management, General Statistics, Python Programming, Algorithms, Tensorflow, Computer Programming, Statistical Programming, Applied Machine Learning, Machine Learning, Mathematical Theory & Analysis, Hyperparameter, Deep Learning, Probability & Statistics, Mathematical Optimization, Artificial Neural Networks, Big Data, Theoretical Computer Science

      4.9

      (60.8k Bewertungen)

      Intermediate · Course

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      UNSW Sydney (The University of New South Wales)

      UNSW Sydney (The University of New South Wales)

      Remote Sensing Image Acquisition, Analysis and Applications

      Kompetenzen, die Sie erwerben: Algorithms, Regression, Mathematics, Business Psychology, Human Resources, Artificial Neural Networks, Computer Vision, Data Visualization, Linear Algebra, Correlation And Dependence, Strategy and Operations, Computer Graphic Techniques, Computer Graphics, Machine Learning Algorithms, Probability & Statistics, Theoretical Computer Science, Machine Learning

      4.6

      (66 Bewertungen)

      Intermediate · Course

    • Placeholder

      Kostenlos

      University of Washington

      University of Washington

      Computational Neuroscience

      Kompetenzen, die Sie erwerben: Python Programming, Statistical Programming, Mathematics, Data Analysis, Neuroscience, Computer Programming, Reinforcement Learning, Marketing, Artificial Neural Networks, Probability & Statistics, Communication, Linear Algebra, Machine Learning, Data Analysis Software, Deep Learning, Other Programming Languages, Modeling, Machine Learning Algorithms

      4.6

      (935 Bewertungen)

      Beginner · Course

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      CertNexus

      CertNexus

      Build Decision Trees, SVMs, and Artificial Neural Networks

      Kompetenzen, die Sie erwerben: Machine Learning

      5.0

      (7 Bewertungen)

      Intermediate · Course

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      IBM

      IBM

      Deep Neural Networks with PyTorch

      Kompetenzen, die Sie erwerben: Algorithms, Python Programming, Statistical Machine Learning, Mathematics, PyTorch, General Statistics, Convolutional Neural Network, Econometrics, Computer Programming, Machine Learning, Probability Distribution, Computer Vision, Artificial Neural Networks, Regression, Computer Graphic Techniques, Machine Learning Algorithms, Probability & Statistics, Theoretical Computer Science, Computer Graphics, Deep Learning

      4.4

      (1.1k Bewertungen)

      Intermediate · Course

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      DeepLearning.AI

      DeepLearning.AI

      Convolutional Neural Networks

      Kompetenzen, die Sie erwerben: Convolutional Neural Network, Python Programming, Machine Learning, Applied Machine Learning, Statistical Programming, Artificial Neural Networks, Computer Programming, Computer Vision, Computer Graphic Techniques, Deep Learning, Computer Graphics

      4.9

      (40.5k Bewertungen)

      Intermediate · Course

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      DeepLearning.AI

      DeepLearning.AI

      Convolutional Neural Networks in TensorFlow

      Kompetenzen, die Sie erwerben: Statistical Classification, Computer Vision, Tensorflow, Statistical Machine Learning, Statistical Programming, Convolutional Neural Network, Python Programming, Applied Machine Learning, Computer Programming, Artificial Neural Networks, Machine Learning, Computer Graphic Techniques, Computer Graphics, Deep Learning, Machine Learning Algorithms

      4.7

      (7.4k Bewertungen)

      Intermediate · Course

    Suchanfragen mit Bezug zu 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…29

    Zusammenfassend sind hier 10 unserer beliebtesten neural networks Kurse

    • Deep Learning: DeepLearning.AI
    • Neural Networks and Deep Learning: DeepLearning.AI
    • Diabetes Disease Detection with XG-Boost and Neural Networks: Coursera Project Network
    • Introduction to Deep Learning & Neural Networks with Keras: IBM
    • AI For Everyone: DeepLearning.AI
    • Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization: DeepLearning.AI
    • Remote Sensing Image Acquisition, Analysis and Applications: UNSW Sydney (The University of New South Wales)
    • Computational Neuroscience: University of Washington
    • Build Decision Trees, SVMs, and Artificial Neural Networks: CertNexus
    • Deep Neural Networks with PyTorch: IBM

    Fähigkeiten, die Sie bei Machine Learning erlernen können

    Python-Programmierung (33)
    TensorFlow (32)
    Deep Learning (30)
    Künstliches Neuronales Netz (24)
    Big Data (18)
    Statistische Klassifikation (17)
    Verstärkungslernen (13)
    Algebra (10)
    Bayes (10)
    Lineare Algebra (10)
    Lineare Regression (9)
    Numpy (9)

    Häufig gestellte Fragen zum Thema Neuronale Netze

    • 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.‎

    Diese häufig gestellten Fragen dienen nur zu Informationszwecken. Den Lernenden wird empfohlen, eingehender zu recherchieren, ob Kurse und andere angestrebte Qualifikationen wirklich ihren persönlichen, beruflichen und finanziellen Vorstellungen entsprechen.
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