Artificial intelligence with python cookbook :: proven recipes for applying ai algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6
If you are looking to build next-generation AI solutions for work or even for your pet projects, you'll find this cookbook useful. With the help of easy-to-follow recipes, this book will take you through the advanced AI and machine learning approaches and algorithms that are required to build s...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
PACKT Publishing,
2020.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | If you are looking to build next-generation AI solutions for work or even for your pet projects, you'll find this cookbook useful. With the help of easy-to-follow recipes, this book will take you through the advanced AI and machine learning approaches and algorithms that are required to build smart models for problem-solving. |
| Beschreibung: | 1 online resource |
| ISBN: | 9781789137965 1789137969 |
Internformat
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| 520 | |a If you are looking to build next-generation AI solutions for work or even for your pet projects, you'll find this cookbook useful. With the help of easy-to-follow recipes, this book will take you through the advanced AI and machine learning approaches and algorithms that are required to build smart models for problem-solving. | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started with Artificial Intelligence in Python -- Technical requirements -- Setting up a Jupyter environment -- Getting ready -- How to do it... -- Installing libraries with Google Colab -- Self-hosting a Jupyter Notebook environment -- How it works... -- There's more... -- See also -- Getting proficient in Python for AI -- Getting ready -- How to do it... -- Obtaining the history of Jupyter commands and outputs -- Execution history -- Outputs | |
| 505 | 8 | |a Auto-reloading packages -- Debugging -- Timing code execution -- Displaying progress bars -- Compiling your code -- Speeding up pandas DataFrames -- Parallelizing your code -- See also -- Classifying in scikit-learn, Keras, and PyTorch -- Getting ready -- How to do it... -- Visualizing data in seaborn -- Modeling in scikit-learn -- Modeling in Keras -- Modeling in PyTorch -- How it works... -- Neural network training -- The SELU activation function -- Softmax activation -- Cross-entropy -- See also -- Modeling with Keras -- Getting ready -- How to do it... -- Data loading and preprocessing | |
| 505 | 8 | |a Model training -- How it works... -- Maximal information coefficient -- Data generators -- Permutation importance -- See also -- Chapter 2: Advanced Topics in Supervised Machine Learning -- Technical requirements -- Transforming data in scikit-learn -- Getting ready -- How to do it... -- Encoding ranges numerically -- Deriving higher-order features -- Combining transformations -- How it works... -- There's more... -- See also -- Predicting house prices in PyTorch -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Live decisioning customer values | |
| 505 | 8 | |a Getting ready -- How to do it... -- How it works... -- Active learning -- Hoeffding Tree -- Class weighting -- See also -- Battling algorithmic bias -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Forecasting CO2 time series -- Getting ready -- How to do it... -- Analyzing time series using ARIMA and SARIMA -- How it works... -- There's more... -- See also -- Chapter 3: Patterns, Outliers, and Recommendations -- Clustering market segments -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Discovering anomalies | |
| 505 | 8 | |a Getting ready -- How to do it... -- How it works... -- k-nearest neighbors -- Isolation forest -- Autoencoder -- See also -- Representing for similarity search -- Getting ready -- How to do it... -- Baseline -- string comparison functions -- Bag-of-characters approach -- Siamese neural network approach -- How it works... -- Recommending products -- Getting ready -- How to do it... -- How it works... -- Precision at k -- Matrix factorization -- The lightfm model -- See also -- Spotting fraudster communities -- Getting ready -- How to do it... -- Creating an adjacency matrix | |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
| 650 | 0 | |a Python (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh96008834 | |
| 650 | 2 | |a Artificial Intelligence |0 https://id.nlm.nih.gov/mesh/D001185 | |
| 650 | 2 | |a Machine Learning |0 https://id.nlm.nih.gov/mesh/D000069550 | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Data capture & analysis. |2 bicssc | |
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| 776 | 0 | 8 | |i Print version: |a Auffarth, Ben |t Artificial Intelligence with Python Cookbook : Proven Recipes for Applying AI Algorithms and Deep Learning Techniques Using TensorFlow 2. x and Pytorch 1. 6 |d Birmingham : Packt Publishing, Limited,c2020 |z 9781789133967 |
