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.
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 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.
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.
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”
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Professor, Computer
Engineering,S.P.I.T. Mumba
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.
Deep learning is a subset of machine learning that uses artificial neural networks. Neural networks are composed of interconnected layers of nodes that process input data. Activation functions introduce non-linearity between layers to increase the model's ability to learn complex patterns. Models are trained via backpropagation to minimize loss by adjusting weights to better match predictions to actual outputs. Overfitting can occur if the model becomes too complex for the data.
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.
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.
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 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.
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.
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 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.
Deep learning is a subset of machine learning that uses artificial neural networks. Neural networks are composed of interconnected layers of nodes that process input data. Activation functions introduce non-linearity between layers to increase the model's ability to learn complex patterns. Models are trained via backpropagation to minimize loss by adjusting weights to better match predictions to actual outputs. Overfitting can occur if the model becomes too complex for the data.
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.
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https://www.youtube.com/channel/UCYLtbUp0AH0ZAv5mNut1Kcg
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.
Data Science - Part VIII - Artifical Neural NetworkDerek Kane
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Deep Learning Interview Questions And Answers | AI & Deep Learning Interview ...Simplilearn
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- Tensors are multidimensional arrays that represent inputs, outputs, and parameters of deep learning models in TensorFlow. They are the fundamental data structure that flows through graphs in TensorFlow.
- The main programming elements in TensorFlow include constants, variables, placeholders, and sessions. Constants are parameters whose values do not change, variables allow adding trainable parameters, placeholders feed data from outside the graph, and sessions run the graph to evaluate nodes.
Activation functions and Training Algorithms for Deep Neural networkGayatri Khanvilkar
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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.
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.
The document discusses the practical considerations for training neural networks using gradient descent. It addresses choosing an initial learning rate and weights, whether to use batch or online training, selecting an appropriate activation function, avoiding flat spots and local minima in the error function, and knowing when to stop training. The key factors that influence successful training of neural networks using gradient descent are preprocessing data, initialization, learning rate, training method, activation functions, and stopping criteria.
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The most famous example is the logistic function. Other big ones are tanh and arc tan.
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This document provides an overview of deep learning concepts including neural networks, supervised and unsupervised learning, and key terms. It explains that deep learning uses neural networks with many hidden layers to learn features directly from raw data. Supervised learning algorithms learn from labeled examples to perform classification or regression on unseen data. Unsupervised learning finds patterns in unlabeled data. Key terms defined include neurons, activation functions, loss functions, optimizers, epochs, batches, and hyperparameters.
A perceptron is a basic model of an artificial neuron that can be used as a binary classifier. A single layer perceptron introduced by Rosenblatt in 1957 uses a step activation function to classify inputs into two classes. It can only handle linearly separable problems with a binary target. The bias helps shift the activation function and the weights are adjusted during training to correctly classify inputs. A multi-layer perceptron can handle non-linear problems using hidden layers between the input and output layers.
- The document discusses multi-layer perceptrons (MLPs), a type of artificial neural network. MLPs have multiple layers of nodes and can classify non-linearly separable data using backpropagation.
- It describes the basic components and working of perceptrons, the simplest type of neural network, and how they led to the development of MLPs. MLPs use backpropagation to calculate error gradients and update weights between layers.
- Various concepts are explained like activation functions, forward and backward propagation, biases, and error functions used for training MLPs. Applications mentioned include speech recognition, image recognition and machine translation.
IRJET- Machine Learning based Object Identification System using PythonIRJET Journal
This document presents a machine learning based object identification system using convolutional neural networks (CNNs) in Python. The system is trained on a dataset of cat and dog images and aims to identify objects in input images. The document compares different CNN structures using various activation functions and classifiers. It finds that a model with a ReLU activation function and sigmoid classifier achieved the highest classification accuracy of around 90.5%. The system demonstrates how CNNs can be used for image classification tasks in machine learning.
L1 and L2 loss functions are used to minimize error during machine learning model training. The L1 loss function minimizes the sum of the absolute differences between true and predicted values, while the L2 loss function minimizes the sum of squared differences. These loss functions help the model adjust its parameters to reduce error via backpropagation. The L1 loss function is generally better when outliers are present in the data, as it is not as heavily influenced by outliers as the L2 loss function.
The document discusses different regularization techniques used in deep neural networks to prevent overfitting, including dropout, L1/L2 regularization, and early stopping. Dropout works by temporarily removing neurons from the network during training, forcing other neurons to learn redundant representations to compensate. L1 and L2 regularization add a penalty term to the loss function to discourage large weights. Early stopping monitors validation error during training and stops training if validation error begins to increase, indicating overfitting. These techniques help deep neural networks generalize better to unseen data.
