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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
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.
