Introduction to optimization for deep learning
Download as PPTX, PDF0 likes162 views
Although stochastic gradient descent (SGD) resolves some issues of gradient descent (GD), as a slow and costly process, it results in fluctuations around the local minima. Mini-batch gradient descent was suggested to get the middle point reducing fluctuations observed in SGD and cost of GD. Benefit of mini-batch gradient descent is usually reported in deep learning (DL) setting as DL models usually are built training on large datasets.
1 of 10
Download to read offline
Recommended
Project Health.doc
Project Health.docPatricia Benedict, PMP
?
This document defines key project management terms related to project budgets, schedules, costs, and performance. It explains what each term represents, such as Budget at Complete (BAC) representing the total planned budget, Actual Costs (AC) representing what has been actually spent so far, and Earned Value (EV) representing how much value has been earned based on work completed. It also defines variances like Cost Variance (CV) and Schedule Variance (SV), as well as performance indexes like Cost Performance Index (CPI), Schedule Performance Index (SPI), and To-Complete Performance Index (TCPI) that indicate if a project is on budget and on schedule. Estimates like Estimate at Completion (EACQSpiders - Chapter- 3 Synchronization point
QSpiders - Chapter- 3 Synchronization pointQspiders - Software Testing Training Institute
?
Synchronizing Your Test
Tests can be synchronized either of the ways
Synchronization point
Exist or Wait statements
Increase the default timeout settings
Training from High End Compute
Training from High End Computemichael bane
?
Outlining our training on high end compute (eg HPC) and energy efficient computing, tailored to you, covering CPU and emerging hardwares and a variety of software/languauesAn overview of gradient descent optimization algorithms.pdf
An overview of gradient descent optimization algorithms.pdfvudinhphuong96
?
This document provides an overview of gradient descent optimization algorithms. It discusses various gradient descent variants including batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent. It describes the trade-offs between these methods in terms of accuracy, time, and memory usage. The document also covers challenges with mini-batch gradient descent like choosing a proper learning rate. It then discusses commonly used optimization algorithms to address these challenges, including momentum, Nesterov accelerated gradient, Adagrad, Adadelta, RMSprop, and Adam. It provides visualizations to explain how momentum and Nesterov accelerated gradient work to help accelerate SGD.logisticregression-190726150723.pdf
logisticregression-190726150723.pdfSuaibDanish
?
Logistic regression is a classification machine learning algorithm that predicts categorical dependent variables. It works by training a model on labeled data to understand the relationship between independent variables and the target variable. The model uses a logistic function to output probabilities between 0 and 1, which can then be used to classify observations into categories. Some common applications of logistic regression include predicting if a user will pay for a streaming service, the likelihood of a sports team winning, or classifying text as positive or negative. It is a simple yet effective algorithm that is widely used as a baseline for performance comparison.Logistic regression : Use Case | Background | Advantages | Disadvantages
Logistic regression : Use Case | Background | Advantages | DisadvantagesRajat Sharma
?
This slide will help you to understand the working of logistic regression which is a type of machine learning model along with use cases, pros and cons.Training Deep Networks with Backprop (D1L4 Insight@DCU Machine Learning Works...
Training Deep Networks with Backprop (D1L4 Insight@DCU Machine Learning Works...Universitat Politècnica de Catalunya
?
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.Advanced Optimization for the Enterprise Webinar
Advanced Optimization for the Enterprise WebinarSigOpt
?
SigOpt provides an optimization platform and techniques to help practitioners solve machine learning problems more efficiently. It allows users to frame problems as black box optimization and optimize multiple competing metrics. SigOpt's parallel optimization capabilities also help fully utilize available compute resources to accelerate results. Case studies demonstrated significant speedups and accuracy gains from SigOpt compared to random search and grid search.An overview of gradient descent optimization algorithms 
An overview of gradient descent optimization algorithms Hakky St
?
This document provides an overview of various gradient descent optimization algorithms that are commonly used for training deep learning models. It begins with an introduction to gradient descent and its variants, including batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent. It then discusses challenges with these algorithms, such as choosing the learning rate. The document proceeds to explain popular optimization algorithms used to address these challenges, including momentum, Nesterov accelerated gradient, Adagrad, Adadelta, RMSprop, and Adam. It provides visualizations and intuitive explanations of how these algorithms work. Finally, it discusses strategies for parallelizing and optimizing SGD and concludes with a comparison of optimization algorithms.Methods of Optimization in Machine Learning
Methods of Optimization in Machine LearningKnoldus Inc.
?
In this session we will discuss about various methods to optimise a machine learning model and, how we can adjust the hyper-parameters to minimise the cost function.Bagging.pptx
Bagging.pptxComsatsSahiwal1
?
The document discusses bagging, an ensemble machine learning method. Bagging (bootstrap aggregating) uses multiple models fitted on random subsets of a dataset to improve stability and accuracy compared to a single model. It works by training base models in parallel on random samples with replacement of the original dataset and aggregating their predictions. Key benefits are reduced variance, easier implementation through libraries like scikit-learn, and improved performance over single models. However, bagging results in less interpretable models compared to a single model.Gradient Descent DS Rohit Sharma fench knjs.pptx
Gradient Descent DS Rohit Sharma fench knjs.pptxbusinessmarketing100
?
this is the ppt of rohit sharma of gradient descent for present in theathre.Tuning for Systematic Trading: Talk 2: Deep Learning
Tuning for Systematic Trading: Talk 2: Deep LearningSigOpt
?
This talk explains how to train deep learning and other expensive models with parallelism and multitask optimization to reduce wall clock time. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.Getting Started with Server-Side Testing
Getting Started with Server-Side TestingOptimizely
?
This document discusses server-side testing and provides guidance on getting started with it. It covers:
1) The benefits of server-side testing like improved user experience and ability to test backend changes.
2) Best practices like defining success metrics, laying foundations through separation of concerns and middleware, and targeting experiments through attributes.
