Lynda - Foundations of Programming Code Efficiency (Size: 895.61 MB)
| | 5.Areas of efficiency.mp4 | 60.67 MB |
| | 3.The first rule applied to new code.mp4 | 52.72 MB |
| | 8.Embrace your constraints.mp4 | 41.7 MB |
| | 7.Rule 4 Always look for the easy win.mp4 | 36.64 MB |
| | 1.Clearing up the misconceptions.mp4 | 34.46 MB |
| | 4.Rule 2 You don't know the problem.mp4 | 34.33 MB |
| | 9.Areas not covered.mp4 | 32.2 MB |
| | 2.Rule 1 Code efficiency.mp4 | 27.48 MB |
| | 6.Rule 3 Understand what's under your control.mp4 | 22.45 MB |
| | 3.Detailed monitoring tools Mac.mp4 | 74.36 MB |
| | 6.Web application tools.mp4 | 43.08 MB |
| | 1.Code analysis tools.mp4 | 41.39 MB |
| | 2.General monitoring tools.mp4 | 13 MB |
| | 4.Detailed monitoring tools PC.mp4 | 10.95 MB |
| | 5.Overview of developer-focused tools.mp4 | 10.38 MB |
| | 1.Core concepts of memory usage.mp4 | 44.55 MB |
| | 2.The memory footprint of an application.mp4 | 38.18 MB |
| | 4.Languages and environment issues.mp4 | 19.68 MB |
| | 3.Strategies for memory efficiency.mp4 | 19.59 MB |
| | 1.Introduction to algorithmic efficiency.mp4 | 22.65 MB |
| | 6.Introduction to Big O complexity.mp4 | 21.07 MB |
| | 2.Basic measurement.mp4 | 20.08 MB |
| | 7.What to look for in code.mp4 | 18.99 MB |
| | 5.Example Tracing and profiling in .NET.mp4 | 13.16 MB |
| | 3.Low-tech tracing and profiling.mp4 | 9.85 MB |
| | 4.Example Tracing and profiling in JavaScript.mp4 | 9.09 MB |
| | 4.Network efficiency.mp4 | 23.78 MB |
| | 3.Strategies for disk IO.mp4 | 13.39 MB |
| | 5.Strategies for network efficiency.mp4 | 10.12 MB |
| | 2.Speed expectations.mp4 | 5.27 MB |
| | 1.Disk-based resources.mp4 | 4.05 MB |
| | 6.What about database efficiency.mp4 | 3.36 MB |
| | 1.NextSteps.mp4 | 10.76 MB |
| | 2.What you should know.mp4 | 36.58 MB |
| | 1.welcome.mp4 | 15.62 MB |
Description
Code efficiency. There are other words we can use (optimization, performance, speed), but it's all about making existing code run faster. Whether for desktop, mobile, or web apps, in this course you'll see how to identify pain points and measure them accurately, as well as view multiple approaches to improve the performance. Author Simon Allardice covers everything from "quick fixes" to more complex (but more accurate) algorithms.
Learn to choose the right data types, understand the pitfalls of using high-level languages, and decide where to spend your time. Plus, see how the underlying memory management model may have more of an impact than you realize, and what performance issues you can expect working with databases and web services.
Topics include:
Identifying problems in the code
Embracing constraints
Using code analysis tools to measure performance
Managing memory
Managing disk-based and network resources
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