Neural networks are machine learning models inspired by the human brain that are able to learn complex patterns in data. They consist of an input layer, hidden layers, and an output layer with nodes and connections between layers that are assigned weights. The model is trained using backpropagation to minimize error by adjusting the weights for predictions. Neural networks can model very complex nonlinear relationships but require large datasets and computational power to avoid overfitting. They are widely used for classification and prediction tasks.
Online Coreset Selection for Rehearsal-based Continual LearningMLAI2
A dataset is a shred of crucial evidence to describe a task. However, each data point in the dataset does not have the same potential, as some of the data points can be more representative or informative than others. This unequal importance among the data points may have a large impact in rehearsal-based continual learning, where we store a subset of the training examples (coreset) to be replayed later to alleviate catastrophic forgetting. In continual learning, the quality of the samples stored in the coreset directly affects the model's effectiveness and efficiency. The coreset selection problem becomes even more important under realistic settings, such as imbalanced continual learning or noisy data scenarios. To tackle this problem, we propose Online Coreset Selection (OCS), a simple yet effective method that selects the most representative and informative coreset at each iteration and trains them in an online manner. Our proposed method maximizes the model's adaptation to a target dataset while selecting high-affinity samples to past tasks, which directly inhibits catastrophic forgetting. We validate the effectiveness of our coreset selection mechanism over various standard, imbalanced, and noisy datasets against strong continual learning baselines, demonstrating that it improves task adaptation and prevents catastrophic forgetting in a sample-efficient manner.
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Unce...MLAI2
Numerous recent works utilize bi-Lipschitz regularization of neural network layers to preserve relative distances between data instances in the feature spaces of each layer. This distance sensitivity with respect to the data aids in tasks such as uncertainty calibration and out-of-distribution (OOD) detection. In previous works, features extracted with a distance sensitive model are used to construct feature covariance matrices which are used in deterministic uncertainty estimation or OOD detection. However, in cases where there is a distribution over tasks, these methods result in covariances which are sub-optimal, as they may not leverage all of the meta information which can be shared among tasks. With the use of an attentive set encoder, we propose to meta learn either diagonal or diagonal plus low-rank factors to efficiently construct task specific covariance matrices. Additionally, we propose an inference procedure which utilizes scaled energy to achieve a final predictive distribution which is well calibrated under a distributional dataset shift.
Attention Is All You Need (NIPS 2017)
(Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin)
paper link: https://arxiv.org/pdf/1706.03762.pdf
Reference:
https://youtu.be/mxGCEWOxfe8 (by Minsuk Heo)
https://youtu.be/5vcj8kSwBCY (Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 14 – Transformers and Self-Attention)
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Recurrent Neural Network
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Mohammad Sabouri
https://sites.google.com/view/acrrl/
Online Coreset Selection for Rehearsal-based Continual LearningMLAI2
A dataset is a shred of crucial evidence to describe a task. However, each data point in the dataset does not have the same potential, as some of the data points can be more representative or informative than others. This unequal importance among the data points may have a large impact in rehearsal-based continual learning, where we store a subset of the training examples (coreset) to be replayed later to alleviate catastrophic forgetting. In continual learning, the quality of the samples stored in the coreset directly affects the model's effectiveness and efficiency. The coreset selection problem becomes even more important under realistic settings, such as imbalanced continual learning or noisy data scenarios. To tackle this problem, we propose Online Coreset Selection (OCS), a simple yet effective method that selects the most representative and informative coreset at each iteration and trains them in an online manner. Our proposed method maximizes the model's adaptation to a target dataset while selecting high-affinity samples to past tasks, which directly inhibits catastrophic forgetting. We validate the effectiveness of our coreset selection mechanism over various standard, imbalanced, and noisy datasets against strong continual learning baselines, demonstrating that it improves task adaptation and prevents catastrophic forgetting in a sample-efficient manner.
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Unce...MLAI2
Numerous recent works utilize bi-Lipschitz regularization of neural network layers to preserve relative distances between data instances in the feature spaces of each layer. This distance sensitivity with respect to the data aids in tasks such as uncertainty calibration and out-of-distribution (OOD) detection. In previous works, features extracted with a distance sensitive model are used to construct feature covariance matrices which are used in deterministic uncertainty estimation or OOD detection. However, in cases where there is a distribution over tasks, these methods result in covariances which are sub-optimal, as they may not leverage all of the meta information which can be shared among tasks. With the use of an attentive set encoder, we propose to meta learn either diagonal or diagonal plus low-rank factors to efficiently construct task specific covariance matrices. Additionally, we propose an inference procedure which utilizes scaled energy to achieve a final predictive distribution which is well calibrated under a distributional dataset shift.
