AI Without Python - An Intro to Machine Learning for C++ Programmers

Overview

Beginner-friendly introduction to AI and deep neural networks for C++ programmers, exploring alternatives to Python for improved performance in machine learning applications.

Syllabus

• Introduction to AI and Machine Learning
Overview of AI and its applications
Defining machine learning and neural networks
Brief history and evolution of AI technologies
• Setting Up for AI Development in C++
Tools and libraries for C++ in AI (e.g., TensorFlow C++ API, Caffe, Deeplearning4j)
Environment setup: IDEs, compilers, and build systems
Comparing performance: C++ vs Python
• Fundamentals of Machine Learning
Supervised vs. unsupervised learning
Key concepts: datasets, features, and labels
Understanding models and algorithms
• Implementing Basic Machine Learning Models in C++
Linear Regression
Theory and application
Hands-on implementation in C++
Decision Trees
Understanding decision tree algorithms
Build and visualize decision trees
• Deep Learning with C++
Introduction to neural networks
Neurons, layers, and activation functions
Convolutional Neural Networks (CNNs)
Basics of CNNs and their applications
Implement a simple CNN in C++
• Performance Optimization in C++
Understanding the importance of efficiency
Techniques for optimizing C++ ML code
Profiling and tuning for better performance
• Real-world Applications and Case Studies
Case studies of C++ in AI applications
Exploring industry use-cases
Guest speaker session (AI expert using C++)
• Final Project
Develop a machine learning application in C++
Project guidelines and evaluation criteria
Presentation and feedback session
• Conclusion and Further Learning
Recap of key learnings
Resources for advanced topics and continuous learning in AI with C++
Networking and community engagement opportunities

Subjects

Conference Talks