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Machine learning A-Z : introduction to AI digital brains of the future / Eddie Black

By: Material type: TextTextPublication details: [Place of publication not identifed] : Independently published, c2019Description: 51 pages ; 28 cmISBN:
  • 9781798130216
Subject(s): LOC classification:
  • Q 325.5 .B53 2019
Contents:
1. Part 1-Data Processing -- 2. Part 2.-Regression -- 3. Part 3.-Classification -- 4. Part 4. Clustering -- 5. Part 5. Association Rule Learning -- 6. Part 6. Reinforcement Learning -- 7. Part 7. Natural Language Processing -- 8. Part 8. Deep Learning -- 9. Part 9. Dimensionality Reduction -- 10. Part 10. Model Selection & Boosting.
Summary: Neural Network, Machine Learning, Deep Learning, Deep Neural Network, Artificial Neural Network, Recurrent/Convolutional/MLP.It all sounds very fancy but what does it really mean? How do I get a good basic understanding of these concepts? These questions i asked myself when i was new to the subject of Deep Learning. Everything seemed so complicated to me and as I was a complete beginner all the information I could find seemed to explain advanced mathematical equations that could as well have been written in Hieroglyphs as far as i was concerned. As I progressively learned how things related to each other, and how to understand the terminology used in the subject I realised the explanations could be simplified a lot to be much easier to grasp, just with some overall structure that anyone could understand. The goal of this book is to clarify some uncertainties and give you a solid foundation to be able to dig deeper into this incredibly exciting area of computer science.
Item type: Books
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Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Books Books National University - Manila LRC - Main General Circulation Machine Learning GC Q 325.5 .B53 2019 c.1 (Browse shelf(Opens below)) c.1 Available NULIB000017985

1. Part 1-Data Processing -- 2. Part 2.-Regression -- 3. Part 3.-Classification -- 4. Part 4. Clustering -- 5. Part 5. Association Rule Learning -- 6. Part 6. Reinforcement Learning -- 7. Part 7. Natural Language Processing -- 8. Part 8. Deep Learning -- 9. Part 9. Dimensionality Reduction -- 10. Part 10. Model Selection & Boosting.

Neural Network, Machine Learning, Deep Learning, Deep Neural Network, Artificial Neural Network, Recurrent/Convolutional/MLP.It all sounds very fancy but what does it really mean? How do I get a good basic understanding of these concepts? These questions i asked myself when i was new to the subject of Deep Learning. Everything seemed so complicated to me and as I was a complete beginner all the information I could find seemed to explain advanced mathematical equations that could as well have been written in Hieroglyphs as far as i was concerned. As I progressively learned how things related to each other, and how to understand the terminology used in the subject I realised the explanations could be simplified a lot to be much easier to grasp, just with some overall structure that anyone could understand. The goal of this book is to clarify some uncertainties and give you a solid foundation to be able to dig deeper into this incredibly exciting area of computer science.

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