Attention Is All You Need (NIPS 2017)
(Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin)
paper link: https://arxiv.org/pdf/1706.03762.pdf
Reference:
https://youtu.be/mxGCEWOxfe8 (by Minsuk Heo)
https://youtu.be/5vcj8kSwBCY (Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 14 – Transformers and Self-Attention)
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Recurrent Neural Network
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Mohammad Sabouri
https://sites.google.com/view/acrrl/
Packet hiding methods for preventing selective jamming attacksShaik Irfan
This project mainly describes how a data can be send securely via a network without getting being hacked by any intruder.here we use various different kind of cryptographic principal and secure mechanism where in which it complete protection to our data
TEST-COST-SENSITIVE CONVOLUTIONAL NEURAL NETWORKS WITH EXPERT BRANCHESsipij
It has been proven that deeper convolutional neural networks (CNN) can result in better accuracy in many
problems, but this accuracy comes with a high computational cost. Also, input instances have not the same
difficulty. As a solution for accuracy vs. computational cost dilemma, we introduce a new test-cost-sensitive
method for convolutional neural networks. This method trains a CNN with a set of auxiliary outputs and
expert branches in some middle layers of the network. The expert branches decide to use a shallower part
of the network or going deeper to the end, based on the difficulty of input instance. The expert branches
learn to determine: is the current network prediction is wrong and if the given instance passed to deeper
layers of the network it will generate right output; If not, then the expert branches stop the computation
process. The experimental results on standard dataset CIFAR-10 show that the proposed method can train
models with lower test-cost and competitive accuracy in comparison with basic models.
Distance-based bias in model-directed optimization of additively decomposable...Martin Pelikan
For many optimization problems it is possible to define a distance metric between problem variables that correlates with the likelihood and strength of interactions between the variables. For example, one may define a metric so that the dependencies between variables that are closer to each other with respect to the metric are expected to be stronger than the dependencies between variables that are further apart. The purpose of this paper is to describe a method that combines such a problem-specific distance metric with information mined from probabilistic models obtained in previous runs of estimation of distribution algorithms with the goal of solving future problem instances of similar type with increased speed, accuracy and reliability. While the focus of the paper is on additively decomposable problems and the hierarchical Bayesian optimization algorithm, it should be straightforward to generalize the approach to other model-directed optimization techniques and other problem classes. Compared to other techniques for learning from experience put forward in the past, the proposed technique is both more practical and more broadly applicable.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
Abstract— This presents a comprehensible neural network tree (CNNTREE). CNNTREE is a proposed general modular neural network structure, where each node in this tree is a comprehensible expert neural network (CENN). One advantage of using CNNTREE is that it is a “gray box”; because it can be interpreted easily for symbolic systems; where each node in the CNNTREE is equivalent for symbolic operator in the symbolic system. Another advantage of CNNTREE is that it could be trained as any normal multi layer feed forward neural network. An evolutionary algorithm is given for designing the CNNTREE. Back propagation is also checked as local learning algorithm that fits for real time learning constraints. The tree generalization and training performance are examined using experiments with a digit recognition problem.
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
Examinations of humans' central nervous systems inspired the concept of artificial neural networks. In an artificial neural network, simple artificial nodes, known as "neurons", "neurodes", "processing elements" or "units", are connected together to form a network which mimics a biological neural network
Awarded your GSA Schedule? What's Next?Cristi Kaib
Has your company been recently awarded a GSA Schedule? Or maybe you have been on it for a while and are not using it to it's full potential? This Series will help.
Packet hiding methods for preventing selective jamming attacksShaik Irfan
This project mainly describes how a data can be send securely via a network without getting being hacked by any intruder.here we use various different kind of cryptographic principal and secure mechanism where in which it complete protection to our data
TEST-COST-SENSITIVE CONVOLUTIONAL NEURAL NETWORKS WITH EXPERT BRANCHESsipij
It has been proven that deeper convolutional neural networks (CNN) can result in better accuracy in many
problems, but this accuracy comes with a high computational cost. Also, input instances have not the same
difficulty. As a solution for accuracy vs. computational cost dilemma, we introduce a new test-cost-sensitive
method for convolutional neural networks. This method trains a CNN with a set of auxiliary outputs and
expert branches in some middle layers of the network. The expert branches decide to use a shallower part
of the network or going deeper to the end, based on the difficulty of input instance. The expert branches
learn to determine: is the current network prediction is wrong and if the given instance passed to deeper
layers of the network it will generate right output; If not, then the expert branches stop the computation
process. The experimental results on standard dataset CIFAR-10 show that the proposed method can train
models with lower test-cost and competitive accuracy in comparison with basic models.
