Vaughn  Sauer

Vaughn Sauer

1658296142

An Implementation Of online Mini-batch Learning for Prediction/julia

OnlineLearning 

An implementation of online mini-batch learning for prediction in julia.

Learners

A Learner is fit by repeated calls to update!(l::Learner, x::DSMat{Float64}, y::Vector{Float64}) on mini-batches (x, y) of a dataset. Updating a learner incrementally optimizes some loss function. The loss function depends on the implementation of concrete subtypes of Learner. The actual optimization routine is implemented by an AbstractSGD object.

Values of the outcome are predicted with predict(l::Learner, x::DSMat{Float64}). The predict!(obj::Learner, pr::Vector{Float64}, x::DSMat{Float64}) method calculates predictions in place.

Features (x) can be either a dense or sparse matrix. (DSMat{T} is an alias for DenseMatrix{T} or SparseMatrixCSC{T, Ti <: Integer})

Available learners

  • GLMLearner(m::GLMModel, optimizer::AbstractSGD) - GLMs without regularization.
  • GLMNetLearner(m::GLMModel, optimizer::AbstractSGD, lambda1 = 0.0, lambda2 = 0.0) - GLMs with l_1 and l_2 regularization.
  • SVMLearner - support vector machine, not fully implemented

The type of GLM is specified by GLMModel. Choices are:

  • LinearModel() for least squares
  • LogisticModel() for logistic regression
  • QuantileModel(tau=0.5) for tau-quantile regression.

Optimization

All of the learners require an optimizer of some sort. Currently, stochastic gradient descent type methods are provided by the AbstractSGD type.

An AbstractSGD implements an update!(obj::AbstractSGD{Float64}, weights::Vector{Float64}, gr::Vector{Float64}) method. This takes the current value of the weight(coefficient) vector and gradient and updates the weight vector in place. The AbstractSGD instance stores tuning parameters and step information, and may have additional storage additional storage for if necessary.

Available optimizers:

  • SimpleSGD(alpha1::Float64, alpha2::Float64) - Standard SGD where step size is alpha1/(1.0 + alpha1 * alpha2 * t).
  • AdaDelta(rho::Float64, eps::Float64) - Implementation of Algorithm 1 here.
  • AdaGrad(eta::Float64) Stepsize is for weight j eta /[sqrt(sum of grad_j^2 up to t) + 1.0e-8]. Paper.
  • AveragedSGD(alpha1::Float64, alpha2::Float64, t0::Int) - Described in section 5.3 here with step size alpha1/(1.0 + alpha1 * alpha2 * t)^(3/4)

Notes

This is a work in progress. Most testing has been in simulations and not with real data. GLMLearner and GLMNetLearner with l_2 regularization seem to work pretty well. GLMNetLearner with l_1 regularization has not been thoroughly tested. Statistical performance tends to be pretty senstive to choice of optimizer and tuning parameters.

TODO

  • Everything is implemented in terms of Float64. Should allow for Float32 as well.
  • Finish the SVM implementation, perhaps add Pegasos implementation
  • Automatic transformations of features
  • More useful interfaces/DataFrames interface
  • More checking of data
  • Automatic bounding for predictions
  • Remove GLMLearner in favor of GLMNetLearner
  • Better docs
  • Eliminate extra memory allocation

Author: lendle
Source code: https://github.com/lendle/OnlineLearning.jl
License: View license
 

#julia #machine-learning 

What is GEEK

Buddha Community

An Implementation Of online Mini-batch Learning for Prediction/julia
Houston  Sipes

Houston Sipes

1600430400

10 Free Online Resources To Learn Swift Language

Swift is a fast and efficient general-purpose programming language that provides real-time feedback and can be seamlessly incorporated into existing Objective-C code. This is why developers are able to write safer, more reliable code while saving time. It aims to be the best language that can be used for various purposes ranging from systems programming to mobile as well as desktop apps and scaling up to cloud services.

Below here, we list down the 10 best online resources to learn Swift language.

(The list is in no particular order)

#developers corner #free online resources to learn swift language #learn swift #learn swift free #learn swift online free #resources to learn swift #swift language #swift programming

Vaughn  Sauer

Vaughn Sauer

1658296142

An Implementation Of online Mini-batch Learning for Prediction/julia

OnlineLearning 

An implementation of online mini-batch learning for prediction in julia.

