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Modern Deep Learning Techniques using TensorFlow Training

Course 1270

  • Duration: 2 days
  • Language: English
  • Level: Foundation
Get This Course 20.900 kr
  • 2-day instructor led training course

  • After-course coaching available

#1270
  • jun 15 - 16 10:00 - 17:30 CEST
    AnyWare
  • aug 15 - 16 15:00 - 22:30 CEST
    Ottawa or AnyWare
  • sep 14 - 15 10:00 - 17:30 CEST
    AnyWare
  • okt 5 - 6 15:00 - 22:30 CEST
    Herndon, VA or AnyWare
  • nov 30 - dec 1 10:00 - 17:30 CET
    AnyWare
  • feb 13 - 14 15:00 - 22:30 CET
    Ottawa or AnyWare
  • feb 22 - 23 10:00 - 17:30 CET
    AnyWare

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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 blocks
  • Train, 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

Lab:

  • Introduction to Tensors and Variables

In this module, you will learn how to:

  • Use tf.data
  • Define a Keras Model
  • Build wide and deep models

Lab:

  • Exploring tf.data
  • Using TensorFlow sequential API
  • Using TensorFlow functional API

In this module, you will learn about:

  • Defining gradient descent
  • Activation functions
  • Hyperparameters
  • Regularisation

Lab:

  • Keras and TensorFlow

In this module, you will learn about:

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

Lab:

  • 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

Lab:

  • 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.