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  • Probabilistic Neural Network


Results for "probabilistic+neural+network"


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    Stanford University

    Probabilistic Graphical Models

    Skills you'll gain: Bayesian Network, Applied Machine Learning, Graph Theory, Machine Learning Algorithms, Probability Distribution, Network Model, Bayesian Statistics, Statistical Modeling, Markov Model, Decision Support Systems, Machine Learning, Probability & Statistics, Network Analysis, Machine Learning Methods, Statistical Inference, Sampling (Statistics), Statistical Methods, Algorithms, Model Evaluation, Computational Thinking

    4.6
    Rating, 4.6 out of 5 stars
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    1.5K reviews

    Advanced · Specialization · 3 - 6 Months

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    Johns Hopkins University

    Foundations of Neural Networks

    Skills you'll gain: Responsible AI, Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks (RNNs), Data Ethics, Deep Learning, Artificial Neural Networks, Reinforcement Learning, Generative AI, Generative Adversarial Networks (GANs), Machine Learning Algorithms, Model Deployment, Debugging, Artificial Intelligence, Image Analysis, Unsupervised Learning, Machine Learning Methods, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Computer Vision

    4.6
    Rating, 4.6 out of 5 stars
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    20 reviews

    Intermediate · Specialization · 3 - 6 Months

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    E

    EDUCBA

    Apply Neural Networks for Car Price Prediction

    Skills you'll gain: Exploratory Data Analysis, Feature Engineering, Data Analysis, Data Preprocessing, Model Evaluation, Data Cleansing, Data Transformation, Predictive Modeling, Pandas (Python Package), Applied Machine Learning, Statistical Methods, Regression Analysis, Scikit Learn (Machine Learning Library), Artificial Neural Networks

    Beginner · Course · 1 - 4 Weeks

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    Coursera

    Statistical Inference & Predictive Modeling Foundations

    Skills you'll gain: Descriptive Statistics, A/B Testing, Classification And Regression Tree (CART), Dashboard, Model Evaluation, Model Deployment, Data-Driven Decision-Making, Histogram, Data Presentation, Predictive Modeling, Data Storytelling, Exploratory Data Analysis, Risk Analysis, Risk Mitigation, Regression Analysis, Data Visualization, MLOps (Machine Learning Operations), Decision Tree Learning, Keras (Neural Network Library), Statistical Inference

    Intermediate · Specialization · 3 - 6 Months

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    Imperial College London

    TensorFlow 2 for Deep Learning

    Skills you'll gain: Tensorflow, Recurrent Neural Networks (RNNs), Autoencoders, Generative Model Architectures, Data Pipelines, Keras (Neural Network Library), Model Evaluation, Deep Learning, Image Analysis, Transfer Learning, Convolutional Neural Networks, Applied Machine Learning, Bayesian Statistics, Supervised Learning, Natural Language Processing, Computer Vision, Model Deployment, Artificial Neural Networks, Data Preprocessing, Probability Distribution

    4.8
    Rating, 4.8 out of 5 stars
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    722 reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: New
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    Coursera

    Probabilistic Graphical Models: A Compact Introduction

    Skills you'll gain: Bayesian Network, Predictive Modeling, Machine Learning Methods, Markov Model, Statistical Modeling, Artificial Intelligence, Statistical Inference, Graph Theory, Network Analysis, Probability & Statistics, Sampling (Statistics), Algorithms, Scalability

    Intermediate · Course · 1 - 4 Weeks

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    IBM

    Introduction to Neural Networks and PyTorch

    Skills you'll gain: PyTorch (Machine Learning Library), Statistical Methods, Logistic Regression, Tensorflow, Artificial Neural Networks, Probability & Statistics, Regression Analysis, Model Evaluation, Data Preprocessing

    4.4
    Rating, 4.4 out of 5 stars
    ·
    1.9K reviews

    Intermediate · Course · 1 - 3 Months

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    I

    IBM

    Deep Learning with PyTorch

    Skills you'll gain: PyTorch (Machine Learning Library), Convolutional Neural Networks, Deep Learning, Artificial Neural Networks, Applied Machine Learning, Supervised Learning, Classification Algorithms, Model Evaluation

    4.5
    Rating, 4.5 out of 5 stars
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    89 reviews

    Intermediate · Course · 1 - 3 Months

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    D

    DeepLearning.AI

    Generative Adversarial Networks (GANs)

    Skills you'll gain: Generative Adversarial Networks (GANs), Generative Model Architectures, Generative AI, PyTorch (Machine Learning Library), Image Analysis, Convolutional Neural Networks, Deep Learning, Model Evaluation, Responsible AI, Artificial Neural Networks, Data Ethics, Machine Learning, Image Quality, Unsupervised Learning, Information Privacy, Data Synthesis

    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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    Dartmouth College

    Practical Machine Learning: Foundations to Neural Networks

    Skills you'll gain: Supervised Learning, Bayesian Network, Logistic Regression, Artificial Neural Networks, Machine Learning Methods, Statistical Modeling, Predictive Modeling, Model Evaluation, Statistical Machine Learning, Probability & Statistics, Bayesian Statistics, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Machine Learning Algorithms, Statistical Methods, Artificial Intelligence, Regression Analysis, Classification Algorithms, Statistical Inference

    Build toward a degree

    Intermediate · Specialization · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
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    EDUCBA

    Deep Learning with Python: CNN, ANN & RNN

    Skills you'll gain: Model Evaluation, Convolutional Neural Networks, Data Preprocessing, Keras (Neural Network Library), Image Analysis, Deep Learning, Tensorflow, Computer Vision, Artificial Neural Networks, Recurrent Neural Networks (RNNs), Data Manipulation, Data Transformation, Financial Forecasting, Data Visualization, Time Series Analysis and Forecasting, Exploratory Data Analysis, Python Programming, Customer Analysis, Applied Machine Learning, Cloud Computing

    4.6
    Rating, 4.6 out of 5 stars
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    47 reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
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    I

    Imperial College London

    Probabilistic Deep Learning with TensorFlow 2

    Skills you'll gain: Tensorflow, Autoencoders, Generative Model Architectures, Deep Learning, Applied Machine Learning, Image Analysis, Bayesian Statistics, Probability Distribution, Unsupervised Learning, Sampling (Statistics)

    4.7
    Rating, 4.7 out of 5 stars
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    109 reviews

    Advanced · Course · 1 - 3 Months

What brings you to Coursera today?

1234…9

In summary, here are 10 of our most popular probabilistic+neural+network courses

  • Probabilistic Graphical Models: Stanford University
  • Foundations of Neural Networks: Johns Hopkins University
  • Apply Neural Networks for Car Price Prediction: EDUCBA
  • Statistical Inference & Predictive Modeling Foundations: Coursera
  • TensorFlow 2 for Deep Learning: Imperial College London
  • Probabilistic Graphical Models: A Compact Introduction: Coursera
  • Introduction to Neural Networks and PyTorch: IBM
  • Deep Learning with PyTorch: IBM
  • Generative Adversarial Networks (GANs): DeepLearning.AI
  • Practical Machine Learning: Foundations to Neural Networks: Dartmouth College

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