3) Examples of server-side experiments like algorithms, onboarding flows, and trial durations.[Webinar] Getting started with server-side testing - presented by WiderFunnel...![[Webinar] Getting started with server-side testing - presented by WiderFunnel...](https://cdn.slidesharecdn.com/ss_thumbnails/serversidetestingwebinardeck-171213214850-thumbnail.jpg?width=560&fit=bounds)
[Webinar] Getting started with server-side testing - presented by WiderFunnel...Chris Goward
?
One of the most difficult aspects of deep experimentation ― which requires a full stack solution and server-side testing ― is laying a solid foundation for success. In this webinar, you'll learn best practices for going beyond client-side testing, and implementing a full stack experimentation strategy to drive results on the entire customer journey.Understanding parametersniffing sqlsat
Understanding parametersniffing sqlsatSanil Mhatre
?
Parameter Sniffing is usually thought of as the bad guy, in association with a performance problem in your database. Contrary to the popular belief, Parameter Sniffing is usually the good guy, continuously working under the hood to help your database applications run faster. However, it can sometimes go wrong, causing severe performance degradation of your queries.
In this session we will discuss the workings of Parameter Sniffing and demonstrate how it helps improve the performance of your database applications. We will also explore how Parameter Sniffing can go wrong and its impact. Several ways to fix bad Parameter Sniffing will be demonstrated to help make an appropriate choice for your scenario.IM426 3A G5.ppt
IM426 3A G5.pptMohamedSalem979344
?
This document describes how to use the Analytic Hierarchy Process (AHP) to make a multi-criteria decision about purchasing an inventory management system. It involves defining the goal, criteria, and alternatives in a hierarchy. Pairwise comparisons are made between criteria and alternatives to assign weights. The weighted scores are calculated and the alternative with the highest score is selected. In this example, the goal is to purchase a system, the criteria are cost, functionality, supplier reputation, and user services, and the alternatives are Systems A, B, and C. System A is determined to have the highest total weighted score, making it the best choice.MACHINE LEARNING YEAR DL SECOND PART.pptx
MACHINE LEARNING YEAR DL SECOND PART.pptxNAGARAJANS68
?
The document discusses various concepts related to machine learning models including prediction errors, overfitting, underfitting, bias, variance, hyperparameter tuning, and regularization techniques. It provides explanations of key terms and challenges in machine learning like the curse of dimensionality. Cross-validation methods like k-fold are presented as ways to evaluate model performance on unseen data. Optimization algorithms such as gradient descent and stochastic gradient descent are covered. Regularization techniques like Lasso, Ridge, and Elastic Net are introduced.Using Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning ModelsSigOpt
?
1. Tuning machine learning models is challenging due to the large number of non-intuitive hyperparameters.
2. Traditional tuning methods like grid search are computationally expensive and can find local optima rather than global optima.
3. Bayesian optimization uses Gaussian processes to build statistical models from prior evaluations to determine the most promising hyperparameters to test next, requiring far fewer evaluations than traditional methods to find better performing models.Using Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning ModelsScott Clark
?
1) Bayesian optimization can be used to efficiently tune the hyperparameters of machine learning models, requiring far fewer evaluations than standard random search or grid search methods to find good hyperparameters.
2) It builds a statistical model called a Gaussian process to model the objective function based on previous evaluations, and uses this to select the most promising hyperparameters to evaluate next in order to optimize an objective metric like accuracy.
3) SigOpt is a service that uses Bayesian optimization to tune machine learning models, outperforming expert humans on tasks like classifying images from CIFAR10 and reducing error rates more than standard methods.Meetup_Consumer_Credit_Default_Vers_2_All
Meetup_Consumer_Credit_Default_Vers_2_AllBernard Ong
?
The document discusses a team's approach to a Kaggle challenge to predict consumer credit default. It outlines their goals, dataset details, modeling strategy using an agile process, and key results. Their parallel modeling approach included feature analysis, single/ensemble models, stacking, voting classifiers, and Bayesian optimization. Their top-scoring model achieved an AUC of 0.88912, placing first on the private Kaggle leaderboard. Lessons included the importance of cross-validation, hyperparameter tuning, and following an agile process.Algorithmic pricing: Forecasting and Pricing
Algorithmic pricing: Forecasting and PricingTofigh Naghibi
?
Algorithmic pricing: Forecasting and Pricing
How to use differential programming together with large scale models to find optimal prices in e-commerce?Tuning 2.0: Advanced Optimization Techniques Webinar
Tuning 2.0: Advanced Optimization Techniques WebinarSigOpt
?
This webinar, hosted by SigOpt co-founder and CEO Scott Clark, explains how advanced features can help you achieve your modeling goals. These features include metric definition and multimetric optimization, conditional parameters, and multitask optimization for long training cycles.Using SigOpt to Tune Deep Learning Models with Nervana Cloud
Using SigOpt to Tune Deep Learning Models with Nervana CloudSigOpt
?
This document discusses using SigOpt to tune deep learning models. It notes that tuning deep learning systems is non-intuitive and expert-intensive using traditional random search or grid search methods. SigOpt provides a more efficient approach using Bayesian optimization to suggest optimal hyperparameters after each trial, reducing wasted expert time and computation. The document provides examples applying SigOpt to tune convolutional neural networks on CIFAR10, demonstrating a 1.6% reduction in error rate over expert tuning with no wasted trials.InstructGPT: Follow instructions with human feedback
InstructGPT: Follow instructions with human feedbackYan Xu
?
Introducing InstructGPT paper on follow instructions with human feedbackReinforcement Learning 8: Planning and Learning with Tabular Methods
Reinforcement Learning 8: Planning and Learning with Tabular MethodsSeung Jae Lee
?
A summary of Chapter 8: Planning and Learning with Tabular Methods of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
Check my website for more slides of books and papers!
https://www.endtoend.aiBuilding an algorithmic price management system using ML
Building an algorithmic price management system using MLGrid Dynamics
?
Leading retailers and marketplaces like Amazon and Groupon implemented very sophisticated, and highly automated price management solutions over the recent years. These solutions are able to dynamically change prices every several minutes, intelligently personalize discounts, and respond to competitor moves in order to optimize profits and inventory. It creates pressure on other retailers and manufacturers, and challenges traditional price management techniques, making it increasingly more complex to stay competitive and profitable.
