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...
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| Main Author | |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
Birmingham :
PACKT Publishing,
2020.
|
| Subjects | |
| Online Access | Full text |
| ISBN | 9781789137965 1789137969 |
| Physical Description | 1 online resource |
Cover
Table of 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