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Ai Systems Engineering I: Data & Numerics (c++)
Published 1/2026
Created by Real AI Engineering,LexpAI Software Technologies Inc.
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 65 Lectures ( 2h 9m ) | Size: 1 GB [/center]
Build the data and numerical foundations you need to implement ML from scratch in C++, fast, stable and scalable
What you'll learn
Implement high-performance data structures that handle massive AI datasets with optimal memory usage
Master numerical computing with deep awareness of precision, stability, and error propagation
Control memory like a pro using modern C++ techniques (RAII, smart pointers) for reliable AI systems
Build robust data pipelines capable of reading, validating, and processing real-world datasets
Optimize code performance through algorithmic analysis and compiler techniques that matter in production
Apply numerical stability concepts to implement algorithms that work reliably in all conditions
Architect modular, maintainable code following industry best practices used by top AI companies
Requirements
Basic C++ knowledge (variables, loops, functions, classes)
Linux - Windows - MacOS
Qt Creator or C++ Compiler already installed
Understanding of basic mathematics (algebra, geometry)Understanding of basic mathematics (algebra, geometry)
Familiarity with command-line development
No prior machine learning experience required
Description
Transform your career into a professional AI Engineer with C++ expertise that powers real-world AI systems.Picture your code streaming millions of data points in milliseconds, running smoothly on edge devices, and scaling effortlessly in the cloud, this is the high-impact skillset companies are urgently hunting for today.This course is your fast track to professional-grade C++ for AI, giving you the precision, speed, and reliability that production environments demand.You will build data structures and numerical algorithms that rival optimized libraries while learning the performance trade-offs that decide whether your AI solution succeeds or stalls in production.We focus on the daily realities of AI engineering: memory-efficient data handling that preserves throughput, floating-point stability that keeps models trustworthy, and optimization techniques that make your code run 10x-100x faster.You will practice translating theory into production-ready code, ensuring your solutions stay robust under real-world constraints like latency, memory limits, and deployment targets.You will gain the confidence to optimize cache usage, minimize allocations, and choose the right container or algorithm for each workload, just like seasoned AI engineers do.You will learn how to diagnose precision pitfalls, prevent catastrophic cancellation, and design numerically stable routines that keep analytics and models accurate at scale.You will see how to structure modular, maintainable C++ that cleanly separates concerns, making your AI pipelines easier to extend, test, and deploy.You will leave with the practical habits and profiling mindset that let you ship faster, fix performance bottlenecks early, and communicate trade-offs clearly to your team and stakeholders.If you are ready to stand out, solve hard performance problems, and get hired to build the AI systems that truly matter, this is your invitation to join.Every minute is designed to move you closer to being the AI engineer who can both architect solutions and deliver them at production speed.
Who this course is for
Developers who know basic C++ and want to become production ready AI engineers
Data/ML engineers needing high-performance C++ for numerics and data pipelines
Systems programmers who want to optimize AI workloads for speed and stability
Students or researchers moving from Python to C++ for performance-critical AI
Ai Systems Engineering I: Data & Numerics (c++)
Published 1/2026
Created by Real AI Engineering,LexpAI Software Technologies Inc.
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 65 Lectures ( 2h 9m ) | Size: 1 GB [/center]
Build the data and numerical foundations you need to implement ML from scratch in C++, fast, stable and scalable
What you'll learn
Implement high-performance data structures that handle massive AI datasets with optimal memory usage
Master numerical computing with deep awareness of precision, stability, and error propagation
Control memory like a pro using modern C++ techniques (RAII, smart pointers) for reliable AI systems
Build robust data pipelines capable of reading, validating, and processing real-world datasets
Optimize code performance through algorithmic analysis and compiler techniques that matter in production
Apply numerical stability concepts to implement algorithms that work reliably in all conditions
Architect modular, maintainable code following industry best practices used by top AI companies
Requirements
Basic C++ knowledge (variables, loops, functions, classes)
Linux - Windows - MacOS
Qt Creator or C++ Compiler already installed
Understanding of basic mathematics (algebra, geometry)Understanding of basic mathematics (algebra, geometry)
Familiarity with command-line development
No prior machine learning experience required
Description
Transform your career into a professional AI Engineer with C++ expertise that powers real-world AI systems.Picture your code streaming millions of data points in milliseconds, running smoothly on edge devices, and scaling effortlessly in the cloud, this is the high-impact skillset companies are urgently hunting for today.This course is your fast track to professional-grade C++ for AI, giving you the precision, speed, and reliability that production environments demand.You will build data structures and numerical algorithms that rival optimized libraries while learning the performance trade-offs that decide whether your AI solution succeeds or stalls in production.We focus on the daily realities of AI engineering: memory-efficient data handling that preserves throughput, floating-point stability that keeps models trustworthy, and optimization techniques that make your code run 10x-100x faster.You will practice translating theory into production-ready code, ensuring your solutions stay robust under real-world constraints like latency, memory limits, and deployment targets.You will gain the confidence to optimize cache usage, minimize allocations, and choose the right container or algorithm for each workload, just like seasoned AI engineers do.You will learn how to diagnose precision pitfalls, prevent catastrophic cancellation, and design numerically stable routines that keep analytics and models accurate at scale.You will see how to structure modular, maintainable C++ that cleanly separates concerns, making your AI pipelines easier to extend, test, and deploy.You will leave with the practical habits and profiling mindset that let you ship faster, fix performance bottlenecks early, and communicate trade-offs clearly to your team and stakeholders.If you are ready to stand out, solve hard performance problems, and get hired to build the AI systems that truly matter, this is your invitation to join.Every minute is designed to move you closer to being the AI engineer who can both architect solutions and deliver them at production speed.
Who this course is for
Developers who know basic C++ and want to become production ready AI engineers
Data/ML engineers needing high-performance C++ for numerics and data pipelines
Systems programmers who want to optimize AI workloads for speed and stability
Students or researchers moving from Python to C++ for performance-critical AI
Cytat:https://rapidgator.net/file/1b8424e39a9a...2.rar.html
https://rapidgator.net/file/11442d735cbb...1.rar.html
https://frdl.io/7klcd841tb58/AI_Systems_...2.rar.html
https://frdl.io/qfwc8vos92g9/AI_Systems_...1.rar.html