Deep Learning for Telecom (with Python)

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

Audience

• Developers
• Data scientists

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.

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