In this talk, we will discuss how predictive modeling and reinforcement learning can be used to build advanced price management systems that unlock the potential of dynamic and personalized pricing. We will present price optimization methods for a number of use cases including introductory pricing, promotion calendars, replenishable and seasonal products, targeted offers, and flash sales. We will also review case studies that demonstrate how these methods were applied in practice and how algorithmic price management components were fitted into pricing strategies.Compiler Design- Machine Independent Optimizations
Compiler Design- Machine Independent OptimizationsJyothishmathi Institute of Technology and Science Karimnagar
?
Machine-Independent Optimizations: The Principal Sources of Optimization, Introduction to Data-Flow Analysis, Foundations of Data-Flow Analysis, Constant Propagation, Partial Redundancy Elimination, Loops in Flow Graphs
Relationship between Happiness & LifeQuality .pdf
Relationship between Happiness & LifeQuality .pdfwrachelsong
?
There a lot of studies showing the correlation between GDP by country and average life satisfcation. Usually, most countries with higher GDP tend to have higher average life satisfaction scores. Inspired by this findings, I began to wonder.. 'What other aspects of life significantly contribute to happiness?' Specifically, we wanted to explore which quality of life indicators have a significant relationship with the happiness scores of different regions.
Research Question : Which quality of life indicators have a significant relationship with the happiness score among different regions?
To address this question, we decided to investigate various factors that might influence happiness, including economic stability, health, social support, and more.More Related Content
Similar to Introduction to optimization for deep learning (20)
An overview of gradient descent optimization algorithms
An overview of gradient descent optimization algorithms Hakky St
?
This document provides an overview of various gradient descent optimization algorithms that are commonly used for training deep learning models. It begins with an introduction to gradient descent and its variants, including batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent. It then discusses challenges with these algorithms, such as choosing the learning rate. The document proceeds to explain popular optimization algorithms used to address these challenges, including momentum, Nesterov accelerated gradient, Adagrad, Adadelta, RMSprop, and Adam. It provides visualizations and intuitive explanations of how these algorithms work. Finally, it discusses strategies for parallelizing and optimizing SGD and concludes with a comparison of optimization algorithms.Methods of Optimization in Machine Learning
Methods of Optimization in Machine LearningKnoldus Inc.
?
In this session we will discuss about various methods to optimise a machine learning model and, how we can adjust the hyper-parameters to minimise the cost function.Bagging.pptx
Bagging.pptxComsatsSahiwal1
?
The document discusses bagging, an ensemble machine learning method. Bagging (bootstrap aggregating) uses multiple models fitted on random subsets of a dataset to improve stability and accuracy compared to a single model. It works by training base models in parallel on random samples with replacement of the original dataset and aggregating their predictions. Key benefits are reduced variance, easier implementation through libraries like scikit-learn, and improved performance over single models. However, bagging results in less interpretable models compared to a single model.Gradient Descent DS Rohit Sharma fench knjs.pptx
Gradient Descent DS Rohit Sharma fench knjs.pptxbusinessmarketing100
?
this is the ppt of rohit sharma of gradient descent for present in theathre.Tuning for Systematic Trading: Talk 2: Deep Learning
Tuning for Systematic Trading: Talk 2: Deep LearningSigOpt
?
This talk explains how to train deep learning and other expensive models with parallelism and multitask optimization to reduce wall clock time. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.Getting Started with Server-Side Testing
Getting Started with Server-Side TestingOptimizely
?
This document discusses server-side testing and provides guidance on getting started with it. It covers:
1) The benefits of server-side testing like improved user experience and ability to test backend changes.
2) Best practices like defining success metrics, laying foundations through separation of concerns and middleware, and targeting experiments through attributes.
3) Examples of server-side experiments like algorithms, onboarding flows, and trial durations.[Webinar] Getting started with server-side testing - presented by WiderFunnel...
[Webinar] Getting started with server-side testing - presented by WiderFunnel...Chris Goward
?
One of the most difficult aspects of deep experimentation ― which requires a full stack solution and server-side testing ― is laying a solid foundation for success. In this webinar, you'll learn best practices for going beyond client-side testing, and implementing a full stack experimentation strategy to drive results on the entire customer journey.Understanding parametersniffing sqlsat
Understanding parametersniffing sqlsatSanil Mhatre
?
Parameter Sniffing is usually thought of as the bad guy, in association with a performance problem in your database. Contrary to the popular belief, Parameter Sniffing is usually the good guy, continuously working under the hood to help your database applications run faster. However, it can sometimes go wrong, causing severe performance degradation of your queries.
In this session we will discuss the workings of Parameter Sniffing and demonstrate how it helps improve the performance of your database applications. We will also explore how Parameter Sniffing can go wrong and its impact. Several ways to fix bad Parameter Sniffing will be demonstrated to help make an appropriate choice for your scenario.IM426 3A G5.ppt
IM426 3A G5.pptMohamedSalem979344
?
This document describes how to use the Analytic Hierarchy Process (AHP) to make a multi-criteria decision about purchasing an inventory management system. It involves defining the goal, criteria, and alternatives in a hierarchy. Pairwise comparisons are made between criteria and alternatives to assign weights. The weighted scores are calculated and the alternative with the highest score is selected. In this example, the goal is to purchase a system, the criteria are cost, functionality, supplier reputation, and user services, and the alternatives are Systems A, B, and C. System A is determined to have the highest total weighted score, making it the best choice.MACHINE LEARNING YEAR DL SECOND PART.pptx
MACHINE LEARNING YEAR DL SECOND PART.pptxNAGARAJANS68
?
The document discusses various concepts related to machine learning models including prediction errors, overfitting, underfitting, bias, variance, hyperparameter tuning, and regularization techniques. It provides explanations of key terms and challenges in machine learning like the curse of dimensionality. Cross-validation methods like k-fold are presented as ways to evaluate model performance on unseen data. Optimization algorithms such as gradient descent and stochastic gradient descent are covered. Regularization techniques like Lasso, Ridge, and Elastic Net are introduced.Using Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning ModelsSigOpt
?
