A collection of machine learning algorithms for online linear classification written in Common Lisp.
cl-online-learning is available from Quicklisp.
When install from github repository,
cd ~/quicklisp/local-projects/ git clone https://github.com/masatoi/cl-online-learning.git
When using Roswell,
ros install masatoi/cl-online-learning
A data point is a pair of a class label (+1 or -1) and a input vector. Both of them have to be declared as single-float.
And dataset is represented as a sequence of data points. READ-DATA function is available to make a dataset from a sparse format used in LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/). This function requires the number of features of that dataset.
;; Number of features (defparameter a1a-dim 123) ;; Read dataset from file (defparameter a1a (clol.utils:read-data (merge-pathnames #P"t/dataset/a1a" (asdf:system-source-directory :cl-online-learning)) a1a-dim)) ;; A data point (car a1a) ; (-1.0 ; . #(0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 ; 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ; 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ; 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 ; 1.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ; 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ; 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0))
A learner object is just a struct, therefore their constructor is available to make it.
(defparameter arow-learner (clol:make-arow a1a-dim 10))
To update the model destructively with one data point, use an update function corresponding to the model type.
(clol:arow-update arow-learner (cdar a1a) ; input (caar a1a)) ; label
TRAIN function can be used to learn the dataset collectively.
(clol:train arow-learner a1a)
It may be necessary to call this function several times until learning converges. For now, the convergence test has not been implemented yet.
(clol:arow-predict arow-learner (cdar a1a)) ; => -1.0 (clol:test arow-learner a1a) ; Accuracy: 84.85981%, Correct: 1362, Total: 1605
For multiclass data, the label of the data point is an integer representing the index of the class. READ-DATA function with MULTICLASS-P keyword option is available for make such a dataset.
(defparameter iris-dim 4) ; A dataset in which a same label appears consecutively need to be shuffled (defparameter iris (clol.utils:shuffle-vector (coerce (clol.utils:read-data (merge-pathnames #P"t/dataset/iris.scale" (asdf:system-source-directory :cl-online-learning)) iris-dim :multiclass-p t) 'simple-vector))) (defparameter iris-train (subseq iris 0 100)) (defparameter iris-test (subseq iris 100))
ONE-VS-REST and ONE-VS-ONE are available for multiclass classification by using multiple binary classifiers. In many cases, ONE-VS-ONE is more accurate, but it requires more computational resource as the number of classes increases.
;; Define model (defparameter arow-1vs1 (clol:make-one-vs-one iris-dim ; Input data dimension 3 ; Number of class 'arow 0.1)) ; Binary classifier type and its parameters ;; Train and test model (clol:train arow-1vs1 iris-train) (clol:test arow-1vs1 iris-test) ; Accuracy: 98.0%, Correct: 49, Total: 50
For sparse data (most elements are 0), the data point is a pair of a class label and a instance of SPARSE-VECTOR struct, and a learner with SPARSE- prefix is used. READ-DATA function with SPARSE-P keyword option is available for make such a dataset.
For example, news20.binary data has too high dimensional features to handle with normal learners. However, by using the sparse version, the learner can be trained with practical computational resources.
(defparameter news20.binary-dim 1355191) (defparameter news20.binary (clol.utils:read-data "/path/to/news20.binary" news20.binary-dim :sparse-p t)) (defparameter news20.binary.arow (clol:make-sparse-arow news20.binary-dim 10)) (time (loop repeat 20 do (clol:train news20.binary.arow news20.binary))) ;; Evaluation took: ;; 1.527 seconds of real time ;; 1.526852 seconds of total run time (1.526852 user, 0.000000 system) ;; 100.00% CPU ;; 5,176,917,149 processor cycles ;; 11,436,032 bytes consed (clol:test news20.binary.arow news20.binary) ; Accuracy: 99.74495%, Correct: 19945, Total: 19996
In a similar way, the sparse version learners are also available in multiclass classification.
(defparameter news20-dim 62060) (defparameter news20-train (clol.utils:read-data "/path/to/news20.scale" news20-dim :sparse-p t :multiclass-p t)) (defparameter news20-test (clol.utils:read-data "/path/to/news20.t.scale" news20-dim :sparse-p t :multiclass-p t)) (defparameter news20-arow (clol:make-one-vs-rest news20-dim 20 'sparse-arow 10)) (loop repeat 12 do (clol:train news20-arow news20-train)) (clol:test news20-arow news20-test) ; Accuracy: 86.90208%, Correct: 3470, Total: 3993
For saving a learner model to a file or restoring from the model file, SAVE and RESTORE function are available respectively. For the above multiclass classification example, saving / restoring code would be:
;; Save (clol:save arow-1vs1 #P"/tmp/iris.model") ;; Restore (defparameter restored-learner (clol:restore #P"/tmp/iris.model")) (clol:test restored-learner iris-test) ; Accuracy: 98.0%, Correct: 49, Total: 50
Source Code: https://github.com/masatoi/cl-online-learning
License: MIT License
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)
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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
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? **
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.
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.
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
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.
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 **
Another significant test is the capacity to precisely decipher results produced by the calculations. You should likewise cautiously pick the calculations for your motivation.
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.
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.
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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 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.
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.
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.
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.
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.
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.
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As more data, better algorithms, and higher computing power continue to shape the future of artificial intelligence (AI), reliable machine learning models have become paramount to optimise outcomes. OpenAI’s meta-learning algorithm, Reptile, is one such model designed to perform a wide array of tasks.
For those unaware, meta-learning refers to the idea of ‘learning to learn by solving multiple tasks, like how humans learn. Using meta-learning, you can design models that can learn new skills or adapt to new environments rapidly with a few training examples.
In the recent past, the meta-learning algorithm has had a fair bit of success as it can learn with limited quantities of data. Unlike other learning models like reinforcement learning, which uses reward mechanisms for each action, meta-learning can generalise to different scenarios by separating a specified task into two functions.
The first function often gives a quick response within a specific task, while the second function includes the extraction of information learned from previous tasks. It is similar to how humans behave, where they often gain knowledge from previous unrelated tasks or experiences.
Typically, there are three common approaches to meta-learning.
For instance, the above image depicts the model-agnostic meta-learning algorithm (MAML) developed by researchers at the University of California, Berkeley, in partnership with OpenAI. The MAML optimises for a representation θ that can quickly adapt to new tasks.
On the other hand, Reptile utilises a stochastic gradient descent (SGD) to initialise the model’s parameters instead of performing several computations that are often resource-consuming. In other words, it also reduces the dependency of higher computational hardware requirements, if implemented in a machine learning project.
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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).
General Scheme (Or, 2020)
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  for more information).
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