Neural Network Activation Functions
Bharatiya Vidya Bhavan’s
Sardar Patel Institute of Technology,
Munshi Nagar, Andheri (w) Mumbai.
Neural Network Activation Functions
AICTE Sponsored Two Week FDP on
“Insights into Intelligent Automation
Machine Learning and Data science”
19th Oct to 31st Oct 2020
By
Dhananjay Kalbande
Professor, Computer
Engineering,S.P.I.T. Mumba
The document discusses neural network architecture and components. It explains that a neural network consists of nodes that represent neurons, similar to the human brain. Data is fed through an input layer, processed through hidden layers, and output at the output layer. Key components include the neuron/node, weights, biases, and activation functions. Common activation functions are sigmoid, tanh, ReLU, and softmax, each suited for different types of problems. The document provides details on each of these components and how they enable neural networks to learn from data.
The document discusses various activation functions used in neural networks including Tanh, ReLU, Leaky ReLU, Sigmoid, and Softmax. It explains that activation functions introduce non-linearity and allow neural networks to learn complex patterns. Tanh squashes outputs between -1 and 1 while ReLU sets negative values to zero, addressing the "dying ReLU" problem. Leaky ReLU allows a small negative slope. Sigmoid and Softmax transform outputs between 0-1 for classification problems. Activation functions determine if a neuron's output is important for prediction.
The document discusses various activation functions used in deep learning neural networks including sigmoid, tanh, ReLU, LeakyReLU, ELU, softmax, swish, maxout, and softplus. For each activation function, the document provides details on how the function works and lists pros and cons. Overall, the document provides an overview of common activation functions and considerations for choosing an activation function for different types of deep learning problems.
Artificial neural networks mimic the human brain by using interconnected layers of neurons that fire electrical signals between each other. Activation functions are important for neural networks to learn complex patterns by introducing non-linearity. Without activation functions, neural networks would be limited to linear regression. Common activation functions include sigmoid, tanh, ReLU, and LeakyReLU, with ReLU and LeakyReLU helping to address issues like vanishing gradients that can occur with sigmoid and tanh functions.
The document discusses components and concepts related to artificial neural networks. It describes the basic units (neurons), connections between neurons, propagation and activation functions, common activation functions like sigmoid and tanh, and network topologies including feedforward and recurrent networks. It provides details on how artificial neural networks are designed based on the human brain and how information is processed through the connections and activation of neurons.
V2.0 open power ai virtual university deep learning and ai introductionGanesan Narayanasamy
OpenPOWER AI virtual University's - focus on bringing together industry, government and academic expertise to connect and help shape the AI future .
https://www.youtube.com/channel/UCYLtbUp0AH0ZAv5mNut1Kcg
The document discusses neural network architecture and components. It explains that a neural network consists of nodes that represent neurons, similar to the human brain. Data is fed through an input layer, processed through hidden layers, and output at the output layer. Key components include the neuron/node, weights, biases, and activation functions. Common activation functions are sigmoid, tanh, ReLU, and softmax, each suited for different types of problems. The document provides details on each of these components and how they enable neural networks to learn from data.
The document discusses various activation functions used in neural networks including Tanh, ReLU, Leaky ReLU, Sigmoid, and Softmax. It explains that activation functions introduce non-linearity and allow neural networks to learn complex patterns. Tanh squashes outputs between -1 and 1 while ReLU sets negative values to zero, addressing the "dying ReLU" problem. Leaky ReLU allows a small negative slope. Sigmoid and Softmax transform outputs between 0-1 for classification problems. Activation functions determine if a neuron's output is important for prediction.
The document discusses various activation functions used in deep learning neural networks including sigmoid, tanh, ReLU, LeakyReLU, ELU, softmax, swish, maxout, and softplus. For each activation function, the document provides details on how the function works and lists pros and cons. Overall, the document provides an overview of common activation functions and considerations for choosing an activation function for different types of deep learning problems.
Artificial neural networks mimic the human brain by using interconnected layers of neurons that fire electrical signals between each other. Activation functions are important for neural networks to learn complex patterns by introducing non-linearity. Without activation functions, neural networks would be limited to linear regression. Common activation functions include sigmoid, tanh, ReLU, and LeakyReLU, with ReLU and LeakyReLU helping to address issues like vanishing gradients that can occur with sigmoid and tanh functions.
The document discusses components and concepts related to artificial neural networks. It describes the basic units (neurons), connections between neurons, propagation and activation functions, common activation functions like sigmoid and tanh, and network topologies including feedforward and recurrent networks. It provides details on how artificial neural networks are designed based on the human brain and how information is processed through the connections and activation of neurons.
V2.0 open power ai virtual university deep learning and ai introductionGanesan Narayanasamy
OpenPOWER AI virtual University's - focus on bringing together industry, government and academic expertise to connect and help shape the AI future .
https://www.youtube.com/channel/UCYLtbUp0AH0ZAv5mNut1Kcg
The document provides an introduction to deep learning, including the following key points:
- Deep learning uses neural networks inspired by the human brain to perform machine learning tasks. The basic unit is an artificial neuron that takes weighted inputs and applies an activation function.
- Popular deep learning libraries and frameworks include TensorFlow, Keras, PyTorch, and Caffe. Common activation functions are sigmoid, tanh, and ReLU.
