OLLAM: Online Learning of Linear Adaptable Models

OLLAM: Online Learning of Linear Adaptatable Models

This package is an implementation of:

1. MIRA and PA algorithms (Krammer and Singer 2003)
2. Averaged Perceptron (Collins 2001)

It is also contains a LIBSVM wrapper for linear models (mainly for comparison and initialization).

Prerequistes

• Stage.jl - Needed for logging and memoization (Note: requires manual install)
• LIBSVM.jl - LibSVM binaries and julia wrapper
• MNIST.jl - for testing

Install

This is an experimental package which is not currently registered in the julia central repository. You can install via:

Pkg.clone("https://github.com/saltpork/Stage.jl")
Pkg.clone("https://github.com/mit-nlp/Ollam.jl")

This process should install all dependent packages in addition to Ollam.

Usage

See test/runtests.jl for detailed usage.

Author: mit-nlp 
Source Code: https://github.com/mit-nlp/Ollam.jl 
License: Apache-2.0 license

#julia #models 

What is GEEK

Buddha Community

Workshop Alert! Deep Learning Model Deployment & Management

The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.

Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.

Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.

In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.

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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)

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Inside MoveNet, Google’s Latest Pose Detection Model

Ahead of Google I/O, Google Research launched a new pose detection model in TensorFlow.js called MoveNet. This ultra-fast and accurate model can detect 17 key points in the human body. MoveNet is currently available on TF Hub with two variants — Lightning and Thunder.

While Lightning is intended for latency-critical applications, Thunder is for applications that call for higher accuracy. Both models claim to run faster than real-time (30+ frames per second (FPS)) on most personal computers, laptops and phones.

The model can be launched in the browser using TensorFlow.js architecture with no server calls needed after the initial page load or external packages. The live demo version is available here.

Currently, the MoveNet model works for the individual in the camera field-of-view. But, soon, Google Research looks to extend the MoveNet model to the multi-person domain so that developers can support applications with multiple people.

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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? **

2. 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.

3. **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.

4. **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

5. **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.

2. **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 **

3. **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.

4. 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.

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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.

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