| Sumario: | 6+ Hours of Video Instruction Learn the main concepts and techniques used in modern machine learning and deep neural networks through numerous examples written in PyTorch Overview This course begins with the basic concepts of machine and deep learning. Subsequently, you gain a reasonable familiarity with the main features of PyTorch and learn how it can be applied to some popular problem domains. About the Instructor David Mertz has been involved with the Python community for 20 years, with data science (under various earlier names), and with machine learning (since way back when it was more likely to be called “artificial intelligence”). He was a director of the Python Software Foundation for six years and continues to serve on, or chair, a variety of PSF working groups. He has also written quite a bit about Python: the column “Charming Python” for IBM developerWorks, for many years; the book Text Processing in Python (Addison-Wesley, 2003); and two short books for O’Reilly. He created the data science training program for Anaconda, Inc., and was a senior trainer for them. Skill Level Intermediate Learn How To Apply various machine and deep learning techniques Understand the difference between various machine and deep learning libraries Create classifiers Enhance an existing classifier Who Should Take This Course Programmers and statisticians interested in using Python and the PyTorch library to implement machine learning Course Requirements Programming experience Lesson Descriptions Lesson 1: What Is Machine Learning? What Is Deep Learning The first lesson begins with a high-level overview of the course. It then presents general concepts in machine learning and concepts specifically relevant to neural networks and deep learning. Ideas every data scientist should understand are discussed. The main libraries available for machine learning, and for deep learning specifically, are presented with an eye toward their comparison to PyTorch. The lesson contains an overview of basic concepts in neural networks. Also discussed is the basic idea of a perceptron and the enormous expansion of simple models with hardware that has become available in the last decade. The lesson delves into the main types of network layers available in neural networks. Activation functions are also discussed. Finally, the lesson finishes up with the importance of metrics in guiding refinements of machine learning models. Also discussed are a few of the most commonly used me...
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