Problems Based Learning

Machine Learning with Python in detail both practically and theoretically with machine learning project for data science.

Mastering Problem-Based Learning: Enhancing Critical Thinking and Collaboration

Prep for Node.js Certified Developer Exam or employment quiz or test. Memory Based Learning is best for memorizing code.

Similar to Rosetta Stones and The Pimsleure Methods, I help you memorize coding using the space repetition technique.

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: * the setting of learning problems based on the model of minimizing the risk functional from empirical data * a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its consistency * non-asymptotic bounds for the risk achieved using the empirical risk minimization principle * principles for controlling the generalization ability of learning machines using small sample sizes based on these bounds * the Support Vector methods that control the generalization ability when estimating function using small sample size.

Master Image Classification with CNN on CIFAR-10 dataset: A Deep Learning Project for Beginners using Python

Master Image Classification with CNN on CIFAR-10 dataset: A Deep Learning Project for Beginners using Python