This course provides essential coverage of artificial intelligence and neural networks in easy-to-understand, plain English. The course provides concrete coverage of the primary parts of AI, including learning approaches, functional areas that AI systems are used for and a thorough introduction to neural networks, how they exist, how they work and how they can be used to process information. The course further establishes a step-by-step process for assembling an AI system, thereby illustrating how and when different practices and components of AI systems with neural networks need to be defined and applied. Finally, the course provides a set of key principles and best practices for AI projects.
The following primary topics are covered:
- – AI Business and Technology Drivers
- β AI Benefits and Challenges
- β Business Problem Categories Addressed by AI
- β AI Types (Narrow, General, Symbolic, Non-Symbolic, etc.)
- β Common AI Learning Approaches and Algorithms
- β Supervised Learning, Unsupervised Learning, Continuous Learning
- β Heuristic Learning, Semi-Supervised Learning, Reinforcement Learning
- β Common AI Functional Designs
- β Computer Vision, Pattern Recognition
- β Robotics, Natural Language Processing (NLP)
- β Speech Recognition, Natural Language Understanding (NLU)
- β Frictionless Integration, Fault Tolerance Model Integration
- β Neural Networks, Neurons, Layers, Links, Weights
- β Understanding AI Models and Training Models and Neural Networks
- β Understanding how Models and Neural Networks Exist
- β Loss, Hyperparameters, Learning Rate, Bias, Epoch
- β Activation Functions (Sigmoid, Tanh, ReLU, Leaky RelU, Softmax, Softplus)
- β Neuron Cell Types (Input, Backfed, Noisy, Hidden, Probabilistic, Spiking, Recurrent, Memory, Kernel, nvolution, Pool, Output, Match Input, etc.)
- β Fundamental and Specialized Neural Network Architectures
- β Perceptron, Feedforward, Deep Feedforward, AutoEncoder, Recurrent, Long/Short Term Memory
- β Deep Convolutional Network, Extreme Learning Machine, Deep Residual Network
- β Support Vector Machine, Kohonen Network, Hopfield Network
- β Generative Adversarial Network, Liquid State Machine
- β How to Build an AI System (Step-by-Step)
- β Common AI System Design Principles and Common AI Project Best Practices
Duration: 1 Day
Taking the Course at a Workshop
This course can be taken as part of instructor-led workshops taught by Arcitura Certified Trainers. These workshops can be open for public registration or delivered privately for a specific organization. Certified Trainers can teach workshops in-person at a specific location or virtually using a video-enabled remote system, such as WebEx.
Visit the Workshop Calendar page to view the current calendar of public workshops.
Visit the Private Training page to learn more about Arcituraβs worldwide private workshop delivery options.
Taking the Course via an eLearning Study Kit
This course can be completed via self-study by purchasing an eLearning study kit subscription, which includes online video lessons, as well as online and offline access to the electronic course materials and additional supplements and resources designed for self-paced study and exam preparation.
Visit the eLearning Study Kits page for more information about eLearning study kits.
Visit the Digital Transformation online store for purchasing information.
Taking the Course via a Printed Study Kit
This course can be completed via self-study by purchasing a printed study kit, which includes the full-color course materials as well as additional supplements and resources designed specifically for self-paced study and exam preparation.
Visit the Printed Study Kits page for more information about full-color printed study kits.
Visit the Digital Transformation online store for purchasing information.
Certifications
This course is part of the following certification track(s):