Hyperparameter Optimization | Applied Machine Learning, Part 3
Machine learning is all about fitting models to data. This process typically involves using an iterative algorithm that minimizes the model error. The parameters that control a machine learning algorithm’s behavior are called hyperparameters. Depending on the values you select for your h
From playlist Applied Machine Learning
Introduction to Hyperparameters | Predictive Modeling and Machine Learning, Part 4
You may see terms like parameters and hyperparameters to describe characteristics of your machine learning models but not know the difference between them. In this video, learn what hyperparameters are, why they are important, and various approaches to optimize them. - Learn more: https:
From playlist Predictive Modeling and Machine Learning
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Machine Learning
Optimizing Hyperparameters | Predictive Modeling and Machine Learning, Part 5
Manually adjusting parameter values is an impractical approach to finding the best set of hyperparameters. This video explains how to automate the optimization process. First, learn how to optimize a machine learning decision tree, then optimize an ensemble of decision trees, and lastly ap
From playlist Predictive Modeling and Machine Learning
Hyperbola 3D Animation | Objective conic hyperbola | Digital Learning
Hyperbola 3D Animation In mathematics, a hyperbola is a type of smooth curve lying in a plane, defined by its geometric properties or by equations for which it is the solution set. A hyperbola has two pieces, called connected components or branches, that are mirror images of each other an
From playlist Maths Topics
What Is Machine Learning? | What Is Machine Learning And How Does It Work? | Simplilearn
This Machine Learning tutorial will help you understand what is Machine Learning, Artificial Intelligence vs Machine Learning vs Deep Learning, how does Machine Learning work, types of Machine Learning, Machine Learning pre-requisites and applications of Machine Learning. Machine learning
Introduction To Machine Learning | Machine Learning Basics for Beginners | ML Basics | Simplilearn
Machine Learning is a trending topic nowadays. This Introduction to Machine Learning video will help you to understand what is Machine Learning, importance of Machine Learning, advantages and disadvantages of Machine Learning, what are the types of Machine Learning - supervised, unsupervis
Machine Learning with scikit learn Part Two | SciPy 2017 Tutorial | Andreas Mueller & Alexandre Gram
Tutorial materials found here: https://scipy2017.scipy.org/ehome/220975/493423/ Machine learning is the task of extracting knowledge from data, often with the goal of generalizing to new and unseen data. Applications of machine learning now touch nearly every aspect of everyday life, fro
From playlist talks
What is Quantum Machine Learning?
Generative machine learning is the field of ML that focuses on generating data. If you've seen any of the realistic-looking faces on pages such as www.thispersondoesnotexist.com or www.whichfaceisreal.com, you've seen generative machine learning in action. Quantum computing is a rapidly ad
From playlist Fundamentals of Machine Learning
Data Science Basics: Predictive Machine Learning
Live Jupyter walk-through of basic predictive machine learning model building in Python with the scikit-learn package.This should be enough to get anyone started building predictive machine learning workflows in Python. The demonstrated workflow is available at: https://github.com/Geostat
From playlist Data Science Basics in Python
This is how to take your ML models from great to GOAT
What are hyperparameters? Why do we want to tune them? And how do we do it? My Patreon : https://www.patreon.com/user?u=49277905
From playlist Data Science Concepts
Machine Learning 3 - Generalization, K-means | Stanford CS221: AI (Autumn 2019)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/30Z6b0p Topics: Generalization, Unsupervised learning, K-means Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onl
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019
Archive - Machine Learning for Risk Management: Forecasting Bitcoin Volatility
- Learn about Quantitative Finance and Risk Management using MATLAB: http://bit.ly/2KrVtSX Volatility forecasting is a common but still very important problem in finance, especially in risk management and quantitative finance. - Free eBook on Modeling Financial Risk: http://bit.ly/2HWX0m
From playlist Machine Learning for Finance
I'm really excited to present this video on Determined AI! Determined has been teaching me how to use their platform and showing me what they are building. I am so excited about this with advancing my Deep Learning experimentation skills and I hope you all find value out of this as well.
From playlist Determined AI
Lecture 2 | Image Classification
Lecture 2 formalizes the problem of image classification. We discuss the inherent difficulties of image classification, and introduce data-driven approaches. We discuss two simple data-driven image classification algorithms: K-Nearest Neighbors and Linear Classifiers, and introduce the con
From playlist Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)
Lecture 13: Convolutional Neural Networks
Lecture 13 provides a mini tutorial on Azure and GPUs followed by research highlight "Character-Aware Neural Language Models." Also covered are CNN Variant 1 and 2 as well as comparison between sentence models: BoV, RNNs, CNNs. -------------------------------------------------------------
From playlist Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017)
(ML 1.1) Machine learning - overview and applications
Attempt at a definition, and some applications of machine learning. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
Keras Tuner with Google Cloud Compute - Keras Examples
This video walkthroughs a series of new tutorials on integrating Google Cloud runtimes with the Keras Tuner library. I hope from this tutorial you are able to get a sense of how to setup hyperparameters, interface them in a model builder function, and connect your experiments to Google Clo
From playlist Keras Code Examples