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Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms
Price: $24.99
(as of Dec 17,2024 14:19:30 UTC – Details)
Publisher : CreateSpace Independent Publishing Platform (November 26, 2013)
Language : English
Paperback : 222 pages
ISBN-10 : 1493682229
ISBN-13 : 978-1493682225
Item Weight : 13.8 ounces
Dimensions : 7.5 x 0.5 x 9.25 inches
Customers say
Customers find the book provides a good introduction to fundamental algorithms used in AI. They mention it has interesting content and clarifies some wrong assumptions. However, some readers feel the pacing lacks substance, there are formatting issues and errors, and there doesn’t seem to be any structure. There are mixed opinions on how well the author explains things simply and how accessible the content is. Some find it easy to understand and comprehend, while others say it’s difficult to understand key concepts.
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In this post, we will explore the basics of artificial intelligence and its fundamental algorithms in Volume 1 of our series “Artificial Intelligence for Humans.”
Artificial intelligence (AI) is a rapidly growing field that aims to develop machines and systems that can mimic human intelligence and perform tasks that typically require human intelligence, such as problem-solving, decision-making, and pattern recognition. AI algorithms are at the core of these intelligent systems, enabling them to learn from data, make predictions, and adapt to changing environments.
In Volume 1 of our series, we will cover some of the fundamental algorithms used in AI, including:
1. Supervised learning: In supervised learning, the algorithm is trained on a labeled dataset, where each data point is associated with a label or target value. The algorithm learns to map input data to output labels by minimizing the error between predicted and actual labels.
2. Unsupervised learning: In unsupervised learning, the algorithm is trained on an unlabeled dataset, where the goal is to discover patterns and relationships in the data without explicit guidance. Clustering and dimensionality reduction are common unsupervised learning techniques.
3. Reinforcement learning: In reinforcement learning, the algorithm learns to make sequential decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. The goal is to maximize long-term rewards by exploring different actions and learning from experience.
4. Neural networks: Neural networks are a class of algorithms inspired by the structure and function of the human brain. They consist of interconnected nodes (neurons) organized in layers, where each neuron computes a weighted sum of its inputs and applies an activation function to produce an output. Deep learning, a subset of neural networks, uses multiple layers to learn hierarchical representations of data.
5. Evolutionary algorithms: Evolutionary algorithms are inspired by the process of natural selection and genetic evolution. They use techniques such as mutation, crossover, and selection to evolve a population of candidate solutions to a problem, with the fittest individuals surviving and reproducing over generations.
By understanding these fundamental algorithms, you can gain insight into how AI systems work and how they can be applied to solve real-world problems. Stay tuned for more in-depth discussions on each algorithm in future volumes of “Artificial Intelligence for Humans.”
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