1. Tuning machine learning models is challenging due to the large number of non-intuitive hyperparameters.
2. Traditional tuning methods like grid search are computationally expensive and can find local optima rather than global optima.
3. Bayesian optimization uses Gaussian processes to build statistical models from prior evaluations to determine the most promising hyperparameters to test next, requiring far fewer evaluations than traditional methods to find better performing models.Using Bayesian Optimization to Tune Machine Learning Models
Using Bayesian Optimization to Tune Machine Learning ModelsScott Clark
?
1) Bayesian optimization can be used to efficiently tune the hyperparameters of machine learning models, requiring far fewer evaluations than standard random search or grid search methods to find good hyperparameters.
2) It builds a statistical model called a Gaussian process to model the objective function based on previous evaluations, and uses this to select the most promising hyperparameters to evaluate next in order to optimize an objective metric like accuracy.
3) SigOpt is a service that uses Bayesian optimization to tune machine learning models, outperforming expert humans on tasks like classifying images from CIFAR10 and reducing error rates more than standard methods.Meetup_Consumer_Credit_Default_Vers_2_All
Meetup_Consumer_Credit_Default_Vers_2_AllBernard Ong
?
The document discusses a team's approach to a Kaggle challenge to predict consumer credit default. It outlines their goals, dataset details, modeling strategy using an agile process, and key results. Their parallel modeling approach included feature analysis, single/ensemble models, stacking, voting classifiers, and Bayesian optimization. Their top-scoring model achieved an AUC of 0.88912, placing first on the private Kaggle leaderboard. Lessons included the importance of cross-validation, hyperparameter tuning, and following an agile process.Algorithmic pricing: Forecasting and Pricing
Algorithmic pricing: Forecasting and PricingTofigh Naghibi
?
Algorithmic pricing: Forecasting and Pricing
How to use differential programming together with large scale models to find optimal prices in e-commerce?Tuning 2.0: Advanced Optimization Techniques Webinar
Tuning 2.0: Advanced Optimization Techniques WebinarSigOpt
?
This webinar, hosted by SigOpt co-founder and CEO Scott Clark, explains how advanced features can help you achieve your modeling goals. These features include metric definition and multimetric optimization, conditional parameters, and multitask optimization for long training cycles.Using SigOpt to Tune Deep Learning Models with Nervana Cloud
Using SigOpt to Tune Deep Learning Models with Nervana CloudSigOpt
?
This document discusses using SigOpt to tune deep learning models. It notes that tuning deep learning systems is non-intuitive and expert-intensive using traditional random search or grid search methods. SigOpt provides a more efficient approach using Bayesian optimization to suggest optimal hyperparameters after each trial, reducing wasted expert time and computation. The document provides examples applying SigOpt to tune convolutional neural networks on CIFAR10, demonstrating a 1.6% reduction in error rate over expert tuning with no wasted trials.InstructGPT: Follow instructions with human feedback
InstructGPT: Follow instructions with human feedbackYan Xu
?
Introducing InstructGPT paper on follow instructions with human feedbackReinforcement Learning 8: Planning and Learning with Tabular Methods
Reinforcement Learning 8: Planning and Learning with Tabular MethodsSeung Jae Lee
?
A summary of Chapter 8: Planning and Learning with Tabular Methods of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
Check my website for more slides of books and papers!
https://www.endtoend.aiBuilding an algorithmic price management system using ML
Building an algorithmic price management system using MLGrid Dynamics
?
Leading retailers and marketplaces like Amazon and Groupon implemented very sophisticated, and highly automated price management solutions over the recent years. These solutions are able to dynamically change prices every several minutes, intelligently personalize discounts, and respond to competitor moves in order to optimize profits and inventory. It creates pressure on other retailers and manufacturers, and challenges traditional price management techniques, making it increasingly more complex to stay competitive and profitable.
In this talk, we will discuss how predictive modeling and reinforcement learning can be used to build advanced price management systems that unlock the potential of dynamic and personalized pricing. We will present price optimization methods for a number of use cases including introductory pricing, promotion calendars, replenishable and seasonal products, targeted offers, and flash sales. We will also review case studies that demonstrate how these methods were applied in practice and how algorithmic price management components were fitted into pricing strategies.Compiler Design- Machine Independent Optimizations
Compiler Design- Machine Independent OptimizationsJyothishmathi Institute of Technology and Science Karimnagar
?
Machine-Independent Optimizations: The Principal Sources of Optimization, Introduction to Data-Flow Analysis, Foundations of Data-Flow Analysis, Constant Propagation, Partial Redundancy Elimination, Loops in Flow GraphsCompiler Design- Machine Independent Optimizations
Compiler Design- Machine Independent OptimizationsJyothishmathi Institute of Technology and Science Karimnagar
?
Recently uploaded (20)
Relationship between Happiness & LifeQuality .pdf
Relationship between Happiness & LifeQuality .pdfwrachelsong
?
There a lot of studies showing the correlation between GDP by country and average life satisfcation. Usually, most countries with higher GDP tend to have higher average life satisfaction scores. Inspired by this findings, I began to wonder.. 'What other aspects of life significantly contribute to happiness?' Specifically, we wanted to explore which quality of life indicators have a significant relationship with the happiness scores of different regions.
Research Question : Which quality of life indicators have a significant relationship with the happiness score among different regions?
To address this question, we decided to investigate various factors that might influence happiness, including economic stability, health, social support, and more.RAGing Against the Literature: LLM-Powered Dataset Mention Extraction-present...
RAGing Against the Literature: LLM-Powered Dataset Mention Extraction-present...suchanadatta3
?
Dataset Mention Extraction (DME) is a critical task in the field of scientific information extraction, aiming to identify references
to datasets within research papers. In this paper, we explore two advanced methods for DME from research papers, utilizing the
capabilities of Large Language Models (LLMs). The first method
employs a language model with a prompt-based framework to ex-
tract dataset names from text chunks, utilizing patterns of dataset mentions as guidance. The second method integrates the Retrieval-Augmented Generation (RAG) framework, which enhances dataset extraction through a combination of keyword-based filtering, semantic retrieval, and iterative refinement. MusicAggregators and comparissons distributions.pdf
MusicAggregators and comparissons distributions.pdfirramos8843
?
