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Building Machine Learning Systems with Python : Explore Machine Learning and Deep Learning Techniques for Building Intelligent Systems Using Scikit-Learn and TensorFlow, 3rd Edition.
Machine learning allows models or systems to learn without being explicitly programmed. You will see how to use the best of libraries support such as scikit-learn, Tensorflow and much more to build efficient smart systems.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
|
| Edición: | 3rd ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 082 | 0 | 4 | |a 005.133 |2 23 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Pedro Coelho, Luis. | |
| 245 | 1 | 0 | |a Building Machine Learning Systems with Python : |b Explore Machine Learning and Deep Learning Techniques for Building Intelligent Systems Using Scikit-Learn and TensorFlow, 3rd Edition. |
| 250 | |a 3rd ed. | ||
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2018. | ||
| 300 | |a 1 online resource (394 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Python Machine Learning; Machine learning and Python -- a dream team; What the book will teach you -- and what it will not; How to best read this book; What to do when you are stuck; Getting started; Introduction to NumPy, SciPy, Matplotlib, and TensorFlow; Installing Python; Chewing data efficiently with NumPy and intelligently with SciPy; Learning NumPy; Indexing; Handling nonexistent values; Comparing the runtime; Learning SciPy; Fundamentals of machine learning. | |
| 505 | 8 | |a Asking a questionGetting answers; Our first (tiny) application of machine learning; Reading in the data; Preprocessing and cleaning the data; Choosing the right model and learning algorithm; Before we build our first model; Starting with a simple straight line; Toward more complex models; Stepping back to go forward -- another look at our data; Training and testing; Answering our initial question; Summary; Chapter 2: Classifying with Real-World Examples; The Iris dataset; Visualization is a good first step; Classifying with scikit-learn; Building our first classification model. | |
| 505 | 8 | |a Evaluation -- holding out data and cross-validationHow to measure and compare classifiers; A more complex dataset and the nearest-neighbor classifier; Learning about the seeds dataset; Features and feature engineering; Nearest neighbor classification; Looking at the decision boundaries; Which classifier to use; Summary; Chapter 3: Regression; Predicting house prices with regression; Multidimensional regression; Cross-validation for regression; Penalized or regularized regression; L1 and L2 penalties; Using Lasso or ElasticNet in scikit-learn; Visualizing the Lasso path. | |
| 505 | 8 | |a P-greater-than-N scenariosAn example based on text documents; Setting hyperparameters in a principled way; Regression with TensorFlow; Summary; Chapter 4: Classification I -- Detecting Poor Answers; Sketching our roadmap; Learning to classify classy answers; Tuning the instance; Tuning the classifier; Fetching the data; Slimming the data down to chewable chunks; Preselecting and processing attributes; Defining what a good answer is; Creating our first classifier; Engineering the features; Training the classifier; Measuring the classifier's performance; Designing more features. | |
| 505 | 8 | |a Deciding how to improve the performanceBias, variance and their trade-off; Fixing high bias; Fixing high variance; High or low bias?; Using logistic regression; A bit of math with a small example; Applying logistic regression to our post-classification problem; Looking behind accuracy -- precision and recall; Slimming the classifier; Ship it!; Classification using Tensorflow; Summary; Chapter 5: Dimensionality Reduction; Sketching our roadmap; Selecting features; Detecting redundant features using filters; Correlation; Mutual information; Asking the model about the features using wrappers. | |
| 500 | |a Other feature selection methods. | ||
| 520 | |a Machine learning allows models or systems to learn without being explicitly programmed. You will see how to use the best of libraries support such as scikit-learn, Tensorflow and much more to build efficient smart systems. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 2 | |a Machine Learning | |
| 650 | 4 | |a Python (Computer Program Language) | |
| 650 | 4 | |a Machine Learning. | |
| 650 | 4 | |a Artificial Intelligence. | |
| 650 | 4 | |a Computers |x Languages |x Python. | |
| 650 | 4 | |a Computers |x Data Science |x Machine Learning. | |
| 650 | 4 | |a Computers |x Artificial Intelligence |x General. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Information architecture. |2 bicssc | |
| 650 | 7 | |a Database design & theory. |2 bicssc | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Computers |x Data Modeling & Design. |2 bisacsh | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 700 | 1 | |a Richert, Wilhelm. | |
| 700 | 1 | |a Brucher, Matthieu. | |
| 758 | |i has work: |a Building machine learning systems with Python (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFK9g48VCV4mMM8tRB8YMX |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Pedro Coelho, Luis. |t Building Machine Learning Systems with Python : Explore Machine Learning and Deep Learning Techniques for Building Intelligent Systems Using Scikit-Learn and TensorFlow, 3rd Edition. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781788623223 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5485017 |z Texto completo |
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