Distance-based bias in model-directed optimization of additively decomposable...Martin Pelikan
For many optimization problems it is possible to define a distance metric between problem variables that correlates with the likelihood and strength of interactions between the variables. For example, one may define a metric so that the dependencies between variables that are closer to each other with respect to the metric are expected to be stronger than the dependencies between variables that are further apart. The purpose of this paper is to describe a method that combines such a problem-specific distance metric with information mined from probabilistic models obtained in previous runs of estimation of distribution algorithms with the goal of solving future problem instances of similar type with increased speed, accuracy and reliability. While the focus of the paper is on additively decomposable problems and the hierarchical Bayesian optimization algorithm, it should be straightforward to generalize the approach to other model-directed optimization techniques and other problem classes. Compared to other techniques for learning from experience put forward in the past, the proposed technique is both more practical and more broadly applicable.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
Abstract— This presents a comprehensible neural network tree (CNNTREE). CNNTREE is a proposed general modular neural network structure, where each node in this tree is a comprehensible expert neural network (CENN). One advantage of using CNNTREE is that it is a “gray box”; because it can be interpreted easily for symbolic systems; where each node in the CNNTREE is equivalent for symbolic operator in the symbolic system. Another advantage of CNNTREE is that it could be trained as any normal multi layer feed forward neural network. An evolutionary algorithm is given for designing the CNNTREE. Back propagation is also checked as local learning algorithm that fits for real time learning constraints. The tree generalization and training performance are examined using experiments with a digit recognition problem.
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
Examinations of humans' central nervous systems inspired the concept of artificial neural networks. In an artificial neural network, simple artificial nodes, known as "neurons", "neurodes", "processing elements" or "units", are connected together to form a network which mimics a biological neural network
Awarded your GSA Schedule? What's Next?Cristi Kaib
Has your company been recently awarded a GSA Schedule? Or maybe you have been on it for a while and are not using it to it's full potential? This Series will help.
La evolución del notariado desde el punto de vista del agente, las funciones de este y con el transcurrir de las civilizaciones, con la evolución de lo positivizado.
Deep learning has renewed interest in computational creativity. Can machines be creative? In which sense? And why this would be useful? We argue current creative AI systems are stuck: they explore combination, analogy or random, but the value of the objects are provided by the system designer.
The only way to creative AI is to develop agents building their own value.
We also argue: the generative potential of deep learning is understudied.
Current focus is on likelihood - whereas creativity is unlikely.
We present an implementation of these ideas on the MNIST handwritten digits dataset - to create symbols that could have been digits (e.g. in an imaginary culture) but that are not.
Jeff Johnson, Research Engineer, Facebook at MLconf NYCMLconf
Hacking GPUs for Deep Learning: GPUs have revolutionized machine learning in recent years, and have made both massive and deep multi-layer neural networks feasible. However, misunderstandings on why they seem to be winning persist. Many of deep learning’s workloads are in fact “too small” for GPUs, and require significantly different approaches to take full advantage of their power. There are many differences between traditional high-performance computing workloads, long the domain of GPUs, and those used in deep learning. This talk will cover these issues by looking into various quirks of GPUs, how they are exploited (or not) in current model architectures, and how Facebook AI Research is approaching deep learning programming through our recent work.
Im Jahr 2009 identifizierte Googles Chefökonom Hal Varian "Statistiker" als "sexiest Job des 21. Jahrhunderts"; seit 2011 ruft IBM die Ära des "Cognitive Computing" aus, die Ära der denkenden Maschinen. Google DeepMinds 2016er Veröffentlichung, die den Aufbau eines selbstlernenden Systems zum Meistern des hochkomplexen Spiels "Go" beschreibt, demonstriert schliesslich: Tätigkeiten, die zuvor uns Menschen vorbehalten waren, liegen nun im Bereich des maschinell Möglichen. Gleichzeitig enwickelt sich das Berufsbild des "Data Scientists" bzw. der "Data Smarts" rasant.
Der Vortrag wirft ein Licht auf diese Entwicklungen aus dem Blickwinkel eines Data Scientists und Enthusiasten in Künstlicher Intelligenz. Was ist bereits Realität, was könnte bald real werden, was steckt dahinter - und was bedeutet das für einen Wirtschaftsingenieur?
