Get yourself trained on Applied Deep Learning with this Online Training Applied Deep Learning with Python: 2-in-1.
Online Training Applied Deep Learning with Python: 2-in-1
Taking an approach that uses the latest developments in the Python ecosystem, Applied Deep Learning with Python begins by guiding you through the Jupyter ecosystem, key visualization libraries, and powerful data sanitization techniques before you train our first predictive model. You’ll explore a variety of approaches to classification, such as support vector networks, random decision forests, and k-nearest neighbors to build out your understanding before you move into a more complex territory. It’s okay if these terms seem overwhelming; you’ll learn how to put them to work.You’ll build upon the classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. Then, you’ll start building out your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data.By guiding you through a trained neural network, this Learning Path explores common deep learning network architectures (convolutional, recurrent, generative adversarial) and branches out into deep reinforcement learning before you dive into model optimization and evaluation. You’ll do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.About the AuthorChris Dalla Villa has been professionally practicing data analytics since graduating with a master’s degree in Physics from the University of Guelph, Canada. He developed a keen interest in Python while researching quantum gases as part of his graduate studies. Alex is currently doing web data analytics, where Python continues to play a key role in his work. He is a frequent blogger about data-centric projects that involve Python and Jupyter Notebooks.Nimish Narang is a Harvard-trained analyst and a programmer, who specializes in designing and developing data science products. He is based in New York City, America. Luis is the head of the Data Products team at Forbes, where they investigate new techniques for optimizing article performance and create clever bots that help them distribute their content. He worked for the United Nations as part of the Humanitarian Data Exchange team (founders of the Center for Humanitarian Data). Later on, he led a team of scientists at the Flowminder Foundation, developing models for assisting the humanitarian community. Luis is a native of Havana, Cuba, and the founder and owner of a small consultancy firm dedicated to supporting the nascent Cuban private sector.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.