Get yourself trained on TensorFlow for Machine with this Online Training TensorFlow for Machine Learning and Neural Network Solutions.
Online Training TensorFlow for Machine Learning and Neural Network Solutions
TensorFlow is quickly becoming the technology of choice for machine learning, because of its ease to develop intelligent machine learning applications and powerful neural networks. If you’re a data professional who is familiar with Python and wants to use TensorFlow for performing machine learning activities on a day-to-day basis, then go for this learning path. This comprehensive 2-in-1 course gives you a clear understanding of machine learning models and the application of models at scale using clustering, classification, regression, and reinforcement learning, all with interesting examples and real-world use cases. Its a perfect blend of concepts and practical examples which makes it easy to understand and implement. It follows a logical flow where you will be able to develop efficient and intelligent applications based on your understanding of the different machine learning concepts with every section. This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Machine Learning with TensorFlow, starts off with installing TensorFlow on Mac OSX and Windows. You will then learn to solve common machine learning problem with the help of examples and real-world use cases. You will also learn how to use the two main UI tools for TensorFlow – Data Flow Graph and TensorBoard. Next, you will be gain in-depth knowledge of CNNs by comparing it with other neural networks. The second course, TensorFlow for Neural Network Solutions, teaches you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You will work on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning using TensorFlow. This course also covers advanced neural networks concepts such as CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last section will show you how to take TensorFlow to production. By the end of this Learning Path, youll be able to tackle common machine learning problems with Googles TensorFlow library and build deployable solutions as well as explore neural networks and machine learning concepts using the latest numerical computing library TensorFlow. Meet Your Expert(s): We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth: Shams Ul Azeem is an undergraduate of NUST Islamabad, Pakistan in Electrical Engineering. He has a great interest in computer science fields and has started his journey from Android development. Currently, he is pursuing his career in machine learning, particularly in deep learning by doing medical related freelance projects with different companies. He was also a member of RISE lab, NUST and has a publication in IEEE International Conference, ROBIO as a co-author on Designing of motions for humanoid goal keeper robots. Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow and Caesar’s Entertainment. He got his degrees in Applied Mathematics from The University of Montana and the College of Saint Benedict and Saint John’s University.He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence. Nick occasionally puts his thoughts and musings on his blog or through his Twitter account.
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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.