Language:
English
Pages:
1 online resource (1 video file, approximately 7 hr., 19 min.)
Edition:
1st edition
Keywords:
Electronic videos ; local
Abstract:
7+ Hours of Video Instruction An intuitive, application-focused introduction to deep learning and TensorFlow, Keras, and PyTorch Overview Deep Learning with TensorFlow, Keras, and PyTorch LiveLessons is an introduction to deep learning that brings the revolutionary machine-learning approach to life with interactive demos from the most popular deep learning library, TensorFlow, and its high-level API, Keras, as well as the hot new library PyTorch. Essential theory is whiteboarded to provide an intuitive understanding of deep learning's underlying foundations; i.e., artificial neural networks. Paired with tips for overcoming common pitfalls and hands-on code run-throughs provided in Python-based Jupyter notebooks, this foundational knowledge empowers individuals with no previous understanding of neural networks to build powerful state-of-the-art deep learning models. About the Instructor Jon Krohn is the Chief Data Scientist at the machine learning company untapt. He presents a popular series of tutorials published by Addison-Wesley and is the author of the acclaimed book Deep Learning Illustrated . Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy. He holds a doctorate in neuroscience from Oxford University, lectures at Columbia University, and carries out machine vision research at Columbia's Irving Medical Center. Skill Level Intermediate Learn How To Build deep learning models in all the major libraries: TensorFlow, Keras, and PyTorch Understand the language and theory of artificial neural networks Excel across a broad range of computational problems including machine vision, natural language processing, and reinforcement learning Create algorithms with state-of-the-art performance by fine-tuning model architectures Self-direct and complete your own Deep Learning projects Who Should Take This Course Software engineers, data scientists, analysts, and statisticians with an interest in deep learning. Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful. Previous experience with statistics or machine learning is not necessary. Course Requirements Some experience with any of the following are an asset, but none are essential: Object-oriented programming, specifically Python Simple shell commands; e.g., in Bash Machine learning or statistics Lesson Descriptions Lesson 1: Introduction to Deep Learning and Artifi...
Note:
Online resource; Title from title screen (viewed February 7, 2020)
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Mode of access: World Wide Web.
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