Main Reason: Its flat annual fee and unlimited uploads make it cost-effective for independent artists with multiple releases. Hot Lead Prediction Analytics Use Case - Smarten
Hot Lead Prediction Analytics Use Case - SmartenSmarten Augmented Analytics
?
To conserve resources and optimize investment, a business must determine which potential opportunities are most likely to result in conversions and evolve into successful deals and determine which opportunities are at risk. This Hot Lead predictive analytics use case describes the value of predictive analytics to prioritize high-value leads and capitalize on an opportunity to convert a lead into a relationship by identifying key patterns that contribute to successful deal closures. Use these tools to identify the leads that are most likely to result in conversion and provide the most benefit to the enterprise. This technique can be used in many industries, including Financial Services, B2C and B2B. For more info https://www.smarten.com/augmented-analytics-learn-explore/use-cases.html
the data analytics process:Real life applications
the data analytics process:Real life applicationsjhanvisaxena30
?
the data analytics process: Real life applications
加拿大成绩单购买原版(顿补濒毕业证书)戴尔豪斯大学毕业证文凭
加拿大成绩单购买原版(顿补濒毕业证书)戴尔豪斯大学毕业证文凭taqyed
?
专业定制国外文凭学历证书加拿大文凭戴尔豪斯大学成绩单？【q薇1954292140】复刻成绩单加拿大戴尔豪斯大学毕业证(Dal毕业证书)《国外学历学位认证书》 戴尔豪斯大学毕业证办理，办学历认证加拿大戴尔豪斯大学文凭办理，加拿大戴尔豪斯大学成绩单办理和真实留信认证、留服认证、戴尔豪斯大学学历认证。学院文凭定制，戴尔豪斯大学原版文凭补办，扫描件文凭定做，100%文凭复刻。【q薇1954292140】Buy Dalhousie University Diploma购买美国毕业证，购买英国毕业证，购买澳洲毕业证，购买加拿大毕业证，以及德国毕业证，购买法国毕业证（q薇1954292140）购买荷兰毕业证、购买瑞士毕业证、购买日本毕业证、购买韩国毕业证、购买新西兰毕业证、购买新加坡毕业证、购买西班牙毕业证、购买马来西亚毕业证等。包括了本科毕业证，硕士毕业证。
如果您在英、加、美、澳、欧洲等留学过程中或回国后：
1、在校期间因各种原因未能顺利毕业《Dal成绩单工艺详解》【Q/WeChat：1954292140】《Buy Dalhousie University Transcript快速办理戴尔豪斯大学教育部学历认证书毕业文凭证书》，拿不到官方毕业证；
2、面对父母的压力，希望尽快拿到；
3、不清楚认证流程以及材料该如何准备；
4、回国时间很长，忘记办理；
5、回国马上就要找工作《正式成绩单戴尔豪斯大学在线制作学位证书》【q薇1954292140】《办留学学历认证Dal专业定制国外成绩单修改GPA》办给用人单位看；
6、企事业单位必须要求办理的；
7、需要报考公务员、购买免税车、落转户口、申请留学生创业基金。
加拿大文凭戴尔豪斯大学成绩单，Dal毕业证【q薇1954292140】办理加拿大戴尔豪斯大学毕业证(Dal毕业证书)【q薇1954292140】定制成绩单GPA戴尔豪斯大学offer/学位证毕业证详解细节、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作。帮你解决戴尔豪斯大学学历学位认证难题。
加拿大文凭购买，加拿大文凭定制，加拿大文凭补办。专业在线定制加拿大大学文凭，定做加拿大本科文凭，【q薇1954292140】复制加拿大Dalhousie University completion letter。在线快速补办加拿大本科毕业证、硕士文凭证书，购买加拿大学位证、戴尔豪斯大学Offer，加拿大大学文凭在线购买。高仿真还原加拿大文凭证书和外壳，定制加拿大戴尔豪斯大学成绩单和信封。购买学位证Dal毕业证【q薇1954292140】办理加拿大戴尔豪斯大学毕业证(Dal毕业证书)【q薇1954292140】文凭购买戴尔豪斯大学offer/学位证研究生文凭、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作。帮你解决戴尔豪斯大学学历学位认证难题。
特殊原因导致无法毕业，也可以联系我们帮您办理相关材料：
１：在戴尔豪斯大学挂科了，不想读了，成绩不理想怎么办？？？
2：打算回国了，找工作的时候，需要提供认证《Dal成绩单购买办理戴尔豪斯大学毕业证书范本》【Q/WeChat：1954292140】Buy Dalhousie University Diploma《正式成绩单论文没过》有文凭却得不到认证。又该怎么办？？？加拿大毕业证购买，加拿大文凭购买，
3：回国了找工作没有戴尔豪斯大学文凭怎么办？有本科却要求硕士又怎么办？
帮您解决在加拿大戴尔豪斯大学未毕业难题（Dalhousie University）文凭购买、毕业证购买、大学文凭购买、大学毕业证购买、买文凭、日韩文凭、英国大学文凭、美国大学文凭、澳洲大学文凭、加拿大大学文凭（q薇1954292140）新加坡大学文凭、新西兰大学文凭、爱尔兰文凭、西班牙文凭、德国文凭、教育部认证，买毕业证，毕业证购买，买大学文凭，购买日韩毕业证、英国大学毕业证、美国大学毕业证、澳洲大学毕业证、加拿大大学毕业证（q薇1954292140）新加坡大学毕业证、新西兰大学毕业证、爱尔兰毕业证、西班牙毕业证、德国毕业证，回国证明，留信网认证，留信认证办理，学历认证。从而完成就业。
主营项目：
1、真实教育部国外学历学位认证《加拿大毕业文凭证书快速办理戴尔豪斯大学成绩单原版仿制》【q薇1954292140】《论文没过戴尔豪斯大学正式成绩单》，教育部存档，教育部留服网站100%可查.