- Neural networks are trained using forward and backpropagation. Forward propagation feeds inputs through the network while backpropagation calculates errors to update weights.
- Convolutional neural networks are effective for image and visual data tasks due to their use of convolutional and pooling layers. Recurrent neural networks can process sequential data due
This document discusses artificial neural networks (ANNs) and how they are inspired by biological neural networks in the human brain. It provides details on the basic components of biological neurons (dendrites, soma, axon, synapses) and how ANNs attempt to mimic this structure. The document then describes some key aspects of ANNs, including activation functions like sigmoid, tanh, ReLU, and how neural networks work by taking input values, applying weights and an activation function, and producing an output. It focuses on ANNs for problems like regression and classification.
Neural networks are inspired by biological neurons and are used to learn relationships in data. The document defines an artificial neural network as a large number of interconnected processing elements called neurons that learn from examples. It outlines the key components of artificial neurons including weights, inputs, summation, and activation functions. Examples of neural network architectures include single-layer perceptrons, multi-layer perceptrons, convolutional neural networks, and recurrent neural networks. Common applications of neural networks include pattern recognition, data classification, and processing sequences.
Understanding Deep Learning & Parameter Tuning with MXnet, H2o Package in RManish Saraswat
Simple guide which explains deep learning and neural network with hands on experience in R using MXnet and H2o package. It also explains gradient descent and backpropagation algorithm.
Complete tutorial: http://blog.hackerearth.com/understanding-deep-learning-parameter-tuning-with-mxnet-h2o-package-r
Neural networks are computing systems inspired by the human brain that are composed of interconnected nodes similar to neurons. They can recognize complex patterns in raw data through learning algorithms. An artificial neural network consists of layers of nodes - an input layer, one or more hidden layers, and an output layer. Weights are assigned to connections between nodes and are adjusted during training to produce the desired output.
The document discusses the history and concepts of artificial intelligence and machine learning. It describes early models like the McCulloch-Pitts neuron and perceptron, and how they evolved with the introduction of backpropagation and multi-layer perceptrons using sigmoid activation functions. Key algorithms discussed include naive Bayes, k-means clustering, and decision trees. Deep learning concepts like convolutional neural networks are also covered at a high level.
The document discusses different types of machine learning paradigms including supervised learning, unsupervised learning, and reinforcement learning. It then provides details on artificial neural networks, describing them as consisting of simple processing units that communicate through weighted connections, similar to neurons in the human brain. The document outlines key aspects of artificial neural networks like processing units, connections between units, propagation rules, and learning methods.
This document provides an introduction and overview of deep learning. It begins with defining neural networks and how they are inspired by biological neurons. It then discusses different types of neural networks like single perceptrons, multi-layer perceptrons, convolutional neural networks, recurrent neural networks, and autoencoders. The document explains key concepts in deep learning like weights, biases, activation functions, loss functions, and training neural networks using gradient descent. It also clarifies terms like epochs, batches, and iterations in the training process. Finally, it touches on important hyperparameters like learning rate that control the training of neural networks.
The document discusses various activation functions used in neural networks. It explains that activation functions introduce non-linearity, which allows neural networks to learn complex relationships. Common activation functions include sigmoid, tanh, ReLU, LeakyReLU. The sigmoid and tanh functions were often used but can lead to vanishing gradients. ReLU helps address this but causes dead neurons. LeakyReLU and PReLU aim to solve the dead neuron problem. Softmax is often used for the last layer of classification networks. Choosing the right activation function depends on the problem and network type.
The document provides an overview of Convolutional Neural Networks (CNNs) including the common layers used to build CNNs such as convolutional, activation, pooling, fully connected, batch normalization, and dropout layers. It describes the functions of each layer type and includes diagrams illustrating CNN architecture. Key components like convolutional layers, pooling layers, and fully connected layers are explained in more detail. Additionally, the document discusses various activation functions used in CNNs such as ReLU, LeakyReLU, Sigmoid, Tanh, Softmax, and more. Their mathematical representations and limitations are also outlined.
The document discusses artificial neural networks and their biological inspiration. It provides details on:
- The basic structure and functioning of biological neurons
- How artificial neural networks are modeled after biological neural networks with nodes, links, weights, and activation functions
- Examples of different activation functions used in artificial neurons like threshold, sigmoid, and linear functions
- How simple logic gates can be modeled using the McCulloch-Pitts neuron model with different weight configurations
- Learning in neural networks involves adjusting the connection weights between neurons through supervised or unsupervised learning processes.
Data Science - Part VIII - Artifical Neural NetworkDerek Kane
This lecture provides an overview of biological based learning in the brain and how to simulate this approach through the use of feed-forward artificial neural networks with back propagation. We will go through some methods of calibration and diagnostics and then apply the technique on three different data mining tasks: binary prediction, classification, and time series prediction.
This chapter discusses several supervised learning networks, including perceptrons, Adaline, Madaline, backpropagation networks, and radial basis function networks. Perceptrons are the simplest form of neural networks and use a linear threshold unit to classify inputs. Backpropagation networks can solve non-linearly separable problems using gradient descent training over multiple layers. Radial basis function networks employ Gaussian kernel functions for classification and functional approximation tasks.
