Shortcuts
Please wait while page loads.
LiberoBanner . Default .
PageMenu- Main Menu-
Page content

Catalogue Display

Introduction to computation and programming Using Python: with application to computational modeling and understanding data

Introduction to computation and programming Using Python: with application to computational modeling and understanding data
Share Link
Item Information
Barcode Shelf Location Collection Volume Ref. Branch Status Due Date Res.
10036569 QA76.73.P98.G88 2021
 Computer Science   GUtech Library . . Available .  
10036570 QA76.73.P98.G88 2021
 Computer Science   GUtech Library . . Available .  
10036571 QA76.73.P98.G88 2021
 Computer Science   GUtech Library . . Available .  
Download Title Catalogue Record 16341
Reserve Title Catalogue Record 16341
. Catalogue Record 16341 ItemInfo Beginning of record . Catalogue Record 16341 ItemInfo Top of page .
Catalogue Information
Field name Details
ISBN 9780262542364
Shelf Location QA76.73.P98.G88 2021
Author Guttag, John V.
Title Introduction to computation and programming Using Python : with application to computational modeling and understanding data
3rd ed.
Publisher Masachussetts : MIT Press , 2021
Description xviii, 637 p. ; 25 cm
Contents Intro -- Contents -- Preface -- Acknowledgments -- 1 Getting Started -- 2 Introduction to Python -- 2.1 The Basic Elements of Python -- 2.2 Branching Programs -- 2.3 Strings and Input -- 2.4 Iteration -- 3 Some Simple Numerical Programs -- 3.1 Exhaustive Enumeration -- 3.2 For Loops -- 3.3 Approximate Solutions and Bisection Search -- 3.4 A Few Words About Using Floats -- 3.5 Newton-Raphson -- 4 Functions, Scoping, and Abstraction -- 4.1 Functions and Scoping -- 4.2 Specifications -- 4.3 Recursion -- 4.4 Global Variables -- 4.5 Modules -- 4.6 Files -- 5 Structured Types, Mutability, and Higher-Order Functions -- 5.1 Tuples -- 5.2 Lists and Mutability -- 5.3 Functions as Objects -- 5.4 Strings, Tuples, and Lists -- 5.5 Dictionaries -- 6 Testing and Debugging -- 6.1 Testing -- 6.2 Debugging -- 7 Exceptions and Assertions -- 7.1 Handling Exceptions -- 7.2 Exceptions as a Control Flow Mechanism -- 7.3 Assertions -- 8 Classes and Object-Oriented Programming -- 8.1 Abstract Data Types and Classes -- 8.2 Inheritance -- 8.3 Encapsulation and Information Hiding -- 8.4 Mortgages, an Extended Example -- 9 A Simplistic Introduction to Algorithmic Complexity -- 9.1 Thinking About Computational Complexity -- 9.2 Asymptotic Notation -- 9.3 Some Important Complexity Classes -- 10 Some Simple Algorithms and Data Structures -- 10.1 Search Algorithms -- 10.2 Sorting Algorithms -- 10.3 Hash Tables -- 11 Plotting and More About Classes -- 11.1 Plotting Using PyLab -- 11.2 Plotting Mortgages, an Extended Example -- 12 Stochastic Programs, Probability, and Statistics -- 12.1 Stochastic Programs -- 12.2 Inferential Statistics and Simulation -- 12.3 Distributions -- 12.4 How Often Does the Better Team Win? -- 12.5 Hashing and Collisions -- 13 Random Walks and More About Data Visualization -- 13.1 The Drunkard's Walk -- 13.2 Biased Random Walks -- 13.3 Treacherous Fields.
14 Monte Carlo Simulation -- 14.1 Pascal's Problem -- 14.2 Pass or Don't Pass? -- 14.3 Using Table Lookup to Improve Performance -- 14.4 Finding π -- 14.5 Some Closing Remarks About Simulation Models -- 15 Understanding Experimental Data -- 15.1 The Behavior of Springs -- 15.2 The Behavior of Projectiles -- 15.3 Fitting Exponentially Distributed Data -- 15.4 When Theory Is Missing -- 16 Lies, Damned Lies, and Statistics -- 16.1 Garbage In Garbage Out (GIGO) -- 16.2 Pictures Can Be Deceiving -- 16.3 Cum Hoc Ergo Propter Hoc -- 16.4 Statistical Measures Don't Tell the Whole Story -- 16.5 Sampling Bias -- 16.6 Context Matters -- 16.7 Beware of Extrapolation -- 16.8 The Texas Sharpshooter Fallacy -- 16.9 Percentages Can Confuse -- 16.10 Just Beware -- 17 Knapsack and Graph Optimization Problems -- 17.1 Knapsack Problems -- 17.2 Graph Optimization Problems -- 18 Dynamic Programming -- 18.1 Fibonacci Sequences, Revisited -- 18.2 Dynamic Programming and the 0/1 Knapsack Problem -- 18.3 Dynamic Programming and Divide-and-Conquer -- 19 A Quick Look at Machine Learning -- 19.1 Feature Vectors -- 19.2 Distance Metrics -- 19.3 Clustering -- 19.4 Types Example and Cluster -- 19.5 K-means Clustering -- 19.6 A Contrived Example -- 19.7 A Less Contrived Example -- 19.8 Wrapping Up -- Python 2.7 Quick Reference -- Index.
Subject Python (Computer program language)-Textbooks
Links to Related Works
Subject References:
Authors:
.
Enriched Content Catalogue Record 16341
.
ISBD Display Catalogue Record 16341
.
Tag Display Catalogue Record 16341
.
Related Works Catalogue Record 16341
.
Marc XML Catalogue Record 16341
.
Add Title to Basket Catalogue Record 16341
.
Catalogue Information 16341 Beginning of record . Catalogue Information 16341 Top of page .
Most Read Titles
# Author Title
1 . Weinschenk, Susan 1 . 100 things every designer needs to know about people 1
2 . Murdock, Kelly 2 . 3ds max 9. 2
3 . Anderson, Andy 3 . Brilliant Photoshop CS3: What you need to know and how to do it 3
4 . Kurose, James F. 4 . Computer Networking: A top-down approach. 4
5 . Freeman, Eric 5 . Head first design patterns. A brain-friendly guide. 5
.
As well of interest
# Author Title
1 . Connolly, Thomas 1 . Database Systems. A Practical Approach to Design, Implementation and Management. 1
2 . Barakat, Nahla H. 2 . Intelligible support vector machines for diagnosis of diabetes mellitus. 2
3 . Stroustrup, Bjarne 3 . The C++ programming language 3
4 . Al Babele, Omar Mohammed 4 . Skin cancer classification and analyis using ResNet in comparison with EfficientNet 4
.

Reviews


This item has not been rated.    Add a Review and/or Rating16341