Deep Learning with TensorFlow: Applications of Deep Neural Networks to Machine Learning Tasks

6+ Hours of Video Instruction

Deep Learning with TensorFlow LiveLessons is an introduction to Deep Learning that bring the revolutionary machine-learning approach to life with interactive demos from the most popular Deep Learning library, TensorFlow, and its high-level API, Keras. 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.

The companion materials for this LiveLesson can be found at https://github.com/the-deep-learners/TensorFlow-LiveLessons/.

• Intermediate

• Build Deep Learning models in TensorFlow and Keras
• Interpret the results of Deep Learning models
• Troubleshoot and improve Deep Learning models
• Understand the language and fundamentals of artificial neural networks
• Build your own Deep Learning project
  

Who Should Take This Course

This course is perfectly suited to 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.

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
• First-year college calculus

About the Instructor

Jon Krohn is the chief data scientist at untapt, a machine learning startup in New York. He leads a Deep Learning Study Group and, having obtained his doctorate in neuroscience from Oxford University, continues to publish academic papers.

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