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 the Data Incubator, where he combines a passion for teaching and statistical programming. He has three years of teaching experience in academic and professional settings and four years of research experience spanning topics in nonparametric statistics, applied mathematics, and artificial intelligence. He holds a master’s degree in statistics.
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