Duration
28 hours (usually 4 days including breaks)
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
