AI News, Machine Learning - Some Bones
- On Wednesday, October 17, 2018
- By Read More
Machine Learning - Some Bones
To start to illustrate the computational process we will look at a very simple example of a neural network.
The perceptron starts by calculating a weighted sum of its inputs The perceptron has five parts: We can ask the perceptron to give us an answer to a question where we have three factors that influence the outcome.
“is it good for you?” “does it taste good?” “does it look good?” We give numerical values to all of the questions and the answers.
the inputs are multiplied by the weights The next step is to sum all the inputs and the weights The neuron’s output is determined by whether the weighted sum is less than or greater than a thresholdValue.
As we know the answers to the question we can use the answers to adjust Now all this is very basic and it would be easy to write a few lines of code to work out that we have three conditions that have a value and are weighted, measure the output against our threshold it can then make a decision of true or false.
single layer, single neuron network (using a linear activation function) receives an input with two features x1 and x2;
It does this by taking a set of weighted inputs, calculating their sum with a function to activate the neuron and passing the output of the activation function to other nodes in the network.
In general terms a network learns from having an input and a known output, so we can give pairs of values (x, y) where x is the input and y the known output The aim is to find the weights (w) that fit closest to the training data.
- On Wednesday, January 16, 2019
Binary Weighted Resistor DAC
In this video Binary Weighted Resistor type DAC is discussed.
DAC Methods Binary Weighted Input
A video by Jim Pytel for Renewable Energy Technology students at Columbia Gorge Community College.
Discrete-time convolution sum and example
Discrete-time convolution represents a fundamental property of linear time-invariant (LTI) systems. Learn how to form the discrete-time convolution sum and see ...
Likert Scales and Coding Groups (Copying Value Labels) - Part 1
Learn about Likert Scales in SPSS and how to copy labels from one variable to another in this video. Entering codes for Likert Scales into SPSS is also covered.
Lecture 16: Dynamic Neural Networks for Question Answering
Lecture 16 addresses the question ""Can all NLP tasks be seen as question answering problems?"". Key phrases: Coreference Resolution, Dynamic Memory ...
Lecture 9: Weighted Bipartite Matching
In this lecture, we will discuss Weighted Bipartite Matching, Transversal, Equality subgraph and Hungarian Algorithm.
Lecture - 9 Packet Scheduling Algorithm Introduction
Lecture Series on Broadband Networks by Prof. Karandikar , Department of Electrical Engineering , IIT Bombay. For more details on NPTEL visit ...
Understanding Model Predictive Control, Part 2: What is MPC?
Learn how model predictive control (MPC) works. MPC uses a model of the plant to make predictions about future plant outputs. It solves an optimization ...
Bringing AI and machine learning innovations to healthcare (Google I/O '18)
Could machine learning give new insights into diseases, widen access to healthcare, and even lead to new scientific discoveries? Already we can see how ...
Mod-08 Lec-29 Radial Basis Function Networks; Gaussian RBF networks
Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IISc Bangalore. For more details on NPTEL visit ...