Course Outline
Introduction
Fundamentals of Artificial Intelligence and Machine Learning
Understanding Deep Learning
- Overview of the Basic Concepts of Deep Learning for Government
- Differentiating Between Machine Learning and Deep Learning
- Overview of Applications for Deep Learning in Public Sector Workflows
Overview of Neural Networks
- What are Neural Networks
- Neural Networks vs Regression Models
- Understanding Mathematical Foundations and Learning Mechanisms
- Constructing an Artificial Neural Network for Government Applications
- Understanding Neural Nodes and Connections in Public Sector Contexts
- Working with Neurons, Layers, and Input and Output Data for Enhanced Governance
- Understanding Single Layer Perceptrons for Efficient Decision-Making
- Differences Between Supervised and Unsupervised Learning in Government Projects
- Learning Feedforward and Feedback Neural Networks for Improved Accountability
- Understanding Forward Propagation and Back Propagation for Transparent Processes
- Understanding Long Short-Term Memory (LSTM) for Dynamic Data Analysis
- Exploring Recurrent Neural Networks in Practice for Government Operations
- Exploring Convolutional Neural Networks in Practice for Enhanced Public Services
- Improving the Way Neural Networks Learn to Optimize Government Workflows
Overview of Deep Learning Techniques Used in Telecom
- Neural Networks for Network Optimization
- Natural Language Processing for Customer Service Automation
- Image Recognition for Security and Monitoring
- Speech Recognition for Voice-Activated Services
- Sentiment Analysis for Public Opinion Tracking
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 for Enhanced Security
- Analyzing Customer Behavior to Identify Demand for New Products and Services for Informed Policy-Making
- Processing Large Volumes of SMS Messages to Gain Insights for Public Engagement
- Speech Recognition for Support Calls for Improved Customer Experience
- Configuring SDNs and Virtualized Networks in Real Time for Efficient Resource Management
Understanding the Benefits of Deep Learning for Telecom
Exploring the Different Deep Learning Libraries for Python
- TensorFlow
- Keras
Setting Up Python with TensorFlow for Deep Learning
- Installing the TensorFlow Python API for Government Use
- Testing the TensorFlow Installation to Ensure Compliance
- Setting Up TensorFlow for Development in Public Sector Projects
- Training Your First TensorFlow Neural Net Model for Enhanced Governance
Setting Up Python with Keras for Deep Learning
Building Simple Deep Learning Models with Keras
- Creating a Keras Model for Government Applications
- Understanding Your Data for Informed Decision-Making
- Specifying Your Deep Learning Model for Efficient Processes
- Compiling Your Model for Optimal Performance
- Fitting Your Model to Ensure Accuracy
- Working with Your Classification Data for Enhanced Analysis
- Working with Classification Models for Improved Outcomes
- Using Your Models for Government Operations
Working with TensorFlow for Deep Learning for Telecom
- Preparing the Data
- Downloading the Data from Reliable Sources
- Preparing Training Data for Government Use Cases
- Preparing Test Data to Ensure Validity
- Scaling Inputs for Consistent Results
- Using Placeholders and Variables for Efficient Management
- Specifying the Network Architecture for Government Applications
- Using the Cost Function for Accurate Evaluations
- Using the Optimizer to Enhance Performance
- Using Initializers for Reliable Start Points
- Fitting the Neural Network for Optimal Results
- Building the Graph
- Inference for Data Analysis
- Loss for Error Minimization
- Training for Continuous Improvement
- Training the Model
- The Graph for Structured Operations
- The Session for Execution Management
- Train Loop for Iterative Learning
- Evaluating the Model
- Building the Eval Graph for Comprehensive Assessment
- Evaluating with Eval Output for Transparent Reporting
- Training Models at Scale for Large-Scale Government Projects
- Visualizing and Evaluating Models with TensorBoard for Enhanced Oversight
Hands-on: Building a Deep Learning Customer Churn Prediction Model Using Python for Government
Extending Your Company's Capabilities
- Developing Models in the Cloud for Scalable Solutions
- Using GPUs to Accelerate Deep Learning for Faster Insights
- Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis in Government Services
Summary and Conclusion
Requirements
- Experience with Python programming for government applications
- General familiarity with telecommunications concepts
- Basic understanding of statistics and mathematical principles
Audience
- Developers in the public sector
- Data scientists working for government agencies
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
