Deep learning with TensorFlow
What you'll learn, and how you can apply it
By the end of this two-day training course, you'll understand:
- What machine learning, neural networks, deep learning, and artificial intelligence are
- What TensorFlow is and what applications it's good for
And you'll be able 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 with a background in Python who wants to better understand machine learning.
- You have experience modeling or have a background in data science, and you want to learn TensorFlow and deep learning.
- You're in a nontechnical role, and you want to more effectively communicate with the engineers and data scientists in your company about TensorFlow and neural networks.
- Familiarity with Python, matrices, modeling, and statistics
- 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
Michael Cullan is a data scientist in residence at Pragmatic Institute, where he teaches hands-on courses in data science and business-oriented topics in managing data science initiatives at the organizational level. He also leads internal data science projects in support of marketing and operations teams. He earned a master’s degree in statistics and a bachelor’s degree in mathematics. His academic research areas ranged from computational paleobiology, where he developed software for measuring evidence for disparate evolutionary models based on fossil data, to music and AI, where he assisted in modeling musical data for a jazz improvisation robot. In his free time, he applies his math and programming skills toward creating code-based visual art and design projects.
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