Learners

A Learner is fit by repeated calls to update!(l::Learner, x::DSMat{Float64}, y::Vector{Float64}) on mini-batches (x, y) of a dataset. Updating a learner incrementally optimizes some loss function. The loss function depends on the implementation of concrete subtypes of Learner. The actual optimization routine is implemented by an AbstractSGD object.

Values of the outcome are predicted with predict(l::Learner, x::DSMat{Float64}). The predict!(obj::Learner, pr::Vector{Float64}, x::DSMat{Float64}) method calculates predictions in place.

Features (x) can be either a dense or sparse matrix. (DSMat{T} is an alias for DenseMatrix{T} or SparseMatrixCSC{T, Ti <: Integer})

Available learners

  • GLMLearner(m::GLMModel, optimizer::AbstractSGD) - GLMs without regularization.
  • GLMNetLearner(m::GLMModel, optimizer::AbstractSGD, lambda1 = 0.0, lambda2 = 0.0) - GLMs with l_1 and l_2 regularization.
  • SVMLearner - support vector machine, not fully implemented

The type of GLM is specified by GLMModel. Choices are:

  • LinearModel() for least squares
  • LogisticModel() for logistic regression
  • QuantileModel(tau=0.5) for tau-quantile regression.

Optimization

All of the learners require an optimizer of some sort. Currently, stochastic gradient descent type methods are provided by the AbstractSGD type.

An AbstractSGD implements an update!(obj::AbstractSGD{Float64}, weights::Vector{Float64}, gr::Vector{Float64}) method. This takes the current value of the weight(coefficient) vector and gradient and updates the weight vector in place. The AbstractSGD instance stores tuning parameters and step information, and may have additional storage additional storage for if necessary.

Available optimizers:

  • SimpleSGD(alpha1::Float64, alpha2::Float64) - Standard SGD where step size is alpha1/(1.0 + alpha1 * alpha2 * t).
  • AdaDelta(rho::Float64, eps::Float64) - Implementation of Algorithm 1 here.
  • AdaGrad(eta::Float64) Stepsize is for weight j eta /[sqrt(sum of grad_j^2 up to t) + 1.0e-8]. Paper.
  • AveragedSGD(alpha1::Float64, alpha2::Float64, t0::Int) - Described in section 5.3 here with step size alpha1/(1.0 + alpha1 * alpha2 * t)^(3/4)

Notes

This is a work in progress. Most testing has been in simulations and not with real data. GLMLearner and GLMNetLearner with l_2 regularization seem to work pretty well. GLMNetLearner with l_1 regularization has not been thoroughly tested. Statistical performance tends to be pretty senstive to choice of optimizer and tuning parameters.

TODO

  • Everything is implemented in terms of Float64. Should allow for Float32 as well.
  • Finish the SVM implementation, perhaps add Pegasos implementation
  • Automatic transformations of features
  • More useful interfaces/DataFrames interface
  • More checking of data
  • Automatic bounding for predictions
  • Remove GLMLearner in favor of GLMNetLearner
  • Better docs
  • Eliminate extra memory allocation

Author: lendle
Source code: https://github.com/lendle/OnlineLearning.jl
License: View license
 

#julia #machine-learning 

Ananya Gupta

Ananya Gupta

1595485129

Pros and Cons of Machine Learning Language

Amid all the promotion around Big Data, we continue hearing the expression “AI”. In addition to the fact that it offers a profitable vocation, it vows to tackle issues and advantage organizations by making expectations and helping them settle on better choices. In this blog, we will gain proficiency with the Advantages and Disadvantages of Machine Learning. As we will attempt to comprehend where to utilize it and where not to utilize Machine learning.

In this article, we discuss the Pros and Cons of Machine Learning.
Each coin has two faces, each face has its property and highlights. It’s an ideal opportunity to reveal the essence of ML. An extremely integral asset that holds the possibility to reform how things work.

