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 for Government Telecom Applications
- Neural Networks
- Natural Language Processing
- Image Recognition
- Speech Recognition
- Sentiment Analysis
Exploring Deep Learning Case Studies for Government Telecom Applications
- 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 Government Telecom Applications
Exploring Different Deep Learning Libraries for Python
- TensorFlow
- Keras
Setting Up Python with TensorFlow for Deep Learning in Government Applications
- 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 in Government Applications
Building Simple Deep Learning Models with Keras for Government Use
- 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 in Government Telecom Applications
- 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 for Government Applications
Extending Your Organization's Capabilities for Government Use
- 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 for government applications
- General understanding of telecommunications concepts
- Basic knowledge of statistical and mathematical principles
Audience
- Software developers
- Data scientists
Testimonials (5)
examples based on our data
Witold - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)
code examples:-)
Marcin - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)
I liked that the instructor had many pre-written scripts to show off many different aspects of ML and AI. I really enjoyed being able to see live demos of so many ways ML and AI is being used. Much of what we covered was cutting edge technology that is still in its early stages of development.
Matthew Pepper - Motorola Solutions
Course - Deep Learning for Telecom (with Python)
The colab notebooks we get to keep
Palmer Greer - Motorola Solutions
Course - Deep Learning for Telecom (with Python)
The clarity with which it was presented