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What are activation functions and why do we need those.pdf
1. What are activation functions and why
do we need those?
Activation functions are functions which are used in the Artificial Neural Networks to
capture the complexities inside the data. A neural network without an activation
function is just a simple regression model. The activation function does the non-
linear transformation to the input making it capable to learn and perform more
complex tasks.We introduce non-linearity in each layer through activation functions.
2. Let us assume there are 3 hidden layers, 1 input and 1 output layer.
W1-Weight matrix between Input layer and first hidden layer
W2-Weight matrix between first hidden layer and second hidden layer
W3-Weight matrix between second hidden layer and third hidden layer
W4-Weight matrix between third hidden layer and output layer
Below mentioned equations represents a feedforward neural network.
If we stack multiple layers, we can see output layer as a function:
3. What are Ideal qualities of an activation function:
The activation function generally introduce non-linearity in the network to capture the
complex relations between input features and output variable/class.
2. Continuously differentiable:
The activation function needs to be differentiable since neural networks are generally
trained using gradient descent process or to enable gradient based optimization
methods.
3. Zero centered:
Zero centered activations functions makes sure that mean activation value is around
0. This is important because convergence is usually seen faster on normalized data.
I have explained many of the commonly used activation below, some are zero
centered some are not. Mostly when we have a activation function which is not zero
centered we tend to use normalization layers like batch normalization to mitigate this
issue.
4. Computational expense should be low:
Activation functions are used in each layer of the network and is computed a lot of
times, hence its computation should be easy and not very computationally
expensive.
5. Killing gradients:
Activation functions like sigmoid has a saturation problem where the value doesn’t
change much for large negative and large positive values.
The derivative of the sigmoid function gets very small there which in turn prevents
the updating of the weights in initial layers during backpropagation and hence the
network doesn’t learn effectively. This should be avoided to learn patterns in the data
and hence the activation function should not ideally suffer from this issue.
4. Most commonly used activation functions:
In this section we will go over different activation functions.
The sigmoid function is defined as:
The sigmoid function is a type of activation function which has a characteristic “S”
shaped curve which has domain of all real numbers and output between 0 and 1. An
undesirable property of the sigmoid function is that the activation of the neuron
saturates either at 0 or 1 when the input from the neuron is either large positive and
large negative. It is also non-zero centered which makes neural network learning
difficult. In almost majority of the cases, it is always better to use Tanh activation
function instead of sigmoid activation function.
2. Tanh function -
tanh curve
Tanh has just one advantage over sigmoid function that it is zero-centered and it’s
value is binded between -1 and 1.
5. 3. RELU(Rectified Linear Unit) -
RELU plot
RELU is one of the many non zero-centered activation function and given this
disadvantage it is still widely used because of the advantages it has. It
is computationally very inexpensive, does not cause saturation and does not cause
the vanishing gradient problem. The RELU function doesn’t have a higher limit,
hence it has a problem of exploding activations and on the other hand for negative
values, it has 0 activation and hence it completely ignores the nodes with negative
values. Hence it suffers from “dying relu” problem.
Dying ReLU problem: During the backpropagation process, the weights and biases
for some neurons are not updated because its nature where activation is zero for
negative values. This might create dead neurons which never get activated.
6. 4. Leaky RELU -
Leaky RELU is a type of activation function based on RELU function with a small
slope for negative values instead of zero.
Leaky RELU function
Here, alpha is generally set to 0.01. It solves the “dying RELU” problem and also its
value is generally small and is not set near to 1 since it will only be a linear function
then.
If we use alpha as hyperparameter for each neuron, it becomes a PReLU or
parametrized RELU function.
7. 5. ReLU6 -
This version of ReLU function is basically a ReLU function restricted on the positive
side.
Image credit:pytorch
This helps in containing the activation function for large input positive values and
hence stops the gradient to go to inf value.
6. Exponential Linear Units (ELUs) Function -
Exponential Linear Unit is also a version of ReLU that modifies the slope of the
negative part of the function.
This activation function also avoids dead ReLU problem but it has exploding gradient
problem because of no constraint on the activations for large positive values.
8. 7. Softmax activation function -
It often used in the last activation layer of a neural network to normalize the output of
a network to a probability value that in turn is mapped to each class which helps us
in deciding the probability of output belonging to each class with respect to given
inputs. It is popularly used for multi-class classification problems.
I hope you enjoyed reading this. I have tried to cover many of the activation functions
which are commonly used in Neural Networks.
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