I wrote the first edition of Thoughtful Machine Learning out of frustration over my . The. 2 | Chapter 1: Probably Approximately Correct Software. The process of hypothesizing, testing, and theorizing makes it very similar to the scientific method. 2 | Chapter 1: Test-Driven Machine Learning. Contribute to mindis/_MachineLearning_eBook development by creating an account on GitHub.

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Thoughtful Machine Learning Pdf

Results 1 - 10 Thoughtful Machine Learning. Pages · An Introduction to Machine Learning - Machine Learning Summer. Pages·· With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms - Selection from Thoughtful Machine Learning. Gain the confidence you need to apply machine learning in your daily work. With this practical guide, Chapter 1 Probably Approximately Correct Software.

Not in United States? Choose your country's store to see books available for download. See if you have enough points for this item. Sign in. Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.

Did you deliver measurable results? Direct marketing, curriculum design, product testing, movie-making—they all live within the axes of constraints and performance. They just opened the long-awaited Terminal B at LaGuardia Airport, previously one of the worst major airports in the world. At 6 am, the line for the ladies room is 20 people long. This part of the billion-dollar facility had just one job, and it failed.

The same is true for the design of the simple coffee stand. But despite the hard work of the tradespeople who followed the plans, the plans themselves were defective. Add to wishlist. Recently added by charky , NicholasNg08 , Wark , ibinu , jeremiah85 , roxolan , szarka , rosekitty.

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No library descriptions found. While it has already created tremendous value in leading technology companies such as Google, Baidu, Microsoft and Facebook, much of the additional waves of value creation will go beyond the software sector. These are the steps I recommend for transforming your enterprise with AI, which I will explain in this playbook: Execute pilot projects to gain momentum Build an in-house AI team Provide broad AI training Develop internal and external communications Execute pilot projects to gain momentum It is more important for your first few AI projects to succeed rather than be the most valuable AI projects.

They should be meaningful enough so that the initial successes will help your company gain familiarity with AI and also convince others in the company to invest in further AI projects; they should not be so small that others would consider it trivial.

The important thing is to get the flywheel spinning so that your AI team can gain momentum. Suggested characteristics for the first few AI projects: It should ideally be possible for a new or external AI team which may not have deep domain knowledge about your business to partner with your internal teams which have deep domain knowledge and build AI solutions that start showing traction within months The project should be technically feasible.

Have a clearly defined and measurable objective that creates business value. When I was leading the Google Brain team, there was significant skepticism within Google and more broadly, around the world of deep learning technology. To help the team gain momentum, I chose the Google Speech team as my first internal customer, and we worked closely with them to make Google Speech recognition much more accurate.

Speech recognition is a meaningful project within Google, but not the most important one—for example, it is less important to the company bottom line than applying AI to web search or advertising. But by making the Speech team more successful using deep learning, other teams started to gain faith in us, which enabled the Google Brain team to gain momentum.

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Once other teams started to see the success of Google Speech working with Google Brain, we were able to acquire more internal customers. With two successes, I started conversations with the advertising team.

Building up momentum gradually led to more and more successful AI projects. This process is a repeatable model that you can use in your company.

Build an in-house AI team While outsourced partners with deep technical AI expertise can help you gain that initial momentum faster, in the long term it will be more efficient to execute some projects with an in-house AI team. Further, you will want to keep some projects within the company to build a more unique competitive advantage.

Thoughtful Machine Learning with Python (pdf)

It is important to have buy-in from the C-suite to build this internal team. During the rise of the internet, hiring a CIO was a turning point for many companies to have a cohesive strategy for using the internet.

In contrast, the companies that ran many independent experiments—ranging from digital marketing to data science experiments to new website launches—failed to leverage internet capabilities if these small pilot projects did not manage to scale to transform the rest of the company. In the AI era, a key moment for many companies will again be the formation of a centralized AI team that can help the whole company.

After completing the initial projects, set up repeated processes to continuously deliver a sequence of valuable AI projects.

Develop consistent standards for recruiting and retention. Many companies are organized with multiple business units reporting to the CEO. New job descriptions and new team organizations will emerge. A good AI leader will be able to advise you on setting up the right processes.

Thoughtful Machine Learning - O'Reilly Media

Since the talent war is largely zero-sum in the short term, working with a recruiting partner that can help you build an AI team will give you a non-trivial advantage. However, providing training to your existing team can also be a good way to create a lot of new talent in-house.

While the media reports of high AI salaries are over-hyped the numbers quoted in press tend to be outliers , AI talent is hard to find.

Fortunately, with the rise of digital content, including MOOCs massive open online courses such as Coursera, ebooks, and YouTube videos, it is more cost effective than ever to train up large numbers of employees in new skills such as AI.

The smart CLO Chief Learning Officer knows that their job is to curate, rather than create content, and then to establish processes to ensure employees complete the learning experiences.