2、办理Dal毕业证，改成绩单《Dal毕业证明办理戴尔豪斯大学文凭皮套购买》【Q/WeChat：1954292140】Buy Dalhousie University Certificates《正式成绩单论文没过》，戴尔豪斯大学Offer、在读证明、学生卡、信封、证明信等全套材料，从防伪到印刷，从水印到钢印烫金，高精仿度跟学校原版100%相同.
3、真实使馆认证（即留学人员回国证明），使馆存档可通过大使馆查询确认.
4、留信网认证，国家专业人才认证中心颁发入库证书，留信网存档可查.
《戴尔豪斯大学学历认证报告怎么弄加拿大毕业证书办理Dal成绩单样本》【q薇1954292140】学位证1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。
【q薇1954292140】办理戴尔豪斯大学毕业证(Dal毕业证书)成绩单影本【q薇1954292140】戴尔豪斯大学offer/学位证、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作加拿大戴尔豪斯大学毕业证(Dal毕业证书)学校原版文凭补办
留信认证的作用：
1. 身份认证：留信认证可以证明你的留学经历是真实的，且你获得的学历或学位是正规且经过认证的。这对于一些用人单位来说，尤其是对留学经历有高度要求的公司（如跨国公司或国内高端公司），这是非常重要的一个凭证。
专业评定：留信认证不仅认证你的学位证书，还会对你的所学专业进行评定。这有助于展示你的学术背景，特别是对于国内公司而言，能够清楚了解你所学专业的水平和价值。
国家人才库入库：认证后，你的信息将被纳入国家人才库，并且可以在国家人才网等平台上展示，供包括500强公司等大型公司挑选和聘用人才。这对于回国找工作特别是进入大公司，具有非常积极的作用。Valkey 101 - SCaLE 22x March 2025 Stokes.pdf
Valkey 101 - SCaLE 22x March 2025 Stokes.pdfDave Stokes
?
An Introduction to Valkey, Presented March 2025 at the Southern California Linux Expo, Pasadena CA. Valkey is a replacement for Redis and is a very fast in memory database, used to caches and other low latency applications. Valkey is open-source software and very fast.
2025-03-03-Philly-AAAI-GoodData-Build Secure RAG Apps With Open LLM
2025-03-03-Philly-AAAI-GoodData-Build Secure RAG Apps With Open LLMTimothy Spann
?
2025-03-03-Philly-AAAI-GoodData-Build Secure RAG Apps With Open LLM
https://aaai.org/conference/aaai/aaai-25/workshop-list/#ws14办理魁北克大学成绩单触购买加拿大鲍蚕础惭成绩单文凭定制
办理魁北克大学成绩单触购买加拿大鲍蚕础惭成绩单文凭定制taqyed
?
真实可查毕业证加拿大文凭购买，加拿大文凭定制，加拿大魁北克大学文凭补办【q薇1954292140】专业在线定制加拿大大学文凭成绩单用纸，定做加拿大本科文凭，【q薇1954292140】复制加拿大Université du Québec à Montréal completion letter。在线快速补办加拿大本科毕业证、硕士文凭证书，购买加拿大学位证、魁北克大学Offer，加拿大大学文凭在线购买。高仿真还原加拿大文凭证书和外壳，定制加拿大魁北克大学成绩单和信封。毕业证网上可查学历信息UQAM毕业证【q薇1954292140】办理加拿大魁北克大学毕业证(UQAM毕业证书)【q薇1954292140】毕业证外壳魁北克大学offer/学位证办学位证、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作。帮你解决魁北克大学学历学位认证难题。
特殊原因导致无法毕业，也可以联系我们帮您办理相关材料：
１：在魁北克大学挂科了，不想读了，成绩不理想怎么办？？？
2：打算回国了，找工作的时候，需要提供认证《UQAM成绩单购买办理魁北克大学毕业证书范本》【Q/WeChat：1954292140】Buy Université du Québec à Montréal Diploma《正式成绩单论文没过》有文凭却得不到认证。又该怎么办？？？加拿大毕业证购买，加拿大文凭购买，
3：回国了找工作没有魁北克大学文凭怎么办？有本科却要求硕士又怎么办？
帮您解决在加拿大魁北克大学未毕业难题（Université du Québec à Montréal）文凭购买、毕业证购买、大学文凭购买、大学毕业证购买、买文凭、日韩文凭、英国大学文凭、美国大学文凭、澳洲大学文凭、加拿大大学文凭（q薇1954292140）新加坡大学文凭、新西兰大学文凭、爱尔兰文凭、西班牙文凭、德国文凭、教育部认证，买毕业证，毕业证购买，买大学文凭，购买日韩毕业证、英国大学毕业证、美国大学毕业证、澳洲大学毕业证、加拿大大学毕业证（q薇1954292140）新加坡大学毕业证、新西兰大学毕业证、爱尔兰毕业证、西班牙毕业证、德国毕业证，回国证明，留信网认证，留信认证办理，学历认证。从而完成就业。
如果您在英、加、美、澳、欧洲等留学过程中或回国后：
1、在校期间因各种原因未能顺利毕业《UQAM成绩单工艺详解》【Q/WeChat：1954292140】《Buy Université du Québec à Montréal Transcript快速办理魁北克大学教育部学历认证书毕业文凭证书》，拿不到官方毕业证；
2、面对父母的压力，希望尽快拿到；
3、不清楚认证流程以及材料该如何准备；
4、回国时间很长，忘记办理；
5、回国马上就要找工作《正式成绩单魁北克大学学历认证失败怎么办》【q薇1954292140】《成绩单水印UQAM成绩单如何办理》办给用人单位看；
6、企事业单位必须要求办理的；
7、需要报考公务员、购买免税车、落转户口、申请留学生创业基金。
加拿大文凭魁北克大学成绩单，UQAM毕业证【q薇1954292140】办理加拿大魁北克大学毕业证(UQAM毕业证书)【q薇1954292140】学历认证失败怎么办魁北克大学offer/学位证成绩单制作案例、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作。帮你解决魁北克大学学历学位认证难题。
【q薇1954292140】办理魁北克大学毕业证(UQAM毕业证书)毕业证范本【q薇1954292140】魁北克大学offer/学位证、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作加拿大魁北克大学毕业证(UQAM毕业证书)学历学位证制作
主营项目：
1、真实教育部国外学历学位认证《加拿大毕业文凭证书快速办理魁北克大学在线制作假学位证》【q薇1954292140】《论文没过魁北克大学正式成绩单》，教育部存档，教育部留服网站100%可查.
