^^Machine learning. Percorso di apprendimento.

Machine learning; apprendimento automatico.

 

Installato Python con miniconda

Libri online con jupyter notebook

  1. python nbviewer.jupyter.org/github/jakevdp/ Whirlwind Tour Of Python
  2. machine learning colab.google/ Python Data Science Handbook

settembre2020 ho deciso di seguire Python Data Science Handbook

 

20-9-2020 ho trovato Dive into Deep Learning

An interactive deep learning book with code, math, and discussions.

Provides NumPy/MXNet, PyTorch, and TensorFlow implementations.

20-9-2020 ho trovato      Deep Learning - The Straight Dope gluon.mxnet.io

 

Links librosito

ix Python.

 

Articoli

Elegant Python code for a Markov chain text generator

 

Esprit de l'escaler

parlando di risposte argute a qualcosa detto da qualcuno, che ti vengono in mente sempre troppo tardi.
Descrive esattamente quella condizione esasperante in cui la risposta perfetta e brillante ti viene in mente soltanto quando ormai sei in fondo alle scale e lontano dal tuo interlocutore, per cui è troppo tardi per dirla e ti prenderesti a calci per non averla pensata prima.

Staircase wit   thinking of a clever comeback when it's too late to deliver it.

wp/Esprit_de_l'escalier

 

 

Table of Contents Python Data Science Handbook

Preface

1. IPython: Beyond Normal Python

  1. Help and Documentation in IPython
  2. Keyboard Shortcuts in the IPython Shell
  3. IPython Magic Commands
  4. Input and Output History
  5. IPython and Shell Commands
  6. Errors and Debugging
  7. Profiling and Timing Code
  8. More IPython Resources

2. Introduction to NumPy

  1. Understanding Data Types in Python
  2. The Basics of NumPy Arrays
  3. Computation on NumPy Arrays: Universal Functions
  4. Aggregations: Min, Max, and Everything In Between
  5. Computation on Arrays: Broadcasting
  6. Comparisons, Masks, and Boolean Logic
  7. Fancy Indexing
  8. Sorting Arrays
  9. Structured Data: NumPy's Structured Arrays

3. Data Manipulation with Pandas

  1. Introducing Pandas Objects
  2. Data Indexing and Selection
  3. Operating on Data in Pandas
  4. Handling Missing Data
  5. Hierarchical Indexing
  6. Combining Datasets: Concat and Append
  7. Combining Datasets: Merge and Join
  8. Aggregation and Grouping
  9. Pivot Tables
  10. Vectorized String Operations
  11. Working with Time Series
  12. High-Performance Pandas: eval() and query()
  13. Further Resources

4. Visualization with Matplotlib

  1. Simple Line Plots
  2. Simple Scatter Plots
  3. Visualizing Errors
  4. Density and Contour Plots
  5. Histograms, Binnings, and Density
  6. Customizing Plot Legends
  7. Customizing Colorbars
  8. Multiple Subplots
  9. Text and Annotation
  10. Customizing Ticks
  11. Customizing Matplotlib: Configurations and Stylesheets
  12. Three-Dimensional Plotting in Matplotlib
  13. Geographic Data with Basemap
  14. Visualization with Seaborn
  15. Further Resources

5. Machine Learning

  1. What Is Machine Learning?
  2. Introducing Scikit-Learn
  3. Hyperparameters and Model Validation
  4. Feature Engineering
  5. In Depth: Naive Bayes Classification
  6. In Depth: Linear Regression
  7. In-Depth: Support Vector Machines
  8. In-Depth: Decision Trees and Random Forests
  9. In Depth: Principal Component Analysis
  10. In-Depth: Manifold Learning
  11. In Depth: k-Means Clustering
  12. In Depth: Gaussian Mixture Models
  13. In-Depth: Kernel Density Estimation
  14. Application: A Face Detection Pipeline
  15. Further Machine Learning Resources

Appendix: Figure Code

 

 

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