Pros of Machine learning

  1. **Effectively recognizes patterns and examples **

AI can survey enormous volumes of information and find explicit patterns and examples that would not be evident to people. For example, for an online business site like Amazon, it serves to comprehend the perusing practices and buy chronicles of its clients to help oblige the correct items, arrangements, and updates pertinent to them. It utilizes the outcomes to uncover important promotions to them.

**Do you know the Applications of Machine Learning? **

  1. No human mediation required (mechanization)

With ML, you don’t have to keep an eye on the venture at all times. Since it implies enabling machines to learn, it lets them make forecasts and improve the calculations all alone. A typical case of this is hostile to infection programming projects; they figure out how to channel new dangers as they are perceived. ML is additionally acceptable at perceiving spam.

  1. **Constant Improvement **

As ML calculations gain understanding, they continue improving in precision and productivity. This lets them settle on better choices. Let’s assume you have to make a climate figure model. As the measure of information you have continues developing, your calculations figure out how to make increasingly exact expectations quicker.

  1. **Taking care of multi-dimensional and multi-assortment information **

AI calculations are acceptable at taking care of information that is multi-dimensional and multi-assortment, and they can do this in unique or unsure conditions. Key Difference Between Machine Learning and Artificial Intelligence

  1. **Wide Applications **

You could be an e-posterior or a social insurance supplier and make ML work for you. Where it applies, it holds the ability to help convey a considerably more close to home understanding to clients while additionally focusing on the correct clients.

**Cons of Machine Learning **

With every one of those points of interest to its effectiveness and ubiquity, Machine Learning isn’t great. The accompanying components serve to confine it:

1.** Information Acquisition**

AI requires monstrous informational indexes to prepare on, and these ought to be comprehensive/fair-minded, and of good quality. There can likewise be times where they should trust that new information will be created.

  1. **Time and Resources **

ML needs sufficient opportunity to allow the calculations to learn and grow enough to satisfy their motivation with a lot of precision and pertinence. It additionally needs monstrous assets to work. This can mean extra necessities of PC power for you.
**
Likewise, see the eventual fate of Machine Learning **

  1. **Understanding of Results **

Another significant test is the capacity to precisely decipher results produced by the calculations. You should likewise cautiously pick the calculations for your motivation.

  1. High mistake weakness

AI is self-governing yet exceptionally powerless to mistakes. Assume you train a calculation with informational indexes sufficiently little to not be comprehensive. You end up with one-sided expectations originating from a one-sided preparing set. This prompts unessential promotions being shown to clients. On account of ML, such botches can set off a chain of mistakes that can go undetected for extensive periods. What’s more, when they do get saw, it takes very some effort to perceive the wellspring of the issue, and significantly longer to address it.

**Conclusion: **

Subsequently, we have considered the Pros and Cons of Machine Learning. Likewise, this blog causes a person to comprehend why one needs to pick AI. While Machine Learning can be unimaginably ground-breaking when utilized in the correct manners and in the correct spots (where gigantic preparing informational indexes are accessible), it unquestionably isn’t for everybody. You may likewise prefer to peruse Deep Learning Vs Machine Learning.

#machine learning online training #machine learning online course #machine learning course #machine learning certification course #machine learning training

Ananya Gupta

Ananya Gupta

1601875752

AI(Artificial Intelligence): The Business Benefits of Machine Learning

Artificial intelligence has been around since a minimum of the 1950s, but it’s only within the past few years that it’s become ubiquitous. Companies we interact with every day— Amazon, Facebook, and Google—have fully embraced AI. It powers product recommendations, maps, and social media feeds.

But it’s not only the tech giants that will employ AI in their products. AI solutions are now accessible to several businesses and individuals. And it’s becoming clear that understanding and employing AI is critical for the companies of tomorrow.

What Is AI?
In the last 20 years, there are major changes in technology—notably the arrival of the mobile. But the innovation that’s on par with inventing electricity is AI.

Machine Learning
Machine learning may be a subset of AI and maybe a set of techniques that give computers the power to find out without being explicitly programmed to try to so. One example is classification, like classifying images: during a very simplistic interpretation, for instance, a computer could automatically classify pictures of apples and oranges to travel in several folders. And with more data over time, the machine will become better future scope and career oppertunity for students who want to make career in Machine Learning.

