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  • Regularization Techniques

Results for "regularization+techniques"


  • D

    DeepLearning.AI

    Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

    Skills you'll gain: Tensorflow, Deep Learning, Performance Tuning, Machine Learning Methods, Data Preprocessing, Artificial Neural Networks, Model Evaluation, Verification And Validation

    4.9
    Rating, 4.9 out of 5 stars
    ·
    64K reviews

    Intermediate · Course · 1 - 4 Weeks

  • J

    Johns Hopkins University

    Fundamental Neuroscience for Neuroimaging

    Skills you'll gain: Magnetic Resonance Imaging, Neurology, Medical Imaging, Diagnostic Radiology, Anatomy, Radiology, X-Ray Computed Tomography, Experimentation, Research Design, Biomedical Technology, Image Analysis, Physiology, Medical Terminology

    4.7
    Rating, 4.7 out of 5 stars
    ·
    2.3K reviews

    Beginner · Course · 1 - 4 Weeks

  • D

    DeepLearning.AI

    Calculus for Machine Learning and Data Science

    Skills you'll gain: Applied Mathematics, Calculus, Numerical Analysis, Mathematical Modeling, Machine Learning, Python Programming, Artificial Neural Networks, Deep Learning, Visualization (Computer Graphics), Derivatives

    4.8
    Rating, 4.8 out of 5 stars
    ·
    947 reviews

    Intermediate · Course · 1 - 4 Weeks

  • I

    IBM

    Supervised Machine Learning: Regression

    Skills you'll gain: Supervised Learning, Regression Analysis, Applied Machine Learning, Predictive Modeling, Machine Learning, Logistic Regression, Scikit Learn (Machine Learning Library), Model Evaluation, Feature Engineering, Statistical Analysis, Classification Algorithms, Pandas (Python Package)

    4.7
    Rating, 4.7 out of 5 stars
    ·
    828 reviews

    Intermediate · Course · 1 - 3 Months

  • J

    Johns Hopkins University

    Advanced Neural Network Techniques

    Skills you'll gain: Autoencoders, Recurrent Neural Networks (RNNs), Deep Learning, Artificial Neural Networks, Reinforcement Learning, Generative AI, Generative Adversarial Networks (GANs), Unsupervised Learning, Machine Learning Methods, Artificial Intelligence and Machine Learning (AI/ML), Data Ethics, Markov Model

    Intermediate · Course · 1 - 3 Months

  • D

    DeepLearning.AI

    Linear Algebra for Machine Learning and Data Science

    Skills you'll gain: Linear Algebra, NumPy, Dimensionality Reduction, Data Preprocessing, Machine Learning Methods, Advanced Mathematics, Data Manipulation, Applied Mathematics, Mathematical Modeling, Machine Learning, Python Programming, Algebra

    4.6
    Rating, 4.6 out of 5 stars
    ·
    2.3K reviews

    Intermediate · Course · 1 - 4 Weeks

  • U

    University of Minnesota

    Matrix Factorization and Advanced Techniques

    Skills you'll gain: Machine Learning Algorithms, Applied Machine Learning, Machine Learning, Dimensionality Reduction, Algorithms, Model Evaluation, Linear Algebra

    4.4
    Rating, 4.4 out of 5 stars
    ·
    190 reviews

    Mixed · Course · 1 - 3 Months

  • T

    The University of Chicago

    Machine Learning: Concepts and Applications

    Skills you'll gain: Keras (Neural Network Library), Unsupervised Learning, Machine Learning Algorithms, Deep Learning, Machine Learning, Applied Machine Learning, Scikit Learn (Machine Learning Library), Classification Algorithms, Supervised Learning, Regression Analysis, Random Forest Algorithm, Dimensionality Reduction, Model Evaluation, Data Preprocessing, Statistical Methods, Tensorflow, Feature Engineering, Pandas (Python Package)

    3.9
    Rating, 3.9 out of 5 stars
    ·
    25 reviews

    Intermediate · Course · 1 - 3 Months

  • K

    Korea Advanced Institute of Science and Technology(KAIST)

    MRI Fundamentals

    Skills you'll gain: Magnetic Resonance Imaging, Medical Imaging, Image Quality, Digital Signal Processing, Medical Equipment and Technology

    4.5
    Rating, 4.5 out of 5 stars
    ·
    470 reviews

    Intermediate · Course · 1 - 3 Months

  • J

    Johns Hopkins University

    Principles of fMRI 1

    Skills you'll gain: Magnetic Resonance Imaging, Medical Imaging, Radiology, Research Design, Image Analysis, Experimentation, Neurology, Data Preprocessing, Science and Research, Statistical Analysis, Statistical Modeling, Statistics

    4.7
    Rating, 4.7 out of 5 stars
    ·
    850 reviews

    Beginner · Course · 1 - 4 Weeks

  • J

    Johns Hopkins University

    Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors

    Skills you'll gain: Linear Algebra, Applied Mathematics, Algebra, Advanced Mathematics, Geometry, Data Transformation, Applied Machine Learning, Dimensionality Reduction, Markov Model, Probability

    4.8
    Rating, 4.8 out of 5 stars
    ·
    76 reviews

    Mixed · Course · 1 - 3 Months

  • M

    Microsoft

    AI and Machine Learning Algorithms and Techniques

    Skills you'll gain: Unsupervised Learning, Generative AI, Large Language Modeling, Supervised Learning, Deep Learning, LLM Application, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Reinforcement Learning, Predictive Modeling, Machine Learning Algorithms, Artificial Neural Networks, Feature Engineering, Model Evaluation, Convolutional Neural Networks, Data Preprocessing, Dimensionality Reduction

    4.6
    Rating, 4.6 out of 5 stars
    ·
    62 reviews

    Intermediate · Course · 1 - 3 Months

1…456…9

In summary, here are 10 of our most popular regularization+techniques courses

  • Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization: DeepLearning.AI
  • Fundamental Neuroscience for Neuroimaging: Johns Hopkins University
  • Calculus for Machine Learning and Data Science: DeepLearning.AI
  • Supervised Machine Learning: Regression: IBM
  • Advanced Neural Network Techniques: Johns Hopkins University
  • Linear Algebra for Machine Learning and Data Science: DeepLearning.AI
  • Matrix Factorization and Advanced Techniques: University of Minnesota
  • Machine Learning: Concepts and Applications: The University of Chicago
  • MRI Fundamentals: Korea Advanced Institute of Science and Technology(KAIST)
  • Principles of fMRI 1: Johns Hopkins University

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