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AI News Sunday, July 15

What do machine learning practitioners actually do? · fast.ai

  • It will address what it is that machine learning practitioners do, with Part 2 explaining AutoML and neural architecture search (which several high profile figures have suggested will be key to decreasing the need for data scientists) and Part 3 will cover Googles heavily hyped AutoML product in particular.
  • The authors identify a number of system-level interactions, risks, and anti-patterns, including: – – The authors write, A remarkable portion of real-world machine learning work is devoted to tackling issues of this form… Its worth noting that glue code and pipeline jungles are symptomatic of integration issues that may have…
  • (emphasis mine) – – In a previous post, I identified some failure modes in which machine learning projects are not effective in the workplace: – – I framed these as organizational failures in my original post, but they can also be described as various participants being overly focused on just…
  • Here are some of the things that machine learning practitioners may need to do during the process: – – Certainly, not every machine learning practitioner needs to do all of the above steps, but components of this process will be a part of many machine learning applications.
  • For myself and many others I know, I would highlight two of the most time-consuming and frustrating aspects of machine learning (in particular, deep learning) as: – – Dealing with data formatting, inconsistencies, and errors is often a messy and tedious process.

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What do machine learning practitioners actually do? · fast.ai

Google Colaboratory

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Google Colaboratory

Artificial Intelligence : The Risks and Benefits

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  • Digital Marketing Manager + Competitive Analysis Strategy

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Artificial Intelligence : The Risks and Benefits

What’s the Difference Between AI, Machine Learning, and Deep Learning?

  • Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
  • Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.
  • Lets look at a couple of problems to see how deep learning is different from simpler neural networks or other forms of machine learning.
  • There are many techniques for AI, but one subset of that bigger list is machine learning let the algorithms learn from the data.
  • Finally, deep learning is a subset of machine learning, using many-layered neural networks to solve the hardest (for computers) problems.

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What’s the Difference Between AI, Machine Learning, and Deep Learning? | Oracle Big Data Blog

Google Colaboratory

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Google Colaboratory

Sponsorship Opportunities For @CloudEXPO New York Open

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Sponsorship Opportunities For @CloudEXPO New York Open | #BigData #AI #DevOps #IoT #Blockchain #SmartCities – All The Internet Of Things

AI adoption at the atomic level of jobs and work

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AI adoption at the atomic level of jobs and work

Artificial Intelligence: Startup Hackrod – Digitalization & Software – Pictures of the Future – Innovation – Home – Siemens Global Website

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Artificial Intelligence: Startup Hackrod – Digitalization & Software – Pictures of the Future – Innovation – Home – Siemens Global Website

China International Robot Show: What’s the smart thinking on AI? – CGTN

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China International Robot Show: What’s the smart thinking on AI? – CGTN

Comprehensive Repository of Data Science and ML Resources

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Comprehensive Repository of Data Science and ML Resources