Deep Learning and Neural Networks
Deep learning may be a further subset of machine learning that permits computers to find out more complex patterns and solve more complex problems. one among the clearest applications of deep learning is in tongue processing, which powers chatbots and voice assistants like Siri. It’s the recent advent of deep learning that has particularly been driving the AI boom.

And all of those are supported neural networks, which is that the concept machines could mimic the human brain, with many layers of artificial neurons. Neural networks are powerful once they are multi-layered, with more neurons and interconnectivity. Neural networks are researched for years, but only recently has the research been pushed to the subsequent level and commercialized.

AI Business Benefits
Now that you simply have a conceptual understanding of AI and its subsets, let’s get to the guts of it: what can AI do for you and your business? We’ll explore highlights within five areas: human resources, accounting, legal, marketing and sales, and customer support.

Human Resources
Artificial intelligence poses a big opportunity in process automation. One example would be recruitment and human resources. As an example, tasks like onboarding and administration of advantages are often automated.If you want to learn deep about AI then join Artificial Intellegence class in Noida and get offer to work on live projects.

Accounting
The dutiful accountant, languishing over the bookkeeping—it’s a classic image. But now many of their services might not be needed. Many traditional bookkeeping tasks are already being performed by AI. Areas like accounts payable and receivable are taking advantage of automated data entry and categorization.

Legal
Some of the foremost fascinating advancements in AI are associated with law and legal technology. Specifically, AI can now read “legal and contractual documents to extract provisions using tongue processing.” Blue J Legal’s website touts the platform’s ability to help with employment law. The Foresight technology “analyzes data drawn from common law cases, using deep learning to get hidden patterns in previous rulings.” briefly, cases can now be analyzed much faster, insights are often drawn from across a good array of legal knowledge, and thus business decisions are often more accurate and assured.

Sales and Marketing Analytics
Analytics can now be done much more rapidly with much larger data sets because of AI. This has profound impacts on all kinds of data analysis, including business and financial decisions.

One of the quickly changing areas is marketing and sales applications. AI makes it easier to predict what a customer is probably going to shop for by learning and understanding their purchasing patterns.

Customer Support
You’ve been there. Waiting forever on a customer support line. Perhaps with a cable company or an enormous bank. Luckily, AI is close to making your life easier, if it hasn’t already.

According to the Harvard Business Review, one of the most benefits of AI is that “intelligent agents offer 24/7 customer service addressing a broad and growing array of issues from password requests to technical support questions—all within the customer’s tongue .” For customer support, a mixture of machine and deep learning can allow queries to be analyzed quicker.

Conclusion
With AI becoming ever more pervasive, having a fundamental understanding of it’s a requirement for continued business success. Whatever role you hold in your business, understanding AI may assist you to solve problems in new and innovative ways, saving time and money. Further, it’s going to assist you to build and style the products and services of the longer term.

#machine learning online training #machine learning online course #machine learning course #machine learning training in noida #artificial intelligence training in noida #artificial intelligence online training

Online Deep Learning (ODL) and Hedge Back-propagation

Introduction

As the main concept of deep neural networks is to train through back-propagation in a batch setting, the data is required to be available in an offline setting. As a consequence, the scheme is irrelevant for many practical situations, in which the data arrives in sequence and cannot be stored. For example: stocks, vehicle position, and many more. ODL is very challenging as it cannot use back-propagation. Two years ago, Sahoo et al (2018) addressed the gap between online learning and deep learning, where they claimed that “without the power of depth, it would be difficult to learn complex patterns”. They presented a novel framework for ODL (to be reviewed later).

Image for post

General Scheme (Or, 2020)

Overview of Online Learning (OL)

OL is an ML method in which data is available in sequential order, and we use it in order to predict future data at each time step. Moreover, in OL, we update the predictor in real-time. According to Shai Shalev-Shwartz: “OL is the process of answering a sequence of questions given (maybe partial) knowledge of the correct answers to previous questions and possibly additional available information”. The family of OL includes online convex optimization, (which leads to efficient algorithms), limited feedback model when the system observes the loss value but does not observe the actual real value and more (I recommend reading ref [2] for more information).

#artificial-intelligence #machine-learning #real-time-systems #online-learning #deep-learning #machine learning