Web Spam Classification Using Supervised Artificial Neural Network Algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are
more efficient, generic and highly adaptive. Neural Network based technologies have high ability of
adaption as well as generalization. As per our knowledge, very little work has been done in this field using
neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised
learning algorithms of artificial neural network by creating classifiers for the complex problem of latest
web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Performance Analysis of the Sigmoid and Fibonacci Activation Functions in NGA...IOSRJVSP
Activation functions are used to transform the mixed inputs into their corresponding output counterparts. Commonly, activation functions are used as transfer functions in engineering and research. Artificial neural networks (ANN) are the preferred choice for most studies and comparisons of activation functions. The Sigmoid Activation Function is the most common and its popularity arise from the fact that it is easy to derive, its boundedness within the unit interval, and it has mathematical properties that work well with the approximation theory. On the other hand, not so common is the Fibonacci Activation Function with similar and perhaps better features than the Sigmoid. Algorithms have a broad range of applications making it plausible to suspect that different problems call for unique activation functions. The aim of this paper is to have a detailed of the role of the activation functions and then analyse the performance of two of them – the Sigmoid and the Fibonacci – in a non-ANN setup using the most basic artificially generated signals. Results show that the Fibonacci activation function performs better with the set of signals applied in the natural gradient algorithm.
Web spam classification using supervised artificial neural network algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
The document discusses machine learning and genetic algorithms. It provides definitions of machine learning as the study of processes that lead to self-improvement of machine performance through experience. It also discusses different types of learning including supervised learning, unsupervised learning, and reinforcement learning. The document then explains genetic algorithms as evolutionary algorithms that use operations like mutation and crossover to evolve solutions to problems over multiple generations.
The document discusses the Clinical Natural Language Processing (cNLP) project. It summarizes the following key points:
1) The cNLP project involves multiple investigators from various institutions with the aim of extracting structured information from unstructured clinical text using natural language processing techniques.
2) In year 1, the project focused on developing gold standard clinical corpora for evaluation and annotation guidelines. Software deliverables included modules for dependency parsing, drug extraction, and smoking status classification.
3) Going forward, the project will focus on relationship extraction, comparative effectiveness research using extracted data, and integrating the resulting natural language processing tool (cTAKES) with other clinical systems and databases.
The document provides an introduction to deep learning, including the following key points:
- Deep learning uses neural networks inspired by the human brain to perform machine learning tasks. The basic unit is an artificial neuron that takes weighted inputs and applies an activation function.
- Popular deep learning libraries and frameworks include TensorFlow, Keras, PyTorch, and Caffe. Common activation functions are sigmoid, tanh, and ReLU.
- Neural networks are trained using forward and backpropagation. Forward propagation feeds inputs through the network while backpropagation calculates errors to update weights.
- Convolutional neural networks are effective for image and visual data tasks due to their use of convolutional and pooling layers. Recurrent neural networks can process sequential data due
This document discusses artificial neural networks (ANNs) and how they are inspired by biological neural networks in the human brain. It provides details on the basic components of biological neurons (dendrites, soma, axon, synapses) and how ANNs attempt to mimic this structure. The document then describes some key aspects of ANNs, including activation functions like sigmoid, tanh, ReLU, and how neural networks work by taking input values, applying weights and an activation function, and producing an output. It focuses on ANNs for problems like regression and classification.
Neural networks are inspired by biological neurons and are used to learn relationships in data. The document defines an artificial neural network as a large number of interconnected processing elements called neurons that learn from examples. It outlines the key components of artificial neurons including weights, inputs, summation, and activation functions. Examples of neural network architectures include single-layer perceptrons, multi-layer perceptrons, convolutional neural networks, and recurrent neural networks. Common applications of neural networks include pattern recognition, data classification, and processing sequences.
Understanding Deep Learning & Parameter Tuning with MXnet, H2o Package in RManish Saraswat
Simple guide which explains deep learning and neural network with hands on experience in R using MXnet and H2o package. It also explains gradient descent and backpropagation algorithm.
Complete tutorial: http://blog.hackerearth.com/understanding-deep-learning-parameter-tuning-with-mxnet-h2o-package-r
Neural networks are computing systems inspired by the human brain that are composed of interconnected nodes similar to neurons. They can recognize complex patterns in raw data through learning algorithms. An artificial neural network consists of layers of nodes - an input layer, one or more hidden layers, and an output layer. Weights are assigned to connections between nodes and are adjusted during training to produce the desired output.
The document discusses the history and concepts of artificial intelligence and machine learning. It describes early models like the McCulloch-Pitts neuron and perceptron, and how they evolved with the introduction of backpropagation and multi-layer perceptrons using sigmoid activation functions. Key algorithms discussed include naive Bayes, k-means clustering, and decision trees. Deep learning concepts like convolutional neural networks are also covered at a high level.
The document discusses different types of machine learning paradigms including supervised learning, unsupervised learning, and reinforcement learning. It then provides details on artificial neural networks, describing them as consisting of simple processing units that communicate through weighted connections, similar to neurons in the human brain. The document outlines key aspects of artificial neural networks like processing units, connections between units, propagation rules, and learning methods.
This document provides an introduction and overview of deep learning. It begins with defining neural networks and how they are inspired by biological neurons. It then discusses different types of neural networks like single perceptrons, multi-layer perceptrons, convolutional neural networks, recurrent neural networks, and autoencoders. The document explains key concepts in deep learning like weights, biases, activation functions, loss functions, and training neural networks using gradient descent. It also clarifies terms like epochs, batches, and iterations in the training process. Finally, it touches on important hyperparameters like learning rate that control the training of neural networks.
