We aim to help students understand the graphical computational model of TensorFlow explore the functions it has to offer
TensorFlow is a powerful open-source software library for machine learning developed by researchers at Google
What this course is about:
TensorFlow is a powerful open-source software library for machine learning developed by researchers at Google Brain.
It has many pre-built functions to ease the task of building different neural networks. TensorFlow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. This course (and others we will offer in the future) will cover the fundamentals and contemporary usage of the TensorFlow library for machine learning research.
Who this program is for:
We aim to help students understand the graphical computational model of TensorFlow explore the functions it has to offer, and learn how to build and structure models best suited for machine learning projects. Through the course, students will be guided through TensorFlow models of increasing complexity, from simple linear/logistic regression to neural networks.
You will learn:
- TensorFlow - The Big Picture
- What is a Tensor? Declaring Tensors
- Working with Matrices in TF
- Using Placeholders and Variables
- Declaring Operations Operations in a TF Graph
- Loss Functions & The Basics of Optimization
- Implementing a Deep Learning Neural Network in TF
Prerequisites & Preparation:
- Basic Python skills needed
- You must understand how to *write* and *call* functions in Python
- Anaconda for Python 3