Course Outline
Introduction
Understanding the Fundamentals of Artificial Intelligence and Machine Learning for Government
Understanding Deep Learning for Government
- Overview of the Basic Concepts of Deep Learning
- Differentiating Between Machine Learning and Deep Learning
- Overview of Applications for Deep Learning in Government
Overview of Neural Networks for Government
- 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 for Government
- Exploring Convolutional Neural Networks in Practice for Government
- Improving the Way Neural Networks Learn for Government Applications
Overview of Deep Learning Techniques Used in Banking for Government
- Neural Networks
- Natural Language Processing
- Image Recognition
- Speech Recognition
- Sentiment Analysis
Exploring Deep Learning Case Studies for Banking in a Government Context
- Anti-Money Laundering Programs
- Know-Your-Customer (KYC) Checks
- Sanctions List Monitoring
- Billing Fraud Oversight
- Risk Management
- Fraud Detection
- Product and Customer Segmentation
- Performance Evaluation
- General Compliance Functions
Understanding the Benefits of Deep Learning for Banking in Government Operations
Exploring the Different Deep Learning Libraries for Python for Government Use
- TensorFlow
- Keras
Setting Up Python with TensorFlow for Deep Learning in a Government Environment
- Installing the TensorFlow Python API
- Testing the TensorFlow Installation
- Setting Up TensorFlow for Development in Government Projects
- Training Your First TensorFlow Neural Net Model for Government Applications
Setting Up Python with Keras for Deep Learning for Government Use
Building Simple Deep Learning Models with Keras for Government
- Creating a Keras Model
- Understanding Your Data in a Government Context
- Specifying Your Deep Learning Model for Government Needs
- Compiling Your Model for Government Applications
- Fitting Your Model to Government Datasets
- Working with Your Classification Data for Government Use
- Working with Classification Models for Government
- Using Your Models in Government Projects
Working with TensorFlow for Deep Learning for Banking and Government
- Preparing the Data
- Downloading the Data for Government Use
- Preparing Training Data for Government Applications
- Preparing Test Data for Government Validation
- Scaling Inputs for Government Standards
- Using Placeholders and Variables in a Government Context
- Specifying the Network Architecture for Government Needs
- Using the Cost Function in Government Models
- Using the Optimizer for Government Applications
- Using Initializers for Government Data
- Fitting the Neural Network to Government Datasets
- Building the Graph for Government Use
- Inference for Government Applications
- Loss Calculation for Government Models
- Training for Government Projects
- Training the Model for Government Operations
- The Graph in a Government Context
- The Session for Government Use
- Train Loop for Government Applications
- Evaluating the Model for Government Needs
- Building the Eval Graph for Government
- Evaluating with Eval Output in a Government Context
- Training Models at Scale for Government Projects
- Visualizing and Evaluating Models with TensorBoard for Government Use
Hands-on: Building a Deep Learning Credit Risk Model Using Python for Government
Extending Your Organization's Capabilities in Government Operations
- Developing Models in the Cloud for Government
- Using GPUs to Accelerate Deep Learning for Government Applications
- Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis in a Government Context
Summary and Conclusion for Government Use
Requirements
- Experience with Python programming for government applications
- General understanding of financial and banking principles
- Basic knowledge of statistical and mathematical concepts
Testimonials (2)
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course - Natural Language Processing with TensorFlow
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