The document discusses various activation functions used in neural networks. It explains that activation functions introduce non-linearity, which allows neural networks to learn complex relationships. Common activation functions include sigmoid, tanh, ReLU, LeakyReLU. The sigmoid and tanh functions were often used but can lead to vanishing gradients. ReLU helps address this but causes dead neurons. LeakyReLU and PReLU aim to solve the dead neuron problem. Softmax is often used for the last layer of classification networks. Choosing the right activation function depends on the problem and network type.
The document provides an overview of Convolutional Neural Networks (CNNs) including the common layers used to build CNNs such as convolutional, activation, pooling, fully connected, batch normalization, and dropout layers. It describes the functions of each layer type and includes diagrams illustrating CNN architecture. Key components like convolutional layers, pooling layers, and fully connected layers are explained in more detail. Additionally, the document discusses various activation functions used in CNNs such as ReLU, LeakyReLU, Sigmoid, Tanh, Softmax, and more. Their mathematical representations and limitations are also outlined.
The document discusses artificial neural networks and their biological inspiration. It provides details on:
- The basic structure and functioning of biological neurons
- How artificial neural networks are modeled after biological neural networks with nodes, links, weights, and activation functions
- Examples of different activation functions used in artificial neurons like threshold, sigmoid, and linear functions
- How simple logic gates can be modeled using the McCulloch-Pitts neuron model with different weight configurations
- Learning in neural networks involves adjusting the connection weights between neurons through supervised or unsupervised learning processes.
Data Science - Part VIII - Artifical Neural NetworkDerek Kane
This lecture provides an overview of biological based learning in the brain and how to simulate this approach through the use of feed-forward artificial neural networks with back propagation. We will go through some methods of calibration and diagnostics and then apply the technique on three different data mining tasks: binary prediction, classification, and time series prediction.
This chapter discusses several supervised learning networks, including perceptrons, Adaline, Madaline, backpropagation networks, and radial basis function networks. Perceptrons are the simplest form of neural networks and use a linear threshold unit to classify inputs. Backpropagation networks can solve non-linearly separable problems using gradient descent training over multiple layers. Radial basis function networks employ Gaussian kernel functions for classification and functional approximation tasks.
Web Spam Classification Using Supervised Artificial Neural Network Algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are
more efficient, generic and highly adaptive. Neural Network based technologies have high ability of
adaption as well as generalization. As per our knowledge, very little work has been done in this field using
neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised
learning algorithms of artificial neural network by creating classifiers for the complex problem of latest
web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Performance Analysis of the Sigmoid and Fibonacci Activation Functions in NGA...IOSRJVSP
Activation functions are used to transform the mixed inputs into their corresponding output counterparts. Commonly, activation functions are used as transfer functions in engineering and research. Artificial neural networks (ANN) are the preferred choice for most studies and comparisons of activation functions. The Sigmoid Activation Function is the most common and its popularity arise from the fact that it is easy to derive, its boundedness within the unit interval, and it has mathematical properties that work well with the approximation theory. On the other hand, not so common is the Fibonacci Activation Function with similar and perhaps better features than the Sigmoid. Algorithms have a broad range of applications making it plausible to suspect that different problems call for unique activation functions. The aim of this paper is to have a detailed of the role of the activation functions and then analyse the performance of two of them – the Sigmoid and the Fibonacci – in a non-ANN setup using the most basic artificially generated signals. Results show that the Fibonacci activation function performs better with the set of signals applied in the natural gradient algorithm.
Web spam classification using supervised artificial neural network algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
The document discusses machine learning and genetic algorithms. It provides definitions of machine learning as the study of processes that lead to self-improvement of machine performance through experience. It also discusses different types of learning including supervised learning, unsupervised learning, and reinforcement learning. The document then explains genetic algorithms as evolutionary algorithms that use operations like mutation and crossover to evolve solutions to problems over multiple generations.
The document discusses the Clinical Natural Language Processing (cNLP) project. It summarizes the following key points:
1) The cNLP project involves multiple investigators from various institutions with the aim of extracting structured information from unstructured clinical text using natural language processing techniques.
2) In year 1, the project focused on developing gold standard clinical corpora for evaluation and annotation guidelines. Software deliverables included modules for dependency parsing, drug extraction, and smoking status classification.
3) Going forward, the project will focus on relationship extraction, comparative effectiveness research using extracted data, and integrating the resulting natural language processing tool (cTAKES) with other clinical systems and databases.
Using Artificial Intelligence in the field of Diagnostics_Case Studies.pptRohanBorgalli
This document discusses using artificial intelligence in diagnostics. It notes that 2.5 billion gigabytes of data are produced daily, and 80% of the world's population lives in low-income communities. AI shows potential to improve diagnostics by analyzing large amounts of data and enhancing accessibility and accuracy of diagnoses. Examples discussed include using AI to detect diabetic retinopathy from eye scans and to assess risk of major depressive disorder from social media posts. Overall, the document examines how AI models can be developed and improved to better serve global healthcare needs.
This document discusses key concepts of software architecture. It makes three main points:
1) Architecture is not just a phase of development but rather is fundamental to software. Every application has an architecture and architect.
