IPython Interactive Computing and Visualization Cookbook - Second Edition

Focused View

Cyrille Rossant

9:07:50

6 View

1-A Tour of Interactive Computing with Jupyter and IPython

01-Course Overview.mp4

02:18

02-Installation and Setup.mp4

03:18

03-Basics of Jupyter Notebook.mp4

10:02

04-NumPy arrays.mp4

10:42

05-Introduction to Interact feature of IPython.mp4

09:14

06-Use of NumPy Arrays.mp4

13:33

07-Writing Functions.mp4

04:40

08-H5py Files.mp4

09:40

2-Data Visualization

09-Styles of Matplotlib.mp4

08:14

10-Statistical Plots with Seaborn.mp4

07:26

11-Regression Plots.mp4

08:31

12-Bokeh Library.mp4

10:34

13-Interactive Visualization Library.mp4

06:50

3-Statistical Data Analysis

14-Lesson Overview.mp4

00:24

15-Statistical Hypothesis Testing.mp4

08:16

16-Coding Problems.mp4

06:21

17-Exploring a Dataset with Pandas and Matplotlib.mp4

15:46

18-Bayes Theorem and Posterior Probability.mp4

11:38

19-Plotting Distribution using Bayes Theorem.mp4

16:11

20-Chi-Squared Test.mp4

05:23

21-Maximum Likelihood Estimation.mp4

06:15

22-Fitting a Probability Distribution to Data.mp4

18:17

23-Estimating a Probability Distribution.mp4

04:02

4-Machine Learning

24-Lesson Overview.mp4

10:11

25-Cross Validation.mp4

12:19

26-Support Vector Machines.mp4

10:13

27-Linear Regression and Ridge Regression.mp4

11:22

28-Logistic Regression.mp4

12:46

29-K- nearest neighbors classifier.mp4

08:04

30-Naive Bayes Classifier.mp4

07:52

31-Using Support Vector Machines for classification tasks.mp4

09:57

32-Using Random Forest.mp4

05:18

33-Principle Compliant Analysis and Detecting.mp4

04:19

34-Detecting Hidden Structures in a Dataset with Clustering.mp4

01:45

5-Numerical Optimization

35-Lesson Overview.mp4

04:27

36-Finding the Root of a Mathematical Function.mp4

09:54

37-Fitting a function to data with non-linear least squares.mp4

11:17

6-Signal Processing

38-Signal Processing.mp4

07:51

39-Analyzing the signal frequency component with a Fast Fourier Transform.mp4

14:31

40-Applying a Linear Filter to a Digital Signal.mp4

04:29

41-Computing the Auto Co-relation of a Time Series.mp4

13:59

7-Image and Audio Processing

42-Image and Audio Processing.mp4

10:56

43-Edge Detection.mp4

10:04

44-Derivatives.mp4

04:46

45-Sobel Edge Detection.mp4

05:56

46-Erosion and Dilation Intuition.mp4

01:12

47-Manipulating the Exposure and Applying Filters.mp4

13:00

48-Image Segmentation.mp4

10:37

49-Finding Points of Interests in an Image.mp4

04:34

50-Detecting Faces in an Image with OpenCV.mp4

05:28

51-Applying Digital Filters to Speech Sounds.mp4

10:23

8-Deterministic Dynamical Systems

52-Types of Dynamical Systems.mp4

05:51

53-Simulating an Ordinary Differential Equation with SciPy.mp4

12:16

54-Simulating a Partial Differential Equation.mp4

07:55

9-Stochastic Dynamical Systems

55-Simulating a Discrete-time Markov Chain.mp4

11:54

56-Simulating a Poisson Process.mp4

06:27

57-Simulating a Stochastic Differential Equation.mp4

04:40

10-Graphs, Geometry, and Geographic Information Systems

58-Graphs, Geometry and Geographic Information Systems.mp4

04:13

59-Manipulating and Visualizing Graphs with NetworkX.mp4

10:18

60-Resolving Dependencies in a Directed Acyclic Graph with a Topological Sort.mp4

07:57

61-Computing Connected Components in an Image.mp4

08:55

11-Symbolic and Numerical Mathematics

62-Diving into Symbolic Computing with SymPy.mp4

06:45

63-Solving Equations and Inequalities.mp4

08:06

64-Analyzing Real-Valued Functions.mp4

03:23

65-Computing Exact Probabilities and Manipulating Random Variables.mp4

02:40

66-Number Theory with SymPy.mp4

01:35

67-Analyzing a Nonlinear Differential System.mp4

09:50

Description

Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.

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Python

Data Visualization

Cyrille Rossant

Instructor's Courses

Cyrille Rossant, PhD, is a neuroscience researcher and software engineer at University College London. He is a graduate of École Normale Supérieure, Paris, where he studied mathematics and computer science. He has also worked at Princeton University and Collège de France. While working on data science and software engineering projects, he gained experience in numerical computing, parallel computing, and high-performance data visualization. He is the author of Learning IPython for Interactive Computing and Data Visualization, Second Edition, Packt Publishing.

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.