Lecture 2
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This document discusses neural networks and how they can be trained. It covers several key points: - Neural networks are assemblies of simple processing units (McCulloch-Pitts neurons) that can perform any computation when connected together and having their weights solved for. - Training neural networks involves adapting the weights to minimize error between the network's actual and desired outputs on a training set, using a learning rule. - A simple perceptron network has weights that are adjusted based on the difference between the desired and actual outputs, proportional to a learning rate. - Bias inputs and weights allow the network to function when all other inputs are zero, replacing the need for an adjustable threshold parameter.
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Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
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- Biological neural networks transmit signals via neurons connected by synapses and axons.
- Artificial neural networks are composed of simple processing elements (neurons) that operate in parallel and are determined by network structure and connection strengths (weights).
- Multilayer neural networks consist of an input layer, hidden layers, and output layer connected by weights to solve complex problems. Learning involves updating weights so the network can efficiently perform tasks.
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More from M. Raihan (20)
Lecture 7
Lecture 7M. Raihan This document discusses backpropagation algorithms for neural networks. It explains that backpropagation aims to optimize neural network weights so the network can learn to map inputs to correct outputs. As an example, it provides a single training set with inputs of 0.05 and 0.10 mapped to outputs of 0.01 and 0.99 respectively. It also references error functions, the forward pass through the network, and the logistic function.
Lecture 6
Lecture 6M. Raihan A multilayer neural network contains an input layer, two or more hidden layers, and an output layer. Information flows from the input nodes to the first hidden layer, then between subsequent hidden layers, and finally to the output layer. Multilayer networks can represent complex decision boundaries and functions through their multiple layers of nodes. They are trained using backpropagation, which calculates error signals starting from the output layer and propagates these errors back through the network to update weights between layers. This process of forward activation and backward error propagation is repeated over many iterations to gradually adjust the weights and minimize errors.
Lecture 5
Lecture 5M. Raihan The document discusses several key concepts in machine learning including reinforcement learning, evolutionary learning, features, training/test/validation sets, overfitting, underfitting, and clustering. Reinforcement learning involves training an agent through rewards/punishments without being directly told what to do. Evolutionary learning follows biological evolution principles of inheritance, variation, and selection. Features represent attributes of an object encoded in a vector. Training/test/validation sets are used to develop and evaluate models. Overfitting and underfitting refer to models fitting the training data too closely or not closely enough, respectively. Clustering groups similar objects together.
Lecture 4
Lecture 4M. Raihan This document provides an overview of machine learning. It defines machine learning as a computer program that improves its performance at tasks through experience. The document discusses different types of machine learning including supervised learning, unsupervised learning, reinforcement learning, and evolutionary learning. It also defines important machine learning concepts such as regression, classification, and clustering. Examples are provided to illustrate these machine learning techniques.
Lecture 3
Lecture 3M. Raihan The document discusses the limitations of perceptrons and how multilayer perceptrons can solve more complex problems. It explains that while a single perceptron can only learn linearly separable problems, multilayer perceptrons using techniques like backpropagation can learn nonlinear and more complex relationships. The document also provides information on decision boundaries and feature vectors, and how multilayer perceptrons are able to learn problems with nonlinear decision boundaries. Machine learning concepts like learning, training from data, and the growth of data are also briefly covered.
Lecture 1
Lecture 1M. Raihan This document discusses neural networks and the biological structures they are inspired by. It contains the following key points:
- Neurons are the fundamental units of the brain and nervous system, receiving sensory input and sending motor commands. A neuron has dendrites that receive input, an axon that sends output signals, and a cell body with DNA.
- McCulloch and Pitts designed one of the first neural networks in 1943, with simplified neuron models that sum weighted inputs and fire if the threshold is reached.
- Hebb's rule describes how the connection between neurons is strengthened when one neuron repeatedly helps cause the other to fire.
- Neural networks are biologically inspired but also have limitations compared to real
Amplifiers and biopotential amplifiers new
Amplifiers and biopotential amplifiers newM. Raihan What is Amplifiers?
Classification of Amplifiers
Based on number of stages
Single-stage Amplifier
Based on its output
ased on the input signals
Based on the frequency range
Based on the Coupling method
Based on the Transistor
Configuration
CE amplifier
CC amplifier
Comparaison between CB, CE
and CC Amplifies
Noise in Amplifier
Signal to Noise Ratio (SNR)
Biopotential Amplifiers
Typical bio-amplifier requirements
Voltage and Frequency
Range for Biopotentials
Electrocardiograph Amplifiers
Interference Reduction Techniques
Comparison of fnir with other neuroimaging modalities relation between eeg sy...