2) Considering architecture throughout the development lifecycle leads to better requirements analysis, design, implementation, testing, and evolution.
3) The "Turbine Model" visualizes development activities over time to show how architecture can be central to the process.
The document discusses the envisioned development of automated vehicle technology from 2020 to 2050. It outlines that by 2020, vehicles may have limited self-driving capabilities (Level 3 automation), major automakers are developing key self-driving technologies, and concepts show interior transformations as vehicles take over driving tasks. By 2030, vehicles may have full self-driving capabilities (Level 4 automation). By 2050, nearly all vehicles are expected to have full self-driving automation and it will be a standard feature in new vehicles. The document also includes a glossary defining technologies involved in automated driving like LIDAR, vehicle-to-vehicle communication, and vehicle platooning.
The document discusses the benefits of exercise for mental health. It states that regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help alleviate symptoms of mental illness.
This document provides an overview of an introductory course on Arduino prototyping. The course covers installing the Arduino integrated development environment and libraries, electrical components like resistors and LEDs, programming basics, and virtual prototyping tools. It then discusses the Arduino board features, different input and output types, and demonstrates building a simple LED circuit on a breadboard. The document emphasizes hands-on learning and introduces concepts like analog and digital signals to help students start prototyping with Arduino.
PCA is a dimensionality reduction technique that uses linear transformations to project high-dimensional data onto a lower-dimensional space while retaining as much information as possible. It works by identifying patterns in data and expressing the data in such a way as to highlight their similarities and differences. Specifically, PCA uses linear combinations of the original variables to extract the most important patterns from the data in the form of principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.
This document provides an overview of Rob Parker's upcoming presentation on telecommunications. The presentation will cover:
1) The history of telecommunications, from physical delivery methods to early electrical systems like the telegraph.
2) Present and future fixed (cabled) and mobile (wireless) telecommunication systems.
3) Applications of present and future telecommunications technologies.
4) Parker's personal view of where telecommunications will be in 10 years.
The presentation aims to educate attendees on the evolution of telecommunications and new technical developments, and spark debate about emerging technologies.
This document discusses factor analysis, including:
- Factor analysis is used for data reduction, scale development, and assessing dimensionality. It identifies underlying factors or dimensions from a set of interrelated variables.
- The key steps in factor analysis are computing a correlation matrix, extracting factors using methods like principal component analysis, rotating factors, and determining the optimal number of factors.
- The document provides guidance on interpreting factor analysis results and deciding how many factors best represent the data.
The document provides an introduction to time series analysis and forecasting using KNIME. It discusses key concepts in time series such as trend, seasonality, cycles and residuals. It also presents examples of time series data and applications of time series analysis. The task is to analyze electricity consumption time series data from Ireland to predict hourly consumption. Techniques like clustering, ARIMA and neural networks will be applied to generate forecasts.
The document summarizes several papers on image captioning using neural networks. It describes Karpathy and Fei-Fei (2015), which obtains image and sentence embeddings, trains them to align image patches with words, and uses an RNN to map image fragments to phrases. It also discusses Vinyals et al (2015) and Mao et al (2015), noting common themes of joint embedding spaces and differences in image processing, inputs to RNNs, and recurrence types. Evaluation metrics like R@K and Med r are also introduced.
This document provides an overview of topics to be covered in R Programming including variables, data types, data import/export, logical statements, loops, functions, data plotting and visualization, and basic statistical functions and packages. It then goes on to introduce R, explaining that it is a programming language for statistical analysis and graphical display. It discusses why R is useful for data analysis and exploration due to its large collection of tools, ability to handle big data, and open source community support. The document also covers installing R and RStudio, defining variables, common data types like vectors, matrices, arrays, lists and data frames, and basic operations and control structures like if/else statements and loops.
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1. Neural Network Activation Functions
Bharatiya Vidya Bhavan’s
Sardar Patel Institute of Technology,
Munshi Nagar, Andheri (w) Mumbai.
Neural Network Activation Functions
AICTE Sponsored Two Week FDP on
“Insights into Intelligent Automation
Machine Learning and Data science”
19th Oct to 31st Oct 2020
By
Dhananjay Kalbande
Professor, Computer
Engineering,S.P.I.T. Mumba
2. NN and ANN
NN is a simplified model of the biological neural system.
ANN is non-linear parameterized function with restricted output range.
Def.1 (By DARPA ,1988)
A neural network is a system composed of many simple processing elements operating in
parallel whose function is determined by network structure, connection strengths, and the
processing performed at computing elements or nodes.
Def.2 (By Zurada,1992)
Artificial neural systems, or neural networks, are physical cellular systems which can
acquire, store and utilize experiential knowledge.
NN and ANN
NN is a simplified model of the biological neural system.
linear parameterized function with restricted output range.
neural network is a system composed of many simple processing elements operating in
parallel whose function is determined by network structure, connection strengths, and the
processing performed at computing elements or nodes.
Artificial neural systems, or neural networks, are physical cellular systems which can
acquire, store and utilize experiential knowledge.
3. ARTIFICIAL NEURAL NET
• Information-processing system.
• Neurons process the information.
• Neurons process the information.
• The signals are transmitted by means of connection links.
• The links possess an associated weight.
• The output signal is obtained by applying activations to the net input.
ARTIFICIAL NEURAL NET
The signals are transmitted by means of connection links.
The links possess an associated weight.
