Loss functions Loss functions are used to quantify how well or bad a model can reproduce the values of the training set. The appropriate loss function depends on the type of problems and the algo...

Control Learning Objectives The aim of this lecture is to design a control system for dynamical systems. Objectives: Feedback Systems Bang-Bang Control PID Control State-Space Repr...

RP6 Localisation Objectives: Differential drive robots Localisation Motion control book: Introduction to Mobile Robot Control, Spyros G. Tzafestas, 2014 Mobile robots A robot is an ...

Loss functions Loss functions are used to quantify how well or bad a model can reproduce the values of the training set. The appropriate loss function depends on the type of problems and the algo...

Logistic regression The problems we had with the perceptron were: only converged for linearly separable problems 仅对线性可分问题收敛 stopped places that did not look like they would generalise well 不...

Perceptron 感知器 Perceptron algorithm was the first “Machine learning” algorithm invented in 1957 by Frank Rosenblatt Classification We have a set of examples of items from two classes. We wa...

Machine Learing Machine learning algorithms can be classified according to the type of problems they solve the model they use the way they learn Types of problems classification ...

OpenMP OpenMP is a … abbreviation for Open Multi Processing allows programmers to annotate their C/FORTRAN code with parallelism specs portability stems from compiler support standard de...

hamilton8 Session 01 HPC validates Moore’s law 验证摩尔定律 Moore’s law is a statement on transistor count/hardware complexity for fixed price Moore’s law used to translate directly into perfor...

From @VergilOP Lecture 5 - Manipulator Kinematics Learning Objectives Objectives Link Description Denavit-Hartenberg(D-H parameters) Manipulator Kinematics Li...