- Machine learning is one of the fastest growing areas of computer science, with far-reaching applications.
- The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
- The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
- These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
- Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
Here are three eBooks available for free.
Edited by Abdelhamid Mellouk and Abdennacer Chebira
Machine Learning can be defined in various ways…
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- While artificial intelligence isn’t likely to come for your job anytime soon (no matter how many dystopian movies say otherwise), AI and machine learning are already automating and improving many everyday tasks, like mobile search or the organization of your family photos.
- AI is also helping a new breed of companies disrupt industries from medical research to agriculture.
- Computers can’t yet replace humans, but they can do a great job handling the mundane clutter of our lives.
For accelerating mobile search with artificial intelligence04.
For giving digital services the power of human speech05.
Don’t fear smart computers—these companies are using AI to prevent disease, predict food shortages, and more.
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- Communication is naively defined as content and the mode of transmission — symbols manifested as images, language transmitted through speech and writing, digital files sent through the internet.
- Most machine learning in today’s products is related to understanding — your phone can translate your voice into text and you can search photos for certain objects or people because of machine understanding.
- Generative modeling is a machine learning technique that creates new data that mimics the data that the machine was trained on.
- In an earlier example, image search was used as an example of computers aiding in communication by helping you find an image that approximates what your mind’s eye sees.
- Instead of returning an image that already exists, the generative system creates an entirely new image based on the text.
Communication is an essential pillar of society. Humanity’s progression over the past millennium was largely driven by the development and evolution of commu…
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- It is a type of machine learning, where one guides the system by tagging the output.
- For example, a supervised machine learning system that can learn which emails are ‘spam’ and which are ‘not spam’ will have its input data tagged with this classification to help the machine learning system learn the characteristics or parameters of the ‘spam’ email and distinguish it from those of ‘not spam’ emails.
- Just as the three year old learns the difference between a ‘block’ and a ‘soft toy’, the supervised machine learning system learns which email is ‘spam’ and which is ‘not spam’.
- Now instead of telling the child which toy to put in which box, you reward the child with a ‘big hug’ when it makes the right choice and make a ‘sad face’ when it makes the wrong action (e.g., block in a soft toy box or soft toy in the block box).
- Based on your problem domain and the availability of data do you know which type of machine learning system you want to build?
Where business and experience meet emerging technology.
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- From First Principles With Pure Python and
Use them on Real-World Datasets
You must understand algorithms to get good at machine learning.
- In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work.
- I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones.
- (yes I have written tons of code that runs operationally)
I get a lot of satisfaction helping developers get started and get really good at machine learning.
- I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.
Discover How to Code Machine Algorithms
From First Principles With Pure Python and
Use them on Real-World Datasets
You must understand algorithms t…
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- We think technology can help.Today, Google and Jigsaw are launching Perspective, an early-stage technology that uses machine learning to help identify toxic comments.
- Through an API, publishers — including members of the Digital News Initiative — and platforms can access this technology and use it for their sites.HOW IT WORKSPerspective reviews comments and scores them based on how similar they are to comments people said were “toxic” or likely to make someone leave a conversation.
- Each time Perspective finds new examples of potentially toxic comments, or is provided with corrections from users, it can get better at scoring future comments.Publishers can choose what they want to do with the information they get from Perspective.
- Publishers could even just allow readers to sort comments by toxicity themselves, making it easier to find great discussions hidden under toxic ones.We’ve been testing a version of this technology with The New York Times, where an entire team sifts through and moderates each comment before it’s posted — reviewing up to 11,000 comments every day.
- We’ve worked together to train models that allows Times moderators to sort through comments more quickly, and we’ll work with them to enable comments on more articles every day.WHERE WE GO FROM HEREPerspective joins the TensorFlow library and the Cloud Machine Learning Platform as one of many new machine learning resources Google has made available to developers.
Imagine trying to have a conversation with your friends about the news you read this morning, but every time you said something, someone shouted in your face, called you a nasty name or accused you…
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- Unfortunately, this happens all too frequently online as people try to discuss ideas on their favorite news sites but instead get bombarded with toxic comments.
- According to the same report, online harassment has affected the lives of roughly 140 million people in the U.S., and many more elsewhere.
- News organizations want to encourage engagement and discussion around their content, but find that sorting through millions of comments to find those that are trolling or abusive takes a lot of money, labor, and time.
- As a result, many sites have shut down comments altogether.
- Through an API, publishers—including members of the Digital News Initiative—and platforms can access this technology and use it for their sites.
Google and Jigsaw announce the launch of Perspective, an early-stage technology that uses machine learning to identify toxic comments.
Continue reading “When computers learn to swear: Using machine learning for better online conversations”