Forecasting the Future - An Introduction to Machine Learning for Weather Prediction in Native Ruby
Explore building weather prediction models using machine learning in Ruby, covering data preparation, linear regression, and practical implementation techniques for Rubyists of all levels.
Machine Learning for Muggles
Explore Azure Machine Learning and Cognitive Services APIs to implement AI capabilities in applications through simple REST calls, making AI more accessible to developers.
Developer Accessible Machine Learning
Explore building and training a basic prediction model, understanding its workings, and incorporating cloud solutions into projects. No mathematical expertise required.
Machine Learning - The Bare Math Behind Libraries
Explore the mathematical foundations of machine learning, from supervised to unsupervised learning techniques, to gain a deeper understanding of neural networks and their underlying processes.
How Many Hidden Layers and Neurons Do You Need in Your Artificial Neural Network?
Explore the process of determining optimal hidden layers and neurons in neural networks through practical examples and code demonstrations.
Least Square Method and Curve Fitting - Straight Line Problems in Numerical Methods
Master curve fitting and least square method techniques for straight line problems through practical numerical examples and statistical applications.
JAX vs PyTorch 2 - Converting Stateful to Stateless Operations in Neural Networks
Explore functional programming in JAX through hands-on comparison with PyTorch 2, focusing on neural network implementation and stateless operations for gradient descent.
Least Squares Fit for Web VPython
Implement least squares fit in Web VPython to find the best fit line for data, enhancing your data analysis capabilities in this programming environment.
Hands-on Python: Fitting Regression Models with Sklearn and Statsmodels - Part 3
Fit regression models using sklearn and statsmodels in Python. Learn model setup, evaluation, and implementation techniques.
Understanding and Misunderstanding Models - From Linear Regression to Complex Systems
Explore the complexities of scientific modeling, from linear regression to complex systems, examining different levels of understanding and the challenges of explanation in scientific communication.
SPSS
Learn essential statistical techniques using SPSS, including ANOVA, regression, t-tests, and descriptive statistics for effective data analysis and interpretation.
A Friendly Introduction to Machine Learning
Explore key machine learning algorithms through clear examples, from linear regression to neural networks and clustering techniques, in this beginner-friendly introduction.
Breaking Black-Box AI
Exploring failure cases of popular AI algorithms through live demos, revealing limitations and silent failures of black-box AI systems to help developers use them more effectively.
Machine Learning: Least Mean Square Regression - Lecture 12
Dive into advanced linear regression concepts, exploring overfitting, gradient descent, and practical plotting techniques for enhanced machine learning understanding.
Introduction to Machine Learning: Regression - Part 1
Explore regression in machine learning: estimate relationships, predict outcomes, and apply algorithms for continuous target variables. Learn evaluation techniques and practical considerations.
Multi-dimensional Domain Generalization with Low-Rank Structures
Explore novel tensor-based approach for robust domain generalization in linear regression, addressing challenges in underrepresented populations and achieving reliable, interpretable results with theoretical guarantees.
Functional Programming and Scala for Machine Learning - YOW! 2015
Explore how functional programming and Scala can revolutionize machine learning, offering strongly-typed solutions for efficient algorithm implementation and scalable data processing.
Benign Overfitting in Linear Regression and Deep Learning
Explore benign overfitting in deep learning with Peter Bartlett. Uncover insights on linear regression, effective rank, and overparameterization in neural networks for optimal prediction accuracy.
Breaking Black-Box AI
Explore failure cases of popular AI algorithms through live demos, from linear regression to deep learning. Learn to identify and address silent failures in black-box AI systems.
Least Mean Square Regression for Machine Learning - Lecture 11
Dive into least mean square regression, exploring loss function minimization and gradient descent methods for effective linear regression implementation and optimization.
Generalized Linear Models and Linear Regression - Lecture 11
Dive into advanced statistical concepts including generalized linear models, nonlinear basis functions, and regularization techniques for robust data analysis and point estimation.