The output signal is obtained by applying activations to the net input.
4. What are Activation Functions?
It perform mathematical operations on the signal output.
It is a function that is used to get the output
Why we use Activation functions with Neural Networks?
It is used to determine the output of neural net
between 0 to 1 or -1 to 1 etc. (depending upon the function).
What are Activation Functions?
It perform mathematical operations on the signal output.
put of node. It is also known as Transfer Function.
Why we use Activation functions with Neural Networks?
etwork like yes or no. It maps the resulting values i
1 to 1 etc. (depending upon the function).
5. Importance Of Activation Functions
1. Simple linear operations namely; multiply the
them across all the inputs arriving to the neuron
2. It is likely that in certain situations, the output
When, this output is fed into the further layers,
larger values, making things computationally
3. This is where the activation functions play a
3. This is where the activation functions play a
to a fix interval (e.g. between -1 and 1,0 to 1
4. The activation functions help the network
suppress the irrelevant data points.
the input by weights, add a bias and sum
neuron are performed in neural networks.
output derived above, takes a large value.
layers, they can be transformed to even
computationally uncontrollable.
a major role i.e. squashing a real-number
a major role i.e. squashing a real-number
1).
network use the important information and
6. Why should one understand the logic behind Activation Functions?
➔ Even though there are many functions in python which can be used use why is it
important to understand the activation functions?
● Ever activation function has different properties and thus they have different
applications
For example :
❏ When we have a multi classification task sigmoid activation cannot be used in
❏ When we have a multi classification task sigmoid activation cannot be used in
the output layer. Softmax activation is used to classify the output values into
different classes
❏ Relu which is a very popular activation function is always used in hidden
layers. (Return max(0,x))
Why should one understand the logic behind Activation Functions?
Even though there are many functions in python which can be used use why is it
important to understand the activation functions?
Ever activation function has different properties and thus they have different
When we have a multi classification task sigmoid activation cannot be used in
When we have a multi classification task sigmoid activation cannot be used in
activation is used to classify the output values into
which is a very popular activation function is always used in hidden
7. ANN: Biological perception
X1
W1
Y
X2
X1
W2
figure shows a simple artificial neural net with two input
ons (X1, X2) and one output neuron (Y). The inter
nected weights are given by W1 and W2.
ANN: Biological perception
input
inter
Activation Unit
9. ivation function decides, whether a neuron should be activated or not by calculating
ighted sum and further adding bias with it. The purpose of the activation function is t
ivation function decides, whether a neuron should be activated or not by calculating
ighted sum and further adding bias with it. The purpose of the activation function is to
roduce non-linearity into the output of a neuron.
ivation function decides, whether a neuron should be activated or not by calculating
ighted sum and further adding bias with it. The purpose of the activation function is to
ivation function decides, whether a neuron should be activated or not by calculating
ighted sum and further adding bias with it. The purpose of the activation function is to
10. TYPES OF ACTIVATION FUNCTIONS
ACTIVATION LEVEL – DISCRETE OR CONTINUOUS
HARD LIMIT FUCNTION (DISCRETE)
Binary Activation function
Binary Activation function
Bipolar activation function
Identity function
SIGMOIDAL ACTIVATION FUNCTION (CONTINUOUS)
Binary Sigmoidal activation function
Bipolar Sigmoidal activation function
TYPES OF ACTIVATION FUNCTIONS
DISCRETE OR CONTINUOUS
HARD LIMIT FUCNTION (DISCRETE)
SIGMOIDAL ACTIVATION FUNCTION (CONTINUOUS)
Binary Sigmoidal activation function
Bipolar Sigmoidal activation function
12. Binary step:
(x) = 1, if x >= threshold; 0 if x < threshold
Bipolar step:
(x) = 1, if x >= threshold; -1 if x < threshold
Binary sigmoid:
Activation function equations:
Binary sigmoid:
(x) = 1 / (1 + e^-x)
Bipolar sigmoid:
(x) = (1 - e^-x) / (1 + e^-x)
Hyperbolic tangent( tanh)
(x) = (1 - e^-2x) / (1 + e^-2x)
● Sigmoid
● Tanh
● Soft
● Winn
(x) = 1, if x >= threshold; 0 if x < threshold
1 if x < threshold
Activation function equations:
Others Act.Functions
● Relu
● Leaky Relu
● Adaline
● Winn
Take
All
● Delt
● Ramp
13. Some activation have their own drawbacks which can only be understood if we
dive deep and understand the activation
For Example: In Tanh and sigmoid activation if the value is close to 0 or
is found that learning becomes very sl
becomes almost 0.Thus update in wei
not decrease rapidly.
activation have their own drawbacks which can only be understood if we
dive deep and understand the activation functions.
Example: In Tanh and sigmoid activation if the value is close to 0 or -1 or 1 it
very slow.This is because slope of the function
in weights is slow and thus the cost(loss) does
Slow learning
14. ctivation function decides, whether a neuron s
eighted sum and further adding bias with it. T
introduce non-linearity into the output of a neuron.
neuron should be activated or not by calculating
with it. The purpose of the activation function is
into the output of a neuron.
15. SIGMOID
(Non-Linear Function)
Sigmoid function is an S shaped curve and
scales down large linear values to a value between
It is used for binary classification where we
above the threshold then it belongs to category
above the threshold then it belongs to category
Where x is linear weighted sum of the
If the values of x is too high the value of function
function is close to 0.
