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

Requirements

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

Audience

  • Developers
  • Data scientists
  28 Hours
 

Number of participants


Starts

Ends


Dates are subject to availability and take place between 09:30 and 16:30.
Open Training Courses require 5+ participants.

Testimonials (5)

Related Courses

Related Categories