Big Data Analytics: Distributed Computing Environments & Computational Graphs (TensorFlow)

Big Data Analytics: Distributed Computing Environments & Computational Graphs (TensorFlow)

Course Overview
This study material provides a comprehensive guide to computational graphs in TensorFlow, a popular framework for machine learning and deep learning. Learn about the fundamental concepts of TensorFlow, including tensors, computational graphs, and sessions. Explore practical examples of linear regression, automatic gradients, and techniques for handling large datasets.

Key Topics Covered:

• The Computational Graph:

  • TensorFlow: Introduction to TensorFlow and its role in machine learning and deep learning.
  • Tensors: Understanding tensors and their significance in TensorFlow.
  • Computational Graphs: Explore the concept of computational graphs and how they represent mathematical computations.
  • Sessions: Learn about TensorFlow sessions and their role in executing computational graphs.
  • Two Phases: Overview of the two phases in TensorFlow computations: graph construction and execution.
  • Hello TensorFlow: A simple example of TensorFlow operations by adding two constants.
  • Tensor Types: Different types of tensors and their uses in TensorFlow.
  • Operations: Understanding overloaded operators and TensorFlow operations.

• Variables:

  • Initializing from Other Variables: How to initialize TensorFlow variables using other variables.

• Example: Linear Regression:

  • Linear Regression & Computational Graph: Explore how linear regression is implemented using computational graphs in TensorFlow.

• Automatic Gradients:

  • Example: Linear Regression with Automatic Gradients: Practical example of using automatic gradients for linear regression.
  • How Automatic Gradients Work: Explanation of how TensorFlow computes gradients automatically.
  • Example: LinReg with Auto Grads / Computational Graph: Detailed example of linear regression with automatic gradients and its representation in computational graphs.

• Large Data I: Feeding:

  • Placeholder Nodes and Feeding: Introduction to placeholder nodes and feeding data into TensorFlow.
  • Example: Feeding SGD: Practical example of feeding data for stochastic gradient descent (SGD).

• Large Data II: Reader Nodes:

  • Reader Nodes: Understanding reader nodes for handling large datasets.
  • Example: SGD Reading On The Fly: Example of reading data on-the-fly during SGD.

• Debugging:

  • Visualize Computational Graph: Techniques for visualizing and debugging computational graphs in TensorFlow.

Why Choose This Material?

• Comprehensive guide to TensorFlow and its computational graph model.
• Practical examples and exercises for linear regression, automatic gradients, and large data handling.
• Ideal for students, machine learning practitioners, and data scientists looking to deepen their understanding of TensorFlow.

This material is perfect for individuals who want to master TensorFlow and understand its application in handling large datasets and building machine learning models.