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Working with TensorFlow

Learn how to use TensorFlow to build Deep Learning models.

The abundance of data and affordable cloud scale has led to an explosion of interest in Deep Learning. Google has released an excellent open-source library called TensorFlow. This library allows for state-of-the-art machine learning at scale with GPU-based acceleration. This course explores algorithms, machine learning, data mining concepts, and how TensorFlow implements them.

GK# 100675
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Who Should Attend?

Experienced Developers, Data Scientist, Data Engineer, and others who seek to work with machine learning algorithms, machine learning, and deep learning fundamentals and concepts.


What You'll Learn

Join an engaging hands-on learning environment, where you’ll learn:

  • Core Deep Learning and Machine Learning math essentials
  • TensorFlow Overview and Basics
  • TensorFlow Operations
  • Neural Networks With TensorFlow
  • Deep Learning With TensorFlow

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.

Course Outline

Machine Learning and Deep Learning Overview

  • Mathematical Concepts
  • ML Overview
  • DL Overview

TensorFlow: Overview and Basics

  • TensorFlow: What is it? History and Background
  • Use cases and Key Applications
  • Machine Learning and Deep Learning Basics
  • Environment, Configuration Settings and Installation
  • TensorFlow Primitives
  • Declaring Tensors
  • Declaring Placeholders and Variables
  • Working with Matrices
  • Declaring Operations
  • Operations in Computational Graph
  • Nested Operations
  • Multiple Layers
  • Implementing Loss Functions
  • Implementing Back Propagation

Machine Learning With TensorFlow

  • Linear Regression Review
  • Linear Regression Using TensorFlow
  • Support Vector Machines (SVM) Review
  • SVM using TensorFlow
  • Nearest Neighbor Method Review
  • Nearest Neighbor Method using TensorFlow

Neural Networks With TensorFlow

  • Neural Networks Review
  • Optimization and Operational Gates
  • Working with Activation Functions
  • Implementing One-Layer Neural Network
  • Implementing Different Layers
  • Implementing Multilayer Neural Networks

Deep Neural Networks With TensorFlow

  • Models and Overview
  • Convolutional Neural Network Overview and Implementation
  • CNN Architecture
  • Recurrent Neural Network Overview and Implementation
  • RNN Architecture

Additional Topics

  • TensorFlow Extensions
  • Scikit Flow
  • TFLearn
  • TF-Slim
  • TensorLayer
  • Keras
  • Unit Testing
  • Taking your implementation to production


Before attending this course, you should have:

  • Strong Python Skills
  • Strong foundational mathematics in Linear Algebra and Probability; Matrix Transformation, Regressions, Standard Deviation, Statistics, Classification, etc.
  • Basic knowledge of machine learning and deep learning algorithms