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Modern Deep Learning Techniques using TensorFlow Training

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

Include dates with afternoon start times

#1270

nov 30 - dec 1 10:00 - 17:30 CET

London or Virtual
feb 13 - 14 15:00 - 22:30 CET

Ottawa or Virtual
feb 22 - 23 10:00 - 17:30 CET

London or Virtual
apr 3 - 4 15:00 - 22:30 CEST

Herndon, VA or Virtual
maj 31 - jun 1 10:00 - 17:30 CEST

London or Virtual
jul 31 - aug 1 15:00 - 22:30 CEST

Ottawa or Virtual

Bring this or any training to your organisation
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Delivered when, where, and how you want it
Blended learning models
Tailored content
Expert team coaching

#1270

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

Module 1: Understanding Artificial Neural Networks and TensorFlow

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

Module 2: Feature Engineering with TensorFlow

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

Module 3: TensorFlow Under the Hood

In this module, you will learn about:

Defining gradient descent
Activation functions
Hyperparameters
Regularisation

Lab:

Keras and TensorFlow

Module 4: Feature Engineering

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

Module 5: Monitoring and Deployment

In this module, you will learn about:

Monitoring with TensorBoard
Saving and Versioning Model
Deploying Models

Lab:

Using TensorBoard to Monitor the performance

Need Help Finding The Right Training Solution?

Our training advisors are here for you.

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.

Modern Deep Learning Techniques using TensorFlow Training

TensorFlow Deep Learning Training Delivery Methods

TensorFlow Deep Learning Training Benefits

TensorFlow Deep Learning Training Outline

Module 1: Understanding Artificial Neural Networks and TensorFlow

Module 2: Feature Engineering with TensorFlow

Module 3: TensorFlow Under the Hood

Module 4: Feature Engineering

Module 5: Monitoring and Deployment

Need Help Finding The Right Training Solution?

TensorFlow Deep Learning Training FAQs

What is TensorFlow?

What is Keras?

What is the difference between Keras and TensorFlow?

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