Modern Deep Learning Techniques using TensorFlow Training

Course 1270

  • Duration: 2 days
  • Language: English
  • Level: Foundation

In this TensorFlow Deep Learning course, you will gain the skills and knowledge required to leverage TensorFlow to solve real-world Deep Learning problems. You will learn how to:

  • Understand tensors and their variables
  • Use TensorFlow's sequential and functional API

TensorFlow Deep Learning Training Delivery Methods

  • In-Person

  • Online

TensorFlow Deep Learning Training Benefits

Leverage artificial neural networks

Understand the strengths and limitations of TensorFlow

Use TensorFlow 2.x, from fundamentals to building blocksTrain, test, and evaluate a TensorFlow Model

Deploy a trained TensorFlow Model

Continue learning and face new challenges with after-course one-on-one instructor coaching

TensorFlow Deep Learning Training Outline

In this module, you will learn:

  • The basics of artificial neural networks
  • The lifecycle of building a TensorFlow model
  • The basics of Deep Learning
  • Layered architecture of TensorFlow
  • Constants, variables and Tensors


  • Introduction to Tensors and Variables

In this module, you will learn how to:

  • Use
  • Define a Keras Model
  • Build wide and deep models


  • Exploring
  • Using TensorFlow sequential API
  • Using TensorFlow functional API

In this module, you will learn about:

  • Defining gradient descent
  • Activation functions
  • Hyperparameters
  • Regularisation


  • Keras and TensorFlow

In this module, you will learn about:

  • Basics of Feature Engineering
  • Raw Data and Features
  • Feature Crosses
  • Transform


  • Feature Engineering
  • Basic Feature Engineering in Keras
  • Advanced Feature Engineering in Keras
  • Exploring Tf.transform

In this module, you will learn about:

  • Monitoring with TensorBoard
  • Saving and Versioning Model
  • Deploying Models


  • Using TensorBoard to Monitor the performance

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TensorFlow Deep Learning Training FAQs

TensorFlow is an open source library for numerical computation and it is used for large-scale machine learning. It uses Python as a front-end API for building applications with the framework, while executing those applications in high-performance C++.

Keras is a leading High-level API. It is written in python and was created to be user friendly and modular.

Keras is a high level library that cannot live on it's own, while TensorFlow is a framework that can live on it's own.