SIGMOID
Linear Function)
and major reason for using this activation func
between 0 and 1.
we set a threshold (generally 0.5) and if the v
category 1 else category 0.
category 1 else category 0.
the features (x1*w1+ x2*w2……)
function is close to 1 and if x is too low the v
16. SIGMOID FUNCTION
Sigmoid function is used in output layer of a binary
classification, where result is either 0 or 1, as value for
sigmoid function lies between 0 and 1 only so, result can be
predicted easily to be 1
1
1
1 if value is greater than
otherwise.
SIGMOID FUNCTION
Sigmoid function is used in output layer of a binary
classification, where result is either 0 or 1, as value for
sigmoid function lies between 0 and 1 only so, result can be
if value is greater than 0.5
0.5
0.5
0.5 and 0
0
0
0
17. EXAMPLE WITH PYTHON CODE
f sigmoid (x):
s=1/(1+np.exp(-x))
return s
return s
np.arrange(-6,6,0.01)
gmoid(x)
ample: x=0
gmoid(x)=½=0.5
EXAMPLE WITH PYTHON CODE
18. Tanh ACTIVATION FUNCTION
The activation that works almost always better than sigmoid function is
Tanh function also known as Tangent Hyperbolic function
Value Range :- -1 to +1
Nature :- non-linear
Usually used in hidden layers of a neur
Usually used in hidden layers of a neur
-1 to 1 hence the mean for the hidden l
it, hence helps in centering the data by bringing mean close to 0. This
makes learning for the next layer much easier.
Tanh ACTIVATION FUNCTION
The activation that works almost always better than sigmoid function is
Tangent Hyperbolic function.
ural network as it’s values lies between
ural network as it’s values lies between
n layer comes out be 0 or very close to
by bringing mean close to 0. This
makes learning for the next layer much easier.
20. Tanh Activation python code
x) = (1 - e^-2x) / (1 + e^-2x)
mport numpy as np;
f Tanh(z):
turn np.tanh(z)
xample :
z=0
nh(0)=0
nh(0)=0
anh(0) #returns 0
R
ython user defined function:
def hyperbole(x):
num = math.exp(-2*x)
return (1-num)/(1+num);
Tanh Activation python code
21. RELU
1. Stands for Rectified linear unit
activation function. Chiefly implemented in
Neural network.
2. Equation :- A(x) = max(0,x)
and 0 otherwise.
3. Value Range :- [0, inf)
3. Value Range :- [0, inf)
4. Uses :- ReLu is less compu
sigmoid because it involves
At a time only a few neuron
sparse making it efficient and easy for computation.
RELU
Rectified linear unit. It is the most widely used
activation function. Chiefly implemented in hidden layers of
A(x) = max(0,x). It gives an output x if x is positive
putationally expensive than tanh and
es simpler mathematical operations.
ons are activated making the network
sparse making it efficient and easy for computation.
23. Python Code For Relu Function
Import numpy as np
def Relu(x):
return np.max(x,0)
Example : if x is 9
Max between 9 and 0 is 9 and thus function will return 9
Thus all negative outputs of the node will be mapped to 0.
Python Code For Relu Function
Max between 9 and 0 is 9 and thus function will return 9
Thus all negative outputs of the node will be mapped to 0.
24. LEAKY RELU
1. Leaky Rectified linear unit(Leaky Relu) is an extension of the Relu
function to overcome the dying neuron problem.
2. Relu return 0 if the input is negative and hence the neuron
becomes inactive as it does not contribute to gradient flow.
becomes inactive as it does not contribute to gradient flow.
3. Leaky Relu overcomes this problem by allowing small value to
flow when the input is negative. So, if the learning is too slow
using Relu, one can try using Leaky Relu to see any improvement
happens or not.
Leaky Rectified linear unit(Leaky Relu) is an extension of the Relu
function to overcome the dying neuron problem.
Relu return 0 if the input is negative and hence the neuron
becomes inactive as it does not contribute to gradient flow.
becomes inactive as it does not contribute to gradient flow.
Leaky Relu overcomes this problem by allowing small value to
flow when the input is negative. So, if the learning is too slow
using Relu, one can try using Leaky Relu to see any improvement
25.
26. Python code for leaky Relu
def LeakyRelu(x):
if(x<0):
return 0.01*x
else:
else:
return x
Example:
x=-1
Function will return -1*0.01=-0.01
Ans=LeakyRelu() #Ans=-0.01
Python code for leaky Relu
RELU LEAKY RELU
27. SOFTMAX ACTIVATION FUNCTION
1) The softmax function is also a type of sigmoid function.
2) It is used when we are trying to handle classification problems.
3) The softmax function is a function that turns a
into a vector of K real values that sum to 1.
4) The input values can be positive, negative, zero, or greater than one,
but the softmax transforms them into values between 0 and 1, so that
but the softmax transforms them into values between 0 and 1, so that
they can be interpreted as probabilities
5) If one of the inputs is small or nega
probability, and if an input is large, t
but it will always remain between 0 and 1.
6) The softmax function is ideally used in the output layer of the classifier
where we are actually trying to atta
of each input.
SOFTMAX ACTIVATION FUNCTION
The softmax function is also a type of sigmoid function.
It is used when we are trying to handle classification problems.