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| contents | Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started with Artificial Intelligence in Python -- Technical requirements -- Setting up a Jupyter environment -- Getting ready -- How to do it... -- Installing libraries with Google Colab -- Self-hosting a Jupyter Notebook environment -- How it works... -- There's more... -- See also -- Getting proficient in Python for AI -- Getting ready -- How to do it... -- Obtaining the history of Jupyter commands and outputs -- Execution history -- Outputs Auto-reloading packages -- Debugging -- Timing code execution -- Displaying progress bars -- Compiling your code -- Speeding up pandas DataFrames -- Parallelizing your code -- See also -- Classifying in scikit-learn, Keras, and PyTorch -- Getting ready -- How to do it... -- Visualizing data in seaborn -- Modeling in scikit-learn -- Modeling in Keras -- Modeling in PyTorch -- How it works... -- Neural network training -- The SELU activation function -- Softmax activation -- Cross-entropy -- See also -- Modeling with Keras -- Getting ready -- How to do it... -- Data loading and preprocessing Model training -- How it works... -- Maximal information coefficient -- Data generators -- Permutation importance -- See also -- Chapter 2: Advanced Topics in Supervised Machine Learning -- Technical requirements -- Transforming data in scikit-learn -- Getting ready -- How to do it... -- Encoding ranges numerically -- Deriving higher-order features -- Combining transformations -- How it works... -- There's more... -- See also -- Predicting house prices in PyTorch -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Live decisioning customer values Getting ready -- How to do it... -- How it works... -- Active learning -- Hoeffding Tree -- Class weighting -- See also -- Battling algorithmic bias -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Forecasting CO2 time series -- Getting ready -- How to do it... -- Analyzing time series using ARIMA and SARIMA -- How it works... -- There's more... -- See also -- Chapter 3: Patterns, Outliers, and Recommendations -- Clustering market segments -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Discovering anomalies Getting ready -- How to do it... -- How it works... -- k-nearest neighbors -- Isolation forest -- Autoencoder -- See also -- Representing for similarity search -- Getting ready -- How to do it... -- Baseline -- string comparison functions -- Bag-of-characters approach -- Siamese neural network approach -- How it works... -- Recommending products -- Getting ready -- How to do it... -- How it works... -- Precision at k -- Matrix factorization -- The lightfm model -- See also -- Spotting fraudster communities -- Getting ready -- How to do it... -- Creating an adjacency matrix |
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| publisher | PACKT Publishing, |
| record_format | marc |
| spelling | Auffarth, Ben. Artificial intelligence with python cookbook : proven recipes for applying ai algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 Birmingham : PACKT Publishing, 2020. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier If you are looking to build next-generation AI solutions for work or even for your pet projects, you'll find this cookbook useful. With the help of easy-to-follow recipes, this book will take you through the advanced AI and machine learning approaches and algorithms that are required to build smart models for problem-solving. Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started with Artificial Intelligence in Python -- Technical requirements -- Setting up a Jupyter environment -- Getting ready -- How to do it... -- Installing libraries with Google Colab -- Self-hosting a Jupyter Notebook environment -- How it works... -- There's more... -- See also -- Getting proficient in Python for AI -- Getting ready -- How to do it... -- Obtaining the history of Jupyter commands and outputs -- Execution history -- Outputs Auto-reloading packages -- Debugging -- Timing code execution -- Displaying progress bars -- Compiling your code -- Speeding up pandas DataFrames -- Parallelizing your code -- See also -- Classifying in scikit-learn, Keras, and PyTorch -- Getting ready -- How to do it... -- Visualizing data in seaborn -- Modeling in scikit-learn -- Modeling in Keras -- Modeling in PyTorch -- How it works... -- Neural network training -- The SELU activation function -- Softmax activation -- Cross-entropy -- See also -- Modeling with Keras -- Getting ready -- How to do it... -- Data loading and preprocessing Model training -- How it works... -- Maximal information coefficient -- Data generators -- Permutation importance -- See also -- Chapter 2: Advanced Topics in Supervised Machine Learning -- Technical requirements -- Transforming data in scikit-learn -- Getting ready -- How to do it... -- Encoding ranges numerically -- Deriving higher-order features -- Combining transformations -- How it works... -- There's more... -- See also -- Predicting house prices in PyTorch -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Live decisioning customer values Getting ready -- How to do it... -- How it works... -- Active learning -- Hoeffding Tree -- Class weighting -- See also -- Battling algorithmic bias -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Forecasting CO2 time series -- Getting ready -- How to do it... -- Analyzing time series using ARIMA and SARIMA -- How it works... -- There's more... -- See also -- Chapter 3: Patterns, Outliers, and Recommendations -- Clustering market segments -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Discovering anomalies Getting ready -- How to do it... -- How it works... -- k-nearest neighbors -- Isolation forest -- Autoencoder -- See also -- Representing for similarity search -- Getting ready -- How to do it... -- Baseline -- string comparison functions -- Bag-of-characters approach -- Siamese neural network approach -- How it works... -- Recommending products -- Getting ready -- How to do it... -- How it works... -- Precision at k -- Matrix factorization -- The lightfm model -- See also -- Spotting fraudster communities -- Getting ready -- How to do it... -- Creating an adjacency matrix Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Data capture & analysis. bicssc Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Data Processing. bisacsh Computers Neural Networks. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast has work: Artificial Intelligence with Python Cookbook (Text) https://id.oclc.org/worldcat/entity/E39PD36t9C69dG8jrwVKGcTKtX https://id.oclc.org/worldcat/ontology/hasWork Print version: Auffarth, Ben Artificial Intelligence with Python Cookbook : Proven Recipes for Applying AI Algorithms and Deep Learning Techniques Using TensorFlow 2. x and Pytorch 1. 6 Birmingham : Packt Publishing, Limited,c2020 9781789133967 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2663695 Volltext |
