(Ebook) Learning Julia Build high performance applications for scientific computing 1st Edition by Anshul Joshi, Rahul Lakhanpal ISBN 9781785883279 1785883275

(Ebook) Learning Julia Build high performance applications for scientific computing 1st Edition by Anshul Joshi, Rahul Lakhanpal ISBN 9781785883279 1785883275

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ISBN 10: 1785883275
ISBN 13: 9781785883279
Author: Anshul Joshi, Rahul Lakhanpal

Julia is a highly appropriate language for scientific computing, but it comes with all the required capabilities of a general-purpose language. It allows us to achieve C/Fortran-like performance while maintaining the concise syntax of a scripting language such as Python. It is perfect for building high-performance and concurrent applications. From the basics of its syntax to learning built-in object types, this book covers it all.

This book shows you how to write effective functions, reduce code redundancies, and improve code reuse. It will be helpful for new programmers who are starting out with Julia to explore its wide and ever-growing package ecosystem and also for experienced developers/statisticians/data scientists who want to add Julia to their skill-set.

The book presents the fundamentals of programming in Julia and in-depth informative examples, using a step-by-step approach. You will be taken through concepts and examples such as doing simple mathematical operations, creating loops, metaprogramming, functions, collections, multiple dispatch, and so on.

By the end of the book, you will be able to apply your skills in Julia to create and explore applications of any domain

(Ebook) Learning Julia Build high performance applications for scientific computing 1st Edition Table of contents:

1. Conventions
2. Reader feedback
3. Downloading the example code
4. Downloading the color images of this book
5. Errata
6. Piracy
7. Questions
8. Understanding Julia's Ecosystem
9. What makes Julia unique?
10. Features and advantages of Julia
11. Installing Julia
12. Julia on Ubuntu (Linux)
13. Julia on Fedora/CentOS/Red Hat (Linux)
14. Julia on Windows
15. Julia on Mac
16. Building from source
17. Understanding the directory structure of Julia's source
18. Julia's source stack
19. Julia's importance in data science
20. Benchmarks
21. Using REPL
22. Using help in Julia
23. Plots in REPL
24. Using Jupyter Notebook
25. What is Juno?
26. Package management
27. Pkg.status() – package status
28. Pkg.add() – adding packages
29. Working with unregistered packages
30. Pkg.update() – package update
31. METADATA repository
32. Developing packages
33. Creating a new package
34. A brief about multiple dispatch
35. Methods in multiple dispatch
36. Understanding LLVM and JIT
37. Summary
38. References
39. Programming Concepts with Julia
40. Revisiting programming paradigms
41. Imperative programming paradigm
42. Logical programming paradigm
43. Functional programming paradigm
44. Object-oriented paradigm
45. Starting with Julia REPL
46. Variables in Julia
47. Naming conventions
48. Integers, bits, bytes, and bools
49. Playing with integers in REPL
50. Understanding overflow behavior
51. Understanding the Boolean data type
52. Floating point numbers in Julia
53. Special functions on floating point numbers
54. Operations on floating point numbers
55. Computations with arbitrary precision arithmetic
56. Writing expressions with coefficients
57. Logical and arithmetic operations in Julia
58. Performing arithmetic operations
59. Performing bitwise operations
60. Operators for comparison and updating
61. Precedence of operators
62. Type conversions (numerical)
63. Understanding arrays, matrices, and multidimensional arrays
64. List comprehension in Julia
65. Creating an empty array
66. Operations on arrays
67. Working with matrices
68. Different operation on matrices
69. Working with multidimensional arrays (matrices)
70. Understanding sparse matrices
71. Understanding DataFrames
72. NA data type in DataArray
73. The requirement of the NA data type
74. DataArray – a series-like data structure
75. DataFrames – tabular data structures
76. Summary
77. Functions in Julia
78. Creating functions
79. The special !
80. Function arguments
81. Pass by values versus pass by reference
82. Pass by sharing
83. The return keyword
84. Arguments
85. No arguments
86. Varargs
87. Optional arguments
88. Understanding scope with respect to functions
89. Nested functions
90. Anonymous functions
91. Multiple dispatch
92. Understanding methods
93. Recursion
94. Built-in functions
95. An example using simple built-in functions
96. Summary
97. Understanding Types and Dispatch
98. Julia's type system
99. What are types?
100. Statically-typed versus dynamically-typed languages
101. So, is Julia a dynamically-typed or statically-typed language?
102. Type annotations
103. More on types
104. The Integer type
105. The Float type
106. The Char type
107. The String type
108. The Bool type
109. Type conversions
110. The subtypes and supertypes
111. The supertype() function
112. The subtype() function
113. User-defined and composite data types
114. Composite types
115. Inner constructors
116. Modules and interfaces
117. Including files in modules
118. Module file paths
119. What is module precompilation?
120. Multiple dispatch explained
121. Summary
122. Working with Control Flow
123. Conditional and repeated evaluation
124. Conditional evaluation in detail
125. Short-circuit evaluation
126. Repeated evaluation
127. Defining range
128. Some more examples of the for loop
129. The break and continue
130. Exception handling
131. The throw() function
132. The error() function
133. The try/catch/finally blocks
134. Tasks in Julia
135. Summary
136. Interoperability and Metaprogramming
137. Interacting with operating systems
138. Filesystem operations
139. I/O operations
140. Example
141. Calling C and Python!
142. Calling C from Julia
143. Calling Python from Julia
144. Expressions and macros
145. Macros
146. But why metaprogramming?
147. Built-in macros
148. Type introspection and reflection capabilities
149. Type introspection
150. Reflection capabilities
151. Summary
152. Numerical and Scientific Computation with Julia
153. Working with data
154. Working with text files
155. Working with CSV and delimited file formats
156. Working with DataFrames
157. NA
158. DataArrays
159. DataFrames
160. Linear algebra and differential calculus
161. Linear algebra
162. Differential calculus
163. Statistics
164. Simple statistics
165. Basic statistics using DataFrames
166. Using Pandas
167. Advanced statistics topics
168. Distributions
169. TimeSeries
170. Hypothesis testing
171. Optimization
172. JuMP
173. Convex.jl
174. Summary
175. Data Visualization and Graphics
176. Basic plots
177. Bar graphs
178. Histograms
179. Pie charts
180. Scatter plots
181. 3-D surface plots
182. Vega
183. Area plots
184. Aster plots
185. Choropleth map
186. Heatmaps
187. Ribbon plots
188. Wordcloud
189. Scatter plots
190. Gadfly
191. Interacting with Gadfly using the plot function
192. Plotting DataFrames with Gadfly
193. Summary
194. Connecting with Databases
195. How to connect with databases?
196. Relational databases
197. SQLite
198. MySQL
199. NoSQL databases
200. MongoDB
201. Introduction to REST
202. What is JSON?
203. Web frameworks
204. Summary
205. Julia’s Internals
206. Under the hood
207. Femtolisp
208. The Julia Core API
209. Performance enhancements
210. Global variables
211. Type declarations
212. Fields with abstract types
213. Container fields with abstract type
214. Declaring type for keyword arguments
215. Miscellaneous performance tweaks
216. Standard library
217. LLVM and JIT explained
218. Parallel computing
219. Focusing on global variables
220. Running loops in parallel
221. TCP sockets and servers
222. Sockets
223. Creating packages
224. Guidelines for package naming

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Tags: Anshul Joshi, Rahul Lakhanpal, Learning Julia, high performance, scientific computing