Course Code

dlfortelecomwithpython
 

     Duration

28 Hours
 
 

     Requirements

  • Experience with Python programming
  • General familiarity with telecom concepts
  • Basic familiarity with statistics and mathematical concepts

Audience

  • Developers
  • Data scientists
 

     Overview

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed.

Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.

Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model.

By the end of this training, participants will be able to:

  • Understand the fundamental concepts of deep learning.
  • Learn the applications and uses of deep learning in telecom.
  • Use Python, Keras, and TensorFlow to create deep learning models for telecom.
  • Build their own deep learning customer churn prediction model using Python.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
 

     Course Outline

Introduction

Fundamentals of Artificial Intelligence and Machine Learning

Understanding Deep Learning

  • Overview of the Basic Concepts of Deep Learning
  • Differentiating Between Machine Learning and Deep Learning
  • Overview of Applications for Deep Learning

Overview of Neural Networks

  • What are Neural Networks
  • Neural Networks vs Regression Models
  • Understanding Mathematical Foundations and Learning Mechanisms
  • Constructing an Artificial Neural Network
  • Understanding Neural Nodes and Connections
  • Working with Neurons, Layers, and Input and Output Data
  • Understanding Single Layer Perceptrons
  • Differences Between Supervised and Unsupervised Learning
  • Learning Feedforward and Feedback Neural Networks
  • Understanding Forward Propagation and Back Propagation
  • Understanding Long Short-Term Memory (LSTM)
  • Exploring Recurrent Neural Networks in Practice
  • Exploring Convolutional Neural Networks in practice
  • Improving the Way Neural Networks Learn

Overview of Deep Learning Techniques Used in Telecom

  • Neural Networks
  • Natural Language Processing
  • Image Recognition
  • Speech Recognition
  • Sentiment Analysis

Exploring Deep Learning Case Studies for Telecom

  • Optimizing Routing and Quality of Service Through Real Time Network Traffic Analysis
  • Predicting Network and Device Failures, Outages, Demand Surges, etc.
  • Analyzing Calls in Real Time to Identify Fraudulent Behavior
  • Analyzing Customer Behavior to Identify Demand for New Products and Services
  • Processing Large Volumes of SMS Messages to Gain Insights
  • Speech Recognition for Support Calls
  • Configuring SDNs and Virtualized Networks in Real Time

Understanding the Benefits of Deep Learning for Telecom

Exploring the Different Deep Learning Libraries for Python

  • TensorFlow
  • Keras

Setting Up Python with the TensorFlow for Deep Learning

  • Installing the TensorFlow Python API
  • Testing the TensorFlow Installation
  • Setting Up TensorFlow for Development
  • Training Your First TensorFlow Neural Net Model

Setting Up Python with Keras for Deep Learning

Building Simple Deep Learning Models with Keras

  • Creating a Keras Model
  • Understanding Your Data
  • Specifying Your Deep Learning Model
  • Compiling Your Model
  • Fitting Your Model
  • Working with Your Classification Data
  • Working with Classification Models
  • Using Your Models 

Working with TensorFlow for Deep Learning for Telecom

  • Preparing the Data
    • Downloading the Data
    • Preparing Training Data
    • Preparing Test Data
    • Scaling Inputs
    • Using Placeholders and Variables
  • Specifying the Network Architecture
  • Using the Cost Function
  • Using the Optimizer
  • Using Initializers
  • Fitting the Neural Network
  • Building the Graph
    • Inference
    • Loss
    • Training
  • Training the Model
    • The Graph
    • The Session
    • Train Loop
  • Evaluating the Model
    • Building the Eval Graph
    • Evaluating with Eval Output
  • Training Models at Scale
  • Visualizing and Evaluating Models with TensorBoard 

Hands-on: Building a Deep Learning Customer Churn Prediction Model Using Python

Extending your Company's Capabilities

  • Developing Models in the Cloud
  • Using GPUs to Accelerate Deep Learning
  • Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis

Summary and Conclusion

 

     Feedback (1)

lots of information, all questions ansered, interesting examples

A1 Telekom Austria AG


The course could be tailored to suit your needs and objectives. It can also be delivered on your premises if preferred.


  
  
  


  

Online Price per participant 6000 AED

  

Classroom Price per participant 6000 AED

Starts

 

Ends

 

  Workday courses take place between 9:30 and 16:30

Location


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Number of Participants






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