Comparison of fnir with other neuroimaging modalities relation between eeg sy...M. Raihan The document discusses neuroimaging techniques such as functional near-infrared spectroscopy (fNIRS). fNIRS uses near-infrared light to monitor changes in oxygenated and deoxygenated hemoglobin in the brain in response to neural activity. It provides a portable, low-cost way to monitor brain activity and oxygenation levels during cognitive tasks. The document discusses how fNIRS works, its applications in detecting brain activity and lie detection by measuring hemodynamic responses in the prefrontal cortex. It also compares fNIRS to other neuroimaging techniques such as fMRI and discusses its potential for use in brain-computer interfaces.
Lecture 10
Lecture 10M. Raihan The document discusses modern CPUs and their specifications. It provides details about AMD and Intel CPUs including their core counts, clock speeds, thermal designs and cache sizes. It also covers CPU architecture types like RISC and CISC. Additional topics covered include microprocessors, hardware components, operating systems like DOS and GUI, computer buses and USB standards.
Lecture 9
Lecture 9M. Raihan This document discusses various data representation techniques including excess-3 code, Gray code, binary coded decimal, ASCII, EBCDIC, Unicode, and logic gates. Excess-3 code represents each decimal number as a binary number in excess of three. Gray code ensures each code differs from its neighbor by one bit. ASCII and EBCDIC are character encoding standards that allow representation of text. Unicode supports encoding for many languages. The document also describes logic gates like AND, OR, NOT, XOR, and how logic expressions can be represented using gates.
Lecture 8
Lecture 8M. Raihan A bit is the smallest unit of data that can represent either a 1 or 0. Eight bits form a byte, which can represent one character. Common units for data storage are bytes, kilobytes, megabytes and gigabytes. There are different number systems used to represent numeric values in computing, including binary, octal and hexadecimal. Binary numbers can be added, subtracted and multiplied using logic gates. Decimal numbers can be converted to their binary or octal equivalent through repeated division and obtaining remainders.
Lecture 7
Lecture 7M. Raihan The document discusses different ways of representing numeric data in computing systems. It covers external representation using symbols like letters and numbers, and internal representation using binary digits (0s and 1s). Numeric data is categorized as integer or real numbers, and further as signed or unsigned values. Different number systems like binary, octal, decimal, and hexadecimal are explained, along with how values are represented and converted between these systems using place value and arithmetic operations on the base of each system. Conversion examples are provided between decimal, binary, octal, and hexadecimal numbers.
Lecture 6
Lecture 6M. Raihan There are two types of LCD screens: passive-matrix and active-matrix. Passive matrix LCD screens arrange elements in rows and columns and activate them sequentially, which can cause ghosting. Active matrix LCD screens use transistors to independently control each element, allowing for higher quality images. Printers also come in impact and non-impact varieties. Impact printers like dot matrix printers work by striking pins against an inked ribbon. Non-impact printers like inkjet and laser printers work without physical contact by spraying or fusing tiny droplets of ink. Laser printers create electrostatic images on a drum using a laser beam.
Lecture 5
Lecture 5M. Raihan The document discusses different types of computer output devices. It describes monitors, printers, 3D printers, braille readers, GPS devices, plotters, headphones, projectors, sound cards, video cards, and different types of displays including CRT displays and LCD monitors. It provides details on what each device is and how it functions, as well as advantages and disadvantages of CRT and LCD displays.
Lecture 4
Lecture 4M. Raihan The document discusses various input devices used in computers. It describes keyboards, mice, joysticks, light pens, trackballs, scanners, graphics tablets, microphones, magnetic ink card readers, optical character readers, optical mark readers, bar code readers, and webcams. For each device, it provides details on what it is and how it functions to input data into a computer. The keyboard section contains the most detail, breaking down its different types of keys and their purposes.
Lecture 18
Lecture 18M. Raihan An information system is a group of components that collect, organize, store, and communicate information to help organizations operate, manage their resources, and compete in the marketplace. There are different types of information systems that support various organizational levels, including transaction processing systems, office automation systems, knowledge work systems, management information systems, decision support systems, and executive support systems. The systems development life cycle is the process used to plan, create, test, and deploy information systems and includes steps like system planning, analysis, design, implementation, testing, and maintenance.
