From Neural Networks to Production Deployment: A Systematic Hands-On Machine Learning Learning Path

This article introduces the open-source learning project "ml-learning-lab", a 12-week systematic hands-on machine learning path with the core concept of "learning by building". Through progressive projects, it builds full-stack AI engineering capabilities covering computer vision, medical imaging, security monitoring, and natural language processing—from implementing basic neural networks to deploying production-grade systems—helping learners bridge the gap between theory and practice.

The project's core philosophy is "learning by building", using a progressive structure with clear weekly goals and deliverables. It covers full-stack skills such as data preprocessing, hyperparameter optimization, model interpretability, real-time inference, and Docker deployment. As of the recording date, the project has completed 45 days (26.8% of the total planned 168 days) and produced 5 operational production-grade projects.

Week 1: Manually implement forward/backward propagation and gradient descent, then use PyTorch to implement MNIST handwritten digit recognition (91.28% accuracy). After introducing CNN, the accuracy increases to 98.92%, and ResNet18 transfer learning on CIFAR-10 achieves 95.70% accuracy. Week 2: Explore the YOLOv8 architecture and build a safety equipment detection system (recognizing 6 types of equipment) with an mAP50-95 of 75.1%, suitable for construction site compliance monitoring.

The Week 3 project focuses on chest X-ray pneumonia detection (binary classification), using ResNet50 transfer learning + data augmentation with a validation accuracy of 94.48%. Grad-CAM is introduced to generate heatmaps that visualize the model's decision regions, enhancing the credibility of medical AI. Finally, it is deployed on Streamlit Cloud to form an interactive web application.

Weeks 4-6: Build a real-time security monitoring system integrating object detection (YOLO), multi-object tracking (DeepSORT), and face recognition (FaceNet), which can trigger violation alerts (e.g., not wearing safety equipment). The system achieves a real-time processing speed of 27 FPS on standard hardware, builds an API via FastAPI, and deploys it in a Docker container, demonstrating complete MLOps capabilities.

Week 7: Build an IMDB sentiment analyzer based on a two-layer LSTM, fully practicing the NLP pipeline (text cleaning, tokenization, word embedding). Through hyperparameter tuning (learning rate, hidden layer dimension, etc.), the accuracy increases from 50.61% to 80.38%. Gradient clipping and bidirectional LSTM techniques are applied, laying the foundation for subsequent Transformer learning.

Technology Stack: PyTorch 2.0, Ultralytics YOLOv8, OpenCV, NLTK, Streamlit, FastAPI, Docker, etc. Completed Projects:

Project Name	Model Type	Performance Metric	Core Learning Points
MNIST Baseline	Fully Connected Network	91.28% Accuracy	Training Loop & Optimization
MNIST-CNN	Convolutional Network	98.92% Accuracy	Spatial Feature Learning
CIFAR-10 Classification	ResNet18 Transfer Learning	95.70% Accuracy	Transfer Learning Efficiency
Safety Equipment Detection	YOLOv8	75.1% mAP50-95	Real-Time Object Detection
MediScan	ResNet50+Grad-CAM	94.48% Accuracy	Medical Imaging & Interpretability
AI Security System	YOLO+DeepSORT+FaceNet	27 FPS Real-Time	Multi-Model Integration & Production Deployment
Sentiment Analyzer	Two-Layer LSTM	80.38% Accuracy	Sequence Modeling & Hyperparameter Tuning

Future Plans: Weeks 8-9: Learn Transformer architecture and BERT model; Weeks 10-12: Explore reinforcement learning. Learning Recommendations: Adopt a project-driven, production-oriented, progressive learning approach. Balance algorithm principles and engineering practice, and train comprehensively from basics to integrated deployment to become a full-stack AI engineer.