2、办理UQAM毕业证，改成绩单《UQAM毕业证明办理魁北克大学毕业证书购买》【Q/WeChat：1954292140】Buy Université du Québec à Montréal Certificates《正式成绩单论文没过》，魁北克大学Offer、在读证明、学生卡、信封、证明信等全套材料，从防伪到印刷，从水印到钢印烫金，高精仿度跟学校原版100%相同.
3、真实使馆认证（即留学人员回国证明），使馆存档可通过大使馆查询确认.
4、留信网认证，国家专业人才认证中心颁发入库证书，留信网存档可查.
《魁北克大学学位证书制作代办流程加拿大毕业证书办理UQAM学位证学生卡录取书》【q薇1954292140】学位证1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。
成绩单购买最佳渠道加拿大文凭魁北克大学成绩单【q薇1954292140】复刻成绩单加拿大魁北克大学毕业证(UQAM毕业证书)研究生学历文凭 魁北克大学毕业证办理，修改成绩单GPA加拿大魁北克大学文凭办理，加拿大魁北克大学成绩单办理和真实留信认证、留服认证、魁北克大学学历认证。学院文凭定制，魁北克大学原版文凭补办，扫描件文凭定做，100%文凭复刻。【q薇1954292140】Buy Université du Québec à Montréal Diploma购买美国毕业证，购买英国毕业证，购买澳洲毕业证，购买加拿大毕业证，以及德国毕业证，购买法国毕业证（q薇1954292140）购买荷兰毕业证、购买瑞士毕业证、购买日本毕业证、购买韩国毕业证、购买新西兰毕业证、购买新加坡毕业证、购买西班牙毕业证、购买马来西亚毕业证等。包括了本科毕业证，硕士毕业证。
留信认证的作用：
1. 身份认证：留信认证可以证明你的留学经历是真实的，且你获得的学历或学位是正规且经过认证的。这对于一些用人单位来说，尤其是对留学经历有高度要求的公司（如跨国公司或国内高端公司），这是非常重要的一个凭证。
专业评定：留信认证不仅认证你的学位证书，还会对你的所学专业进行评定。这有助于展示你的学术背景，特别是对于国内公司而言，能够清楚了解你所学专业的水平和价值。
国家人才库入库：认证后，你的信息将被纳入国家人才库，并且可以在国家人才网等平台上展示，供包括500强公司等大型公司挑选和聘用人才。这对于回国找工作特别是进入大公司，具有非常积极的作用。
2. 留信认证对就业的好处
提高竞争力：通过留信认证，尤其是对你所学专业的认证，可以大大提高你在国内求职时的竞争力。许多公司对留学生背景和所学专业有很高的要求，认证后的信息能够帮助公司快速识别符合条件的候选人。
增强信任度：公司往往会对没有认证的学历背景产生疑虑，而留信认证为你的学历背景提供了第三方权威机构的背书，增强了雇主的信任。Introduction to database and analysis software’s suitable for.pptx
Introduction to database and analysis software’s suitable for.pptxnabinparajuli9
?
This ppt provides brief introduction and major strengths and limitations of database and analysis software’s suitable for health research.
![[aon_presentation EN] global_job_leveling .pdf](https://cdn.slidesharecdn.com/ss_thumbnails/englobaljobleveling-250309105035-96521a43-thumbnail.jpg?width=560&fit=bounds)
The Role of Christopher Campos Orlando in Sustainability Analytics
The Role of Christopher Campos Orlando in Sustainability Analyticschristophercamposus1
?
Christopher Campos Orlando specializes in leveraging data to promote sustainability and environmental responsibility. With expertise in carbon footprint analysis, regulatory compliance, and green business strategies, he helps organizations integrate sustainability into their operations. His data-driven approach ensures companies meet ESG standards while achieving long-term sustainability goals.
原版复刻加拿大多伦多大学成绩单(UTSG毕业证书) 文凭
原版复刻加拿大多伦多大学成绩单(UTSG毕业证书) 文凭taqyed
?