Deep Learning Interview Questions And Answers | AI & Deep Learning Interview ...Simplilearn
This Deep Learning interview questions and answers presentation will help you prepare for Deep Learning interviews. This presentation is ideal for both beginners as well as professionals who are appearing for Deep Learning, Machine Learning or Data Science interviews. Learn what are the most important Deep Learning interview questions and answers and know what will set you apart in the interview process.
Some of the important Deep Learning interview questions are listed below:
1. What is Deep Learning?
2. What is a Neural Network?
3. What is a Multilayer Perceptron (MLP)?
4. What is Data Normalization and why do we need it?
5. What is a Boltzmann Machine?
6. What is the role of Activation Functions in neural network?
7. What is a cost function?
8. What is Gradient Descent?
9. What do you understand by Backpropagation?
10. What is the difference between Feedforward Neural Network and Recurrent Neural Network?
11. What are some applications of Recurrent Neural Network?
12. What are Softmax and ReLU functions?
13. What are hyperparameters?
14. What will happen if learning rate is set too low or too high?
15. What is Dropout and Batch Normalization?
16. What is the difference between Batch Gradient Descent and Stochastic Gradient Descent?
17. Explain Overfitting and Underfitting and how to combat them.
18. How are weights initialized in a network?
19. What are the different layers in CNN?
20. What is Pooling in CNN and how does it work?
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.
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
Learn more at: https//www.simplilearn.com
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
2. What is it?
Based on neuron network in the brain – learning
capability.
Classification/prediction
Supports very complex relationship between
predictors and a response.....it learns.
Courtesy of brainblogger.com
5. Structure – explained (cont.)
Input nodes
Consist of the predictors (ex. #bedrooms, ft2 for
home prices)
Hidden Layer nodes
Each Hidden Layer node receives input from all
Input nodes
Output: g(weighted sums of inputs + bias node)
Where g() = Linear, exponential, or logistic/sigmoidal
function
Output nodes
Output = the prediction of the model
Output: Same equation as Hidden Layer node
Use a cutoff value for binary responses
6. Rules of thumb
Normalize numerical variables
; where variable is within range of
[a,b], (a<b)
Create dummy variables for categorical
variables
Ordinal: m fractions between [0,1]
Nominal: transform into m-1 dummies
p predictors = p nodes
Setting weights/node bias‟: Start at 0.00 ± 0.05
Transform highly skewed predictors. Ex. log
transform
7. Training the Model
Back Propagation
Algorithm – most popular to update weights
(Learning)
Uses difference between predicted and actual value
(error) to determine the weight
Weight – skews the input values to each node
This is distributed evenly to each hidden node back through
the system
Two updating methods
Case updating – after each observation (or trial)
Completely running through every observation (or trial) is
called an Epoch or Sweep or Iteration
Batch updating – Entire training set is run then sum of
errors is used
8. Training the Model
When do we stop?
We never stop learning…. jk
1. Only incremental differences (diminished return)
2. Misclassification has hit reasonable threshold
3. You cant run no mo….. You have reached your
limit
Now to the Neural Net
9. Avoiding Overfitting
Causes error rate to be too large
Important to limit the number of training epochs
Detected by examining the performance of the
validation set and seeing when it starts to
deteriorate
10. Required User Input
Deciding the network architecture
Specify number of hidden layers and nodes in
each layer
Trial-and-error based on experience
11. Recommendations
Number of hidden layers - should usually be 1
because even just one layer can capture complex
relationships between predictors
Size of hidden layer – start with the same number
of nodes as your number of predictors, then
decrease or increase accordingly. Too many can
lead to overfitting
Number of Outputs – should equal the number of
classes. For binary, a single node is sufficient.
12. Advantages of Neural Networks
Good predictive performance
Due to ability to capture complicated relationships
and high tolerance to noisy data
13. Weaknesses
Cannot provide insight into structure of the
relationship
Flexibility becomes a weakness when dealing
with small training sets
Long run-time can hinder performance when realtime predictions are necessary
14. Facebook and Neural Networks
Facebook‟s AI Team is building neural networks
to better target advertising and News Feed
inclusion
“it has to be able to turn „I <3 u babe‟ into a series
of machine learning events, from an increase to
said babe‟s visibility in the News Feed to an alert
if she changes her relationship status.”
“Deep learning is about making data analysis
sophisticated enough to derive your personality
from your natural social output.”
http://www.extremetech.com/computing/167179-facebook-is-working-on-deep-learning-neural-networks-to-learn-even-more-about-yourpersonal-life
