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  • Pytorch머신러닝라이브러리

Results for "pytorch(머신 러닝 라이브러리)"


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    Duke University

    Introduction to Machine Learning

    Skills you'll gain: PyTorch (Machine Learning Library), Machine Learning Methods, Reinforcement Learning, Deep Learning, Image Analysis, Applied Machine Learning, Natural Language Processing, Machine Learning, Artificial Neural Networks, Supervised Learning, Unsupervised Learning, Python Programming, Computer Vision, Medical Imaging

    4.7
    Rating, 4.7 out of 5 stars
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    3.8K reviews

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
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    Multiple educators

    Machine Learning

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Classification And Regression Tree (CART), Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Tensorflow, Responsible AI, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Python Programming

    4.9
    Rating, 4.9 out of 5 stars
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    37K reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
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    IBM

    IBM AI Engineering

    Skills you'll gain: Prompt Engineering, Apache Spark, Large Language Modeling, PyTorch (Machine Learning Library), Computer Vision, Unsupervised Learning, Generative AI, PySpark, Keras (Neural Network Library), Supervised Learning, Deep Learning, Reinforcement Learning, Regression Analysis, LLM Application, Scikit Learn (Machine Learning Library), Applied Machine Learning, Natural Language Processing, Machine Learning, Python Programming, Data Science

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
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    21K reviews

    Intermediate · Professional Certificate · 3 - 6 Months

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    Northwestern University

    현대 로봇공학, 강좌 6: 캡스톤 프로젝트, 모바일 매니퓰레이션

    Skills you'll gain: Control Systems, Robotic Process Automation, Simulations, Record Keeping, Matlab, Computer Programming, Linear Algebra

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
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    IBM

    Fundamentals of AI Agents Using RAG and LangChain

    Skills you'll gain: Prompt Engineering, LLM Application, Generative AI, Large Language Modeling, PyTorch (Machine Learning Library), Artificial Intelligence and Machine Learning (AI/ML), Natural Language Processing

    4.6
    Rating, 4.6 out of 5 stars
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    174 reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
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    DeepLearning.AI

    Generative Adversarial Networks (GANs)

    Skills you'll gain: Generative AI, Generative Model Architectures, PyTorch (Machine Learning Library), Image Analysis, Deep Learning, Responsible AI, Artificial Neural Networks, Data Ethics, Computer Vision, Machine Learning, Image Quality, Unsupervised Learning, Information Privacy, Data Synthesis, Performance Testing

    4.7
    Rating, 4.7 out of 5 stars
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    2.4K reviews

    Intermediate · Specialization · 1 - 3 Months

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    University of Washington

    머신 러닝: 회귀 모델

    Skills you'll gain: Regression Analysis, Supervised Learning, Machine Learning Algorithms, Machine Learning, Predictive Modeling, Applied Machine Learning, Scikit Learn (Machine Learning Library), Statistical Modeling, Feature Engineering, Python Programming, Linear Algebra, NumPy, Algorithms

    Mixed · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
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    IBM

    IBM Generative AI Engineering

    Skills you'll gain: Prompt Engineering, Exploratory Data Analysis, Data Wrangling, LangChain, Prompt Patterns, Large Language Modeling, Unsupervised Learning, PyTorch (Machine Learning Library), Generative AI, Restful API, Keras (Neural Network Library), Data Transformation, Supervised Learning, Flask (Web Framework), Data Analysis, Data Cleansing, Data Manipulation, Responsible AI, LLM Application, Data Import/Export

    4.6
    Rating, 4.6 out of 5 stars
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    96K reviews

    Beginner · Professional Certificate · 3 - 6 Months

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    Packt

    Machine Learning with PyTorch and Scikit-Learn

    Skills you'll gain: Reinforcement Learning, Dimensionality Reduction, PyTorch (Machine Learning Library), Deep Learning, Generative AI, Pandas (Python Package), Scikit Learn (Machine Learning Library), Python Programming, Machine Learning, Artificial Neural Networks, Data Processing, Natural Language Processing, Feature Engineering, Predictive Modeling, Supervised Learning, Unsupervised Learning, Data Transformation, NumPy

    Intermediate · Course · 3 - 6 Months

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    U

    University of Washington

    머신 러닝 기초: 사례 연구 접근 방식

    Skills you'll gain: Regression Analysis, Jupyter, Machine Learning Software, Applied Machine Learning, Image Analysis, Machine Learning, Classification And Regression Tree (CART), Predictive Modeling, Predictive Analytics, Deep Learning, Supervised Learning, Artificial Intelligence, Computer Vision, Unsupervised Learning, Text Mining, Natural Language Processing

    Mixed · Course · 1 - 3 Months

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    University of London

    모두를 위한 머신 러닝

    Skills you'll gain: Applied Machine Learning, Artificial Intelligence, Machine Learning, Computer Vision, Data Literacy, Data Processing

    4.7
    Rating, 4.7 out of 5 stars
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    12 reviews

    Beginner · Course · 1 - 4 Weeks

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    DeepLearning.AI

    النماذج المتعاقبة

    Skills you'll gain: Deep Learning, Natural Language Processing, Tensorflow, PyTorch (Machine Learning Library), Artificial Neural Networks, Applied Machine Learning, Machine Learning Methods, Time Series Analysis and Forecasting, Algorithms

    Intermediate · Course · 1 - 4 Weeks

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In summary, here are 10 of our most popular pytorch(머신 러닝 라이브러리) courses

  • Introduction to Machine Learning: Duke University
  • Machine Learning: DeepLearning.AI
  • IBM AI Engineering: IBM
  • 현대 로봇공학, 강좌 6: 캡스톤 프로젝트, 모바일 매니퓰레이션: Northwestern University
  • Fundamentals of AI Agents Using RAG and LangChain: IBM
  • Generative Adversarial Networks (GANs): DeepLearning.AI
  • 머신 러닝: 회귀 모델: University of Washington
  • IBM Generative AI Engineering: IBM
  • Machine Learning with PyTorch and Scikit-Learn: Packt
  • 머신 러닝 기초: 사례 연구 접근 방식: University of Washington

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