Lecture 17
Lecture 17M. Raihan The document discusses various topics related to online work and e-commerce. It begins by describing different types of internet connections like dial-up, DSL, cable, satellite, and mobile. It then discusses the hardware needed like modems and routers. It provides definitions and examples of different types of e-commerce like B2B, B2C, C2C, C2B, B2A, and C2A. The advantages of e-commerce are described as reaching a global market, shortening distribution chains, increased productivity and competitiveness for businesses, and cost reduction. The disadvantages are strong dependence on technology, lack of adequate legislation, consumer reluctance, privacy and identity issues, and security concerns.
Lecture 16
Lecture 16M. Raihan Bitmap images store color information for each individual pixel arranged in a pattern, while vector images use mathematical formulas to draw lines and curves. Bitmaps can be edited by changing pixel colors, while vectors are edited by manipulating lines and curves. Vector images tend to be smaller files since they just store formulas rather than individual pixel data.
Graphics software allows users to manipulate images on a computer and can be categorized as raster or vector based. Common software includes paint programs for photo editing, draw programs, and CAD for design. Word processors, publishing software, illustration programs, and presentation software allow combining text and graphics. Clip art contains images that can be imported, while multimedia integrates various media types into an interactive experience.
Lecture 3
Lecture 3M. Raihan The document provides an overview of the internet and its key components. It discusses how the internet originated from the ARPANET project in 1968 and evolved to become a global system of interconnected computer networks using TCP/IP. The key elements that enable the internet are described, including protocols like TCP and IP, packets, routers, domain names, HTML, email and the World Wide Web. Common internet services like file transfer, communication tools, information retrieval and e-commerce are also summarized. Connection types, firewalls and other networking concepts are briefly covered as well.
Lecture 15
Lecture 15M. Raihan Productivity software includes programs that help users create documents, spreadsheets, presentations, and databases. Examples include word processors, spreadsheet applications like Excel, and database management systems. Word processors allow editing documents and changing formats. Spreadsheets organize and calculate data in rows and columns, and are useful for tasks like tracking finances, grades, lists, and stats. Database management systems help users create and manage databases to store and retrieve information. Productivity software can be obtained through commercial purchases, shareware, freeware, or as open source code.
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Lecture 2
- 3. Neural Networks 21-Sep-17 Can put lots of McCulloch & Pitts neurons together. Connect them up in any way we like. In fact, assemblies of the neurons are capable of universal computation. Can perform any computation that a normal computer can. Just have to solve for all the weights wij 3
- 6. Training Neurons 21-Sep-17 Adapting the weights is learning How does the network know it is right? How do we adapt the weights to make the network right more often? Training set with target outputs. Learning rule. 6
- 7. A Simple Perceptron 21-Sep-17 It’s a single-unit network Change the weight by an amount proportional to the difference between the desired output and the actual output. 7
- 8. Updating the Weights 21-Sep-17 Wij Wij+ Wij We want to change the values of the weights Aim: minimize the error at the output If E = t-y, want E to be 0 Use: 8
- 9. The Learning Rate 21-Sep-17 ɳ controls how much to change by weights. If we could miss it, ɳ = 1. Weight change a lot, whenever the answer is wrong. Makes the network unstable. Small ɳ Weights need to see the inputs more often before they change significantly. Network takes longer to learn. But, more stable network. 9
- 10. Bias Input 21-Sep-17 What happens when all the inputs to a neuron is zero? It doesn’t matter what the weights are The only way that we can control whether neuron fires or not is through the threshold. That’s why threshold should be adjustable. Changing the threshold requires an extra parameter that we need to write code for. We can add an extra input weight to the neuron, with the values of the input to that weight always being fixed. h = 𝒊=𝟏 𝒎 𝒙𝒊 𝒘𝒊 10
- 16. More Than One Input Neuron 21-Sep-17 The weights for each neuron separately describe a straight line. 16
- 17. Perceptron Limitations 21-Sep-17 A single layer perceptron can only learn linearly separable problems. Boolean AND function is linearly separable, whereas Boolean XOR function (and the parity problem in general) is not. 17
- 19. What Can Perceptrons Represent? 21-Sep-17 19