The softmax function is a function that turns a vector of K real values
into a vector of K real values that sum to 1.
The input values can be positive, negative, zero, or greater than one,
but the softmax transforms them into values between 0 and 1, so that
but the softmax transforms them into values between 0 and 1, so that
probabilities.
gative, the softmax turns it into a small
e, then it turns it into a large probability,
but it will always remain between 0 and 1.
The softmax function is ideally used in the output layer of the classifier
ttain the probabilities to define the class
28. EXAMPLE : Z =
SOFTMAX
FORMULA
First we can calculate the exponential of each
top half of the softmax equation.Note that in th
than 5, 2981 is much larger than 148 due to the effect of the exponential.
h element of the input array. This is the term in th
the input elements, although 8 is only a little large
than 5, 2981 is much larger than 148 due to the effect of the exponential.
29. 2. We can obtain the normalization term, the bottom half of the softmax
equation, by summing all three exponential terms:
3. Finally, dividing by the normalization term, we obtain the softmax
output for each of the three elements. Note that there is not a single
output value because the softmax transforms an array to an array of
output value because the softmax transforms an array to an array of
the same length, in this case 3.
We can obtain the normalization term, the bottom half of the softmax
equation, by summing all three exponential terms:
Finally, dividing by the normalization term, we obtain the softmax
output for each of the three elements. Note that there is not a single
output value because the softmax transforms an array to an array of
output value because the softmax transforms an array to an array of
30.
31. Python code for softmax activation
function
def softmax_function(x):
e=2.718281 #set value of e which is 2.718
z = e**x #z=e^x
z_ = z/z.sum() #softmax definition
return z_
Or
Or
Import numpy as np
def softmax(z):
np.exp(z)
return e_x / e_x.sum()
Python code for softmax activation
32. WINNER TAKES ALL
1. Winner Takes All is based on
2. The connections between the output neurons shows the
competition between them
3. one of neurons would be ‘ON
3. one of neurons would be ‘ON
winner and others would be ‘OFF’.
4. Only the weights of winner neuron gets updated
WINNER TAKES ALL
on the competitive learning rule.
The connections between the output neurons shows the
N’ which means it would be the
N’ which means it would be the
winner and others would be ‘OFF’.
Only the weights of winner neuron gets updated
33. The learning is based on the pre
layer, say mth has the maximum
in figure. This neuron is declared as the winner.
remise that one of the neurons in the
m response due to input x, as shown
in figure. This neuron is declared as the winner.
34. EXAMPLE : Max Net
The single node whose number of inputs is
activations of all other nodes would be inact
with
f(x)= { x if x>0
{ 0 if x≤0
Python code:
-> def Winner_Takes_All() :
-> If (x>0): return x
-> else: return 0
EXAMPLE : Max Net (unsupervised learning)
is maximum would be active or winner and t
active. Max Net uses identity activation funct
35. ADALINE
(LINEAR BINARY CLASSIFIER)
All the input feature are multiplied with
heir respective weights
Add all the multiplied values .
he weighted sum is passed through a
near activation function and output of
his is compared with the target output
which is used to update the weights.
inally the output is passed through a non
near activation function like Unit step
unction.
ADALINE
(LINEAR BINARY CLASSIFIER)
36. THON CODE for Adaline
om mlxtend.classifier import Adaline
y =make_moons(n_samples=100, random_state=123)
a = Adaline(epochs=50, eta=0.05, random_seed=0)
a = Adaline(epochs=50, eta=0.05, random_seed=0)
a.fit(X, y)
THON CODE for Adaline
37. LTA RULE
Calculate the derivative of f(net)
Calculate difference between the expected
and current output of activation(o)
Multiply the derivative of f(net) with the
step 2.
step 2.
expected output of activation(d)
the difference calculated in
38. RAMP FUNCTION
Ramp activation function is used to normalize the output of neural networks within the linear
range of activation function.
RAMP FUNCTION
Ramp activation function is used to normalize the output of neural networks within the linear
39. Python code for Ramp Activation function
#if output range required is between 0 to 1
def Ramp(x):
if(x>1) : return 1
elif(x<0): return 0
else: return x
else: return x
#if output range required is between -1 and 1
def Ramp(x):
if(x>1) : return 1
elif(x<-1): return -1
else: return x
Python code for Ramp Activation function
1 and 1
40. Example of Ramp Activation
#if output range required is between 0 to 1
If x = -1 function returns 0
If x=10 function returns 1
If x=0.25 function returns 0.25
If x=0.25 function returns 0.25
#if output range required is between 0 to 1
If x = -7 function returns -1
If x=10 function returns 1
If x=0.25 function returns 0.25
Example of Ramp Activation
#if output range required is between 0 to 1
#if output range required is between 0 to 1
41. ……….Just recall
ACTIVATION FUNCTIONS ARE TRANSFER FUNCTIONS.
IT TRANFER NET INPUT SIGNAL TO OUTPUT SIGNAL.
IT GENERATE THE OUTPUT OF THE NN MODEL.
How AI can make better society and better India using non
……….Just recall
ACTIVATION FUNCTIONS ARE TRANSFER FUNCTIONS.
IT TRANFER NET INPUT SIGNAL TO OUTPUT SIGNAL.
IT GENERATE THE OUTPUT OF THE NN MODEL.
How AI can make better society and better India using non-invasive methods……