Search result for Bayesian networks Online Courses & Certifications
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Probabilistic Deep Learning with TensorFlow 2
by Dr Kevin Webster- 4.7
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Approx. 53 hours to complete
You will learn how probability distributions can be represented and incorporated into deep learning models in TensorFlow, including Bayesian neural networks, normalising flows and variational autoencoders. Probabilistic layers and Bayesian neural networks Welcome to week 2 - Probabilistic layers and Bayesian neural networks...
Mathematics & Statistics of Machine Learning & Data Science
by Cinnamon TechX- 4.2
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11 hours on-demand video
Learn Mathematics and Statistics of Machine Learning, Artificial Intelligence, Neural Networks and Deep Learning A key advantage of deep learning networks is that they often continue to improve as the size of your data increases. Mathematics and Statistics behind Neural Networks Bayesian Decision Theory...
$12.99
Machine Learning, Data Science and Deep Learning with Python
by Sundog Education by Frank Kane- 4.7
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15.5 hours on-demand video
Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's) Bayesian Methods Build artificial neural networks with Tensorflow and Keras...
$14.99
Social and Economic Networks: Models and Analysis
by Matthew O. Jackson- 4.8
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Approx. 30 hours to complete
Learn how to model social and economic networks and their impact on human behavior. How do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors? Diffusion on Networks 3: Diffusion on Random Networks Learning on Networks Games on Networks 1: Games on Networks...
Deep Learning: GANs and Variational Autoencoders
by Lazy Programmer Team- 4.4
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7.5 hours on-demand video
Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow This by itself is really cool, but we'll also be incorporating ideas from Bayesian Machine Learning, Reinforcement Learning, and Game Theory....
$29.99
Python: Step into the World of Machine Learning
by Packt Publishing- 2.9
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6 hours on-demand video
Willi Richert has a PhD in machine learning/robotics, where he used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation....
$12.99
Networks Illustrated: Principles without Calculus
by Christopher Brinton , Mung Chiang- 4.4
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Approx. 24 hours to complete
These are just a few of the many intriguing questions we can ask about the social and technical networks that form integral parts of our daily lives. Power Control in Cellular Networks Power of Networks Random Access in Wifi Networks Bayesian Ranking: Part I Bayesian Ranking: Part II Influencing People in Social Networks...
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Introduction to Machine Learning for Data Science
by David Valentine- 4.4
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5.5 hours on-demand video
How to identify and apply Machine Learning algorithms, with exotic names like “Decision Trees”, “Neural Networks” “K’s Nearest Neighbors” and “Naive Bayesian Classifiers”...
$14.99
Computational Neuroscience
by Rajesh P. N. Rao , Adrienne Fairhall- 4.6
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Approx. 26 hours to complete
2 Population Coding and Bayesian Estimation Computing with Networks (Rajesh Rao) 3 The Fascinating World of Recurrent Networks Computing with Networks Networks that Learn: Plasticity in the Brain & Learning (Rajesh Rao) Networks that Learn...
Probabilistic Graphical Models 2: Inference
by Daphne Koller- 4.6
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Approx. 38 hours to complete
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more....
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