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Course Outline
- Overview of Neural Networks and Deep Learning for Government
- The Concept of Machine Learning (ML)
- Rationale for Utilizing Neural Networks and Deep Learning in Public Sector Operations
- Selecting Appropriate Neural Network Architectures for Diverse Problems and Data Types
- Learning and Validating Neural Networks for Government Applications
- Comparing Logistic Regression to Neural Networks in Government Contexts
- Neural Networks for Government
- Biological Inspirations Behind Neural Networks
- Components of Neural Networks: Neurons, Perceptrons, and Multilayer Perceptron (MLP) Models
- The Backpropagation Algorithm for Learning in MLPs
- Common Activation Functions: Linear, Sigmoid, Tanh, Softmax
- Loss Functions Suitable for Forecasting and Classification Tasks
- Key Parameters: Learning Rate, Regularization, Momentum
- Constructing Neural Networks Using Python
- Evaluating the Performance of Neural Networks in Python
- Basics of Deep Networks for Government
- An Introduction to Deep Learning
- Deep Network Architecture: Parameters, Layers, Activation Functions, Loss Functions, Solvers
- Restricted Boltzmann Machines (RBMs)
- Autoencoders for Data Compression and Feature Learning
- Deep Network Architectures for Government
- Deep Belief Networks (DBNs): Architecture and Applications
- Autoencoders for Dimensionality Reduction and Anomaly Detection
- Restricted Boltzmann Machines for Feature Learning
- Convolutional Neural Networks (CNNs) for Image Recognition
- Recursive Neural Networks for Hierarchical Data Structures
- Recurrent Neural Networks (RNNs) for Sequence Prediction and Time Series Analysis
- Overview of Libraries and Interfaces Available in Python for Government
- Caffe: A Deep Learning Framework
- Theano: A Computational Library for Defining and Evaluating Mathematical Expressions
- TensorFlow: An Open-Source Platform for Machine Learning
- Keras: A High-Level Neural Networks API
- MxNet: A Deep Learning Framework for Flexibility and Efficiency
- Selecting the Appropriate Library for Specific Problems in Government
- Building Deep Networks in Python for Government
- Selecting the Most Suitable Architecture for a Given Problem
- Hybrid Deep Networks for Complex Tasks
- Learning Network: Choosing an Appropriate Library and Defining the Architecture
- Tuning the Network: Initialization, Activation Functions, Loss Functions, Optimization Methods
- Avoiding Overfitting: Identifying Overfitting Issues in Deep Networks and Applying Regularization Techniques
- Evaluating the Performance of Deep Networks for Government Applications
- Case Studies in Python for Government
- Image Recognition Using Convolutional Neural Networks (CNNs)
- Detecting Anomalies with Autoencoders
- Forecasting Time Series Data with Recurrent Neural Networks (RNNs)
- Dimensionality Reduction Using Autoencoders
- Classification Tasks with Restricted Boltzmann Machines (RBMs)
Requirements
A strong understanding and appreciation of machine learning, systems architecture, and programming languages are valuable for government positions.
14 Hours