毕业证书加拿大文凭多伦多大学成绩单？【q薇1954292140】复刻成绩单加拿大多伦多大学毕业证(UTSG毕业证书)成绩单定购 多伦多大学毕业证办理，毕业证文凭样本加拿大多伦多大学文凭办理，加拿大多伦多大学成绩单办理和真实留信认证、留服认证、多伦多大学学历认证。学院文凭定制，多伦多大学原版文凭补办，扫描件文凭定做，100%文凭复刻。【q薇1954292140】Buy St. George campus Diploma购买美国毕业证，购买英国毕业证，购买澳洲毕业证，购买加拿大毕业证，以及德国毕业证，购买法国毕业证（q薇1954292140）购买荷兰毕业证、购买瑞士毕业证、购买日本毕业证、购买韩国毕业证、购买新西兰毕业证、购买新加坡毕业证、购买西班牙毕业证、购买马来西亚毕业证等。包括了本科毕业证，硕士毕业证。
如果您在英、加、美、澳、欧洲等留学过程中或回国后：
1、在校期间因各种原因未能顺利毕业《UTSG成绩单工艺详解》【Q/WeChat：1954292140】《Buy St. George campus Transcript快速办理多伦多大学教育部学历认证书毕业文凭证书》，拿不到官方毕业证；
2、面对父母的压力，希望尽快拿到；
3、不清楚认证流程以及材料该如何准备；
4、回国时间很长，忘记办理；
5、回国马上就要找工作《正式成绩单多伦多大学毕业证书》【q薇1954292140】《购买毕业证UTSG成绩单定购》办给用人单位看；
6、企事业单位必须要求办理的；
7、需要报考公务员、购买免税车、落转户口、申请留学生创业基金。
加拿大文凭多伦多大学成绩单，UTSG毕业证【q薇1954292140】办理加拿大多伦多大学毕业证(UTSG毕业证书)【q薇1954292140】成绩单影本多伦多大学offer/学位证研究生学历信息、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作。帮你解决多伦多大学学历学位认证难题。
加拿大文凭购买，加拿大文凭定制，加拿大文凭补办。专业在线定制加拿大大学文凭，定做加拿大本科文凭，【q薇1954292140】复制加拿大St. George campus completion letter。在线快速补办加拿大本科毕业证、硕士文凭证书，购买加拿大学位证、多伦多大学Offer，加拿大大学文凭在线购买。高仿真还原加拿大文凭证书和外壳，定制加拿大多伦多大学成绩单和信封。办硕士学位证UTSG毕业证【q薇1954292140】办理加拿大多伦多大学毕业证(UTSG毕业证书)【q薇1954292140】成绩单定制多伦多大学offer/学位证学历认证报告怎么弄、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作。帮你解决多伦多大学学历学位认证难题。
特殊原因导致无法毕业，也可以联系我们帮您办理相关材料：
１：在多伦多大学挂科了，不想读了，成绩不理想怎么办？？？
2：打算回国了，找工作的时候，需要提供认证《UTSG成绩单购买办理多伦多大学毕业证书范本》【Q/WeChat：1954292140】Buy St. George campus Diploma《正式成绩单论文没过》有文凭却得不到认证。又该怎么办？？？加拿大毕业证购买，加拿大文凭购买，
3：回国了找工作没有多伦多大学文凭怎么办？有本科却要求硕士又怎么办？
帮您解决在加拿大多伦多大学未毕业难题（St. George campus）文凭购买、毕业证购买、大学文凭购买、大学毕业证购买、买文凭、日韩文凭、英国大学文凭、美国大学文凭、澳洲大学文凭、加拿大大学文凭（q薇1954292140）新加坡大学文凭、新西兰大学文凭、爱尔兰文凭、西班牙文凭、德国文凭、教育部认证，买毕业证，毕业证购买，买大学文凭，购买日韩毕业证、英国大学毕业证、美国大学毕业证、澳洲大学毕业证、加拿大大学毕业证（q薇1954292140）新加坡大学毕业证、新西兰大学毕业证、爱尔兰毕业证、西班牙毕业证、德国毕业证，回国证明，留信网认证，留信认证办理，学历认证。从而完成就业。
主营项目：
1、真实教育部国外学历学位认证《加拿大毕业文凭证书快速办理多伦多大学成绩单GPA修改》【q薇1954292140】《论文没过多伦多大学正式成绩单》，教育部存档，教育部留服网站100%可查.
2、办理UTSG毕业证，改成绩单《UTSG毕业证明办理多伦多大学学校原版文凭补办》【Q/WeChat：1954292140】Buy St. George campus Certificates《正式成绩单论文没过》，多伦多大学Offer、在读证明、学生卡、信封、证明信等全套材料，从防伪到印刷，从水印到钢印烫金，高精仿度跟学校原版100%相同.
3、真实使馆认证（即留学人员回国证明），使馆存档可通过大使馆查询确认.
4、留信网认证，国家专业人才认证中心颁发入库证书，留信网存档可查.
《多伦多大学扫描件文凭定做加拿大毕业证书办理UTSG文凭定制您的学术成就》【q薇1954292140】学位证1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。
【q薇1954292140】办理多伦多大学毕业证(UTSG毕业证书)仿造在线制作本科文凭【q薇1954292140】多伦多大学offer/学位证、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作加拿大多伦多大学毕业证(UTSG毕业证书)学位证书样本
留信认证的作用：
1. 身份认证：留信认证可以证明你的留学经历是真实的，且你获得的学历或学位是正规且经过认证的。这对于一些用人单位来说，尤其是对留学经历有高度要求的公司（如跨国公司或国内高端公司），这是非常重要的一个凭证。
专业评定：留信认证不仅认证你的学位证书，还会对你的所学专业进行评定。这有助于展示你的学术背景，特别是对于国内公司而言，能够清楚了解你所学专业的水平和价值。
国家人才库入库：认证后，你的信息将被纳入国家人才库，并且可以在国家人才网等平台上展示，供包括500强公司等大型公司挑选和聘用人才。这对于回国找工作特别是进入大公司，具有非常积极的作用。Hire Android App Developers in India with Cerebraix
Hire Android App Developers in India with Cerebraixcerebraixs
?
Android app developers are crucial for creating
high-quality, user-friendly, and innovative mobile
applications. Their expertise in mobile development,
UI/UX design, and seamless integration ensures robust
and scalable apps that drive user engagement and
business success in the competitive mobile market.Introduction to optimization for deep learning
- 1. Ali Madani https://www.linkedin.com/in/amlearning/ Introduction to optimization for deep learning
- 2. gradient descent 2 Obtaining the parameters in the direction of maximum variation: Cost function Learning rate
- 3. gradient descent 3 Obtaining the parameters in the direction of maximum variation: Cost function Learning rate Changing the parameters to minimize the cost
- 4. gradient descent 4 Obtaining the parameters in the direction of maximum variation: Cost function Learning rate Summation over all data points ● slow ● intractable
- 5. Stochastic gradient descent 5 Obtaining the parameters in the direction of maximum variation: Cost function Learning rate
- 6. Stochastic gradient descent 6 Obtaining the parameters in the direction of maximum variation: Issue solved: parameter update for each training example ● Objective function fluctuates ○ Maybe get to a better local minima faster (by jumps) Cost function Learning rate
- 7. Comparison of gradient descent and stochastic gradient descent 7
- 8. Mini-batch gradient descent 8 Let’s get the middle Cost function Learning rate
- 9. Mini-batch gradient descent 9 Let’s get the middle Cost function Learning rate Update for mini-batches of n training examples
- 10. Please share what you learned with others