| spellingShingle | Auffarth, Ben Artificial intelligence with python cookbook : proven recipes for applying ai algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started with Artificial Intelligence in Python -- Technical requirements -- Setting up a Jupyter environment -- Getting ready -- How to do it... -- Installing libraries with Google Colab -- Self-hosting a Jupyter Notebook environment -- How it works... -- There's more... -- See also -- Getting proficient in Python for AI -- Getting ready -- How to do it... -- Obtaining the history of Jupyter commands and outputs -- Execution history -- Outputs Auto-reloading packages -- Debugging -- Timing code execution -- Displaying progress bars -- Compiling your code -- Speeding up pandas DataFrames -- Parallelizing your code -- See also -- Classifying in scikit-learn, Keras, and PyTorch -- Getting ready -- How to do it... -- Visualizing data in seaborn -- Modeling in scikit-learn -- Modeling in Keras -- Modeling in PyTorch -- How it works... -- Neural network training -- The SELU activation function -- Softmax activation -- Cross-entropy -- See also -- Modeling with Keras -- Getting ready -- How to do it... -- Data loading and preprocessing Model training -- How it works... -- Maximal information coefficient -- Data generators -- Permutation importance -- See also -- Chapter 2: Advanced Topics in Supervised Machine Learning -- Technical requirements -- Transforming data in scikit-learn -- Getting ready -- How to do it... -- Encoding ranges numerically -- Deriving higher-order features -- Combining transformations -- How it works... -- There's more... -- See also -- Predicting house prices in PyTorch -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Live decisioning customer values Getting ready -- How to do it... -- How it works... -- Active learning -- Hoeffding Tree -- Class weighting -- See also -- Battling algorithmic bias -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Forecasting CO2 time series -- Getting ready -- How to do it... -- Analyzing time series using ARIMA and SARIMA -- How it works... -- There's more... -- See also -- Chapter 3: Patterns, Outliers, and Recommendations -- Clustering market segments -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Discovering anomalies Getting ready -- How to do it... -- How it works... -- k-nearest neighbors -- Isolation forest -- Autoencoder -- See also -- Representing for similarity search -- Getting ready -- How to do it... -- Baseline -- string comparison functions -- Bag-of-characters approach -- Siamese neural network approach -- How it works... -- Recommending products -- Getting ready -- How to do it... -- How it works... -- Precision at k -- Matrix factorization -- The lightfm model -- See also -- Spotting fraudster communities -- Getting ready -- How to do it... -- Creating an adjacency matrix Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Data capture & analysis. bicssc Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Data Processing. bisacsh Computers Neural Networks. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh96008834 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Artificial intelligence with python cookbook : proven recipes for applying ai algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 |
| title_auth | Artificial intelligence with python cookbook : proven recipes for applying ai algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 |
| title_exact_search | Artificial intelligence with python cookbook : proven recipes for applying ai algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 |
| title_full | Artificial intelligence with python cookbook : proven recipes for applying ai algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 |
| title_fullStr | Artificial intelligence with python cookbook : proven recipes for applying ai algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 |
| title_full_unstemmed | Artificial intelligence with python cookbook : proven recipes for applying ai algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 |
| title_short | Artificial intelligence with python cookbook : |
| title_sort | artificial intelligence with python cookbook proven recipes for applying ai algorithms and deep learning techniques using tensorflow 2 x and pytorch 1 6 |
| title_sub | proven recipes for applying ai algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6 |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat Data capture & analysis. bicssc Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Data Processing. bisacsh Computers Neural Networks. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast |
| topic_facet | Machine learning. Artificial intelligence. Python (Computer program language) Artificial Intelligence Machine Learning Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. Data capture & analysis. Neural networks & fuzzy systems. Computers Intelligence (AI) & Semantics. Computers Data Processing. Computers Neural Networks. Artificial intelligence Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2663695 |
| work_keys_str_mv | AT auffarthben artificialintelligencewithpythoncookbookprovenrecipesforapplyingaialgorithmsanddeeplearningtechniquesusingtensorflow2xandpytorch16 |