Tutorial Series
Deep-dive into topics with our multi-part tutorial series
16
Series
78
Episodes
- 1. Smart Factory Fundamentals: How AI Actually Works in Manufacturing
- 2. Computer Vision for Quality Control: Defect Detection Basics
- 3. Predictive Maintenance 101: Using Machine Learning to Prevent Downtime
- 4. Real-Time Anomaly Detection on Production Lines with Deep Learning
- 5. Digital Twin Technology: Creating Virtual Factory Replicas with Python
- 6. Reinforcement Learning for Production Scheduling: Why DQN Failed and How PPO Saved Our Factory Line
- 7. Edge AI vs Cloud AI: Choosing the Right Architecture for Factories
- 8. Time Series Forecasting for Demand Planning: Why Prophet Fails in Factories
- 9. Object Detection and Tracking: Monitoring Assembly Line Workflows
- 10. Sensor Fusion and IoT Integration in Smart Manufacturing
- 11. Explainable AI for Factory Operations: Building Trust in Automated Decisions
- 12. Building an End-to-End Smart Factory AI Pipeline: Case Study and Best Practices
- 1. Getting Started with Quantitative Investment in Python
- 2. Data Collection and Preprocessing for Quant Trading in Python
- 3. Technical Indicators and Feature Engineering with Pandas for Quant Trading
- 4. Backtesting Frameworks: Building Your First Trading Strategy
- 5. Risk Management and Portfolio Optimization Techniques in Python
- 6. Machine Learning Models for Stock Price Prediction: Why Most Fail and What Actually Works
- 7. Pairs Trading Is Dead (Unless You Know Where to Look)
- 8. Real-Time Trading Systems and Deployment Best Practices
- 1. Part 1: The Core of RL: Markov Decision Processes (MDP) Explained
- 2. Part 2: Building Your First Custom Gym Environment using OpenAI Gymnasium
- 3. Part 3: Policy Gradient vs. Q-Learning: Choosing the Right Agent
- 4. Part 4: Stable Baselines3: Practical Tips for Training Robust Agents
- 5. Part 5: Reward Engineering: How to Shape Behaviors in Financial/Robotic Tasks
- 6. Part 6: Beyond Simulation: Addressing the Sim-to-Real Gap
- 1. Navigating the Landscape of Financial Datasets on Kaggle
- 2. Exploratory Data Analysis (EDA) for Stock Price Prediction
- 3. Advanced Feature Engineering for Financial Time-Series
- 4. Building a Credit Risk Scoring Model with Machine Learning
- 5. Detecting Financial Fraud using Anomaly Detection Techniques
- 6. Deep Learning for Algorithmic Trading: From LSTM to Transformers
- 1. Building a US Stock Market Data Pipeline with Python and Yahoo Finance API
- 2. Plotly Defeats Matplotlib for Stock Visualization: Here's the Math
- 3. Sentiment Analysis on Financial News Using NLP for Stock Prediction
- 4. Building a Portfolio Optimization Engine with Modern Portfolio Theory in Python
- 5. Deploying a Real-Time Stock Market Dashboard with FastAPI and WebSockets
- 1. Game AI with Reinforcement Learning: Why RL Beats Traditional Methods
- 2. Q-Learning for Grid Worlds: Building Your First Game AI Agent
- 3. Deep Q-Networks (DQN): Training AI to Play Atari Games
- 4. Policy Gradient Methods: PPO and A3C for Complex Game Environments
- 5. Advanced Game AI: Multi-Agent RL, Curriculum Learning, and Self-Play
- 1. Part 1: Sentiment Analysis of Financial News using FinBERT
- 2. Part 2: Mapping Market Volatility to Global News Headlines
- 3. Part 3: Decoding Central Bank Speeches with NLP (Fed Meetings)
- 4. Part 4: Extracting Alpha Signals from Social Media (Twitter/X)
- 5. Part 5: Automating Earnings Call Summarization with LLMs
- 1. Optimizing Whisper for Mobile: Model Quantization and Compression Techniques
- 2. On-Device Inference: Running Whisper Efficiently with ONNX and Core ML
- 3. Real-time Whisper Is a Battery Nightmare (Here's How to Fix It)
- 4. Whisper Fundamentals: Understanding OpenAI's Speech Recognition Model Architecture
- 1. Building a Condition-Based Maintenance System from Scratch: Sensor Data Collection and Preprocessