Machine learning from scratch in TensorFlow
What you'll learn, and how you can apply it
- Understand machine learning, neural networks, deep learning, and artificial intelligence basic concepts
- Learn what TensorFlow is and what applications it's good for
- Discover how to create deep learning models for classification and regression using TensorFlow
- Evaluate the benefits and disadvantages of using TensorFlow over other machine learning software
Who is this presentation for?
- You're a software engineer or programmer who wants to develop a basic understanding of machine learning.
- You have experience modeling or have a background in data science, and you want to learn TensorFlow.
- A working knowledge of Python
- Familiarity with matrices, modeling, and statistics
Hardware and/or installation requirements:
- A WiFi-enabled laptop (You'll be given a cloud instance with TensorFlow.)
- Introduction to TensorFlow
- Iterative algorithms
- Machine learning
- Basic neural networks
- Deep neural networks
- Variational autoencoders
- Convolutional neural networks
- Adversarial noise
- Recurrent neural networks
About your instructor
Dylan Bargteil is a data scientist in residence at the Data Incubator, where he works on research-guided curriculum development and instruction. Previously, he worked with deep learning models to assist surgical robots and was a research and teaching assistant at the University of Maryland, where he developed a new introductory physics curriculum and pedagogy in partnership with the Howard Hughes Medical Institute (HHMI). Dylan studied physics and math at the University of Maryland and holds a PhD in physics from New York University.
Get the Platinum pass or the Training pass to add this course to your package.
Comments on this page are now closed.
For conference registration information and customer service
For more information on community discounts and trade opportunities with O’Reilly conferences
For information on exhibiting or sponsoring a conference
For media/analyst press inquires