Best practices of orchestrating Python and R code in ML projects

Best practices of orchestrating #Python and #rstats code in #MachineLearning projects

  • Instead of arguing about Python vs R I will examine the best practices of integrating both languages in one data science project.
  • Today, data scientists are generally divided among two languages — some prefer R, some prefer Python.
  • Usually algorithms used for classification or regression are implemented in both languages and some scientist are using R while some of them preferring Python.
  • Instead of using logistic regression in R we will write Python jobs in which we will try to use random forest as training model.
  • py is presented below: – – Also here we are adding code for download necessary R and Python codes from above (clone the Githubrepository): – – Our dependency graph of this data science project look like this: – – Now lets see how it is possible to speed up and simplify…


Instead of arguing about Python vs R I will examine the best practices of integrating both languages in one data science project.

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Tensorflow Tutorial : Part 2 – Getting Started

Tensorflow Tutorial : Part 2 – Getting Started #abdsc

  • This post is the second part of the multi-part series on a complete tensorflow tutorial – – – If you have tensorflow already installed, you can just skip to the next section.
  • Below we have the different data types in supported by Tensorflow.
  • Note: Quantitized values [qint8, qint16 and quint8] are special values for tensorflow that help reduce the size of the data.
  • In fact, Google has gone to the extent of introducing Tensorflow Processing Units (TPUs) to speed up computation by leveraging quantitized values – – We will quickly generate some data to get started.
  • In the next part, we will finally be ready to train our first tensorflow model on house prices.

In this multi-part series, we will explore how to get started with tensorflow. This tensorflow tutorial will lay a solid foundation to this popular tool that…
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Learning through human feedback

Learning through human feedback  #DeepLearning #AI #ArtificialIntelligence

  • That is why DeepMind co-founded initiatives like the Partnership on AI to Benefit People and Society and why we have a team dedicated to technical AI Safety.
  • Research in this field needs to be open and collaborative to ensure that best practices are adopted as widely as possible, which is why we are also collaborating with OpenAI on research in technical AI Safety.
  • One of the central questions in this field is how we allow humans to tell a system what we want it to do and – importantly – what we don’t want it to do.
  • This is increasingly important as the problems we tackle with machine learning grow more complex and are applied in the real world.
  • The first results from our collaboration demonstrate one method to address this, by allowing humans with no technical experience to teach a reinforcement learning (RL) system – an AI that learns by trial and error – a complex goal.

A central question in technical AI safety is how to tell an algorithm what we want it to do. Working with OpenAI, we demonstrate a novel system that allows a human with no technical experience to teach an AI how to perform a complex task, such as manipulating a simulated robotic arm.
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A Designer’s Guide To The $15 Billion Artificial Intelligence Industry

A designer's guide to the $15 billion artificial intelligence industry  (from 2016)

  • The best designers know that they need to study human behavior if they want to make the right choices for their users.
  • It allows designers to cater to, and anticipate, individual users’ needs.
  • With AI, products and services aren’t just performing basic functions; they’re emotionally aware, letting designers create the best experience for each user.
  • Psychology: The way an AI system communicates with users at 2 p.m. should be different from the way it talks to them at 2 a.m., taking into account the unusually late time and understanding that the users are likely frustrated because they can’t sleep, playful because they’ve been drinking, or panicked because there’s been an emergency.
  • Designers have to understand the many ways users might react in different scenarios and how they will express their intention depending on factors like their mood, location, and what they ate that day.

As artificial intelligence gains ground, designers will need to adapt. Here’s how to get started.

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3 guiding principles for ethical AI, from IBM CEO Ginni Rometty

.@IBM 's CEO Ginni Rometty sums up the principles for ethical #AI into three words

  • For IBM, the purpose of AI will be to aid humans, not replace them. “
  • Google uses DeepMind AI to reduce energy use at data centers and save money (TechRepublic)
  • We say cognitive, not AI, because we are augmenting intelligence,” Rometty said. “
  • Microsoft’s new breakthrough: AI that’s as good as humans at listening…
  • You must be clear as you build AI platforms how they are trained, and what data was used in training. “

At the World Economic Forum on Tuesday, IBM CEO Ginni Rometty laid out three principles to guide technologists for responsible AI use as cognitive abilities continue to develop.
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A Designer’s Guide To The $15 Billion Artificial Intelligence Industry

A designer's guide to the $15 billion artificial intelligence industry

  • Sociology: Designers will need to consider when and if AI systems should be considered a part of society.
  • The best designers know that they need to study human behavior if they want to make the right choices for their users.
  • First a word on why designers should embrace artificial intelligence in the first place.
  • AI has the potential to fundamentally change how society functions .
  • With AI, products and services arent just performing basic functions; theyre emotionally aware, letting designers create the best experience for each user.

As artificial intelligence gains ground, designers will need to adapt. Here’s how to get started.
Continue reading “A Designer’s Guide To The $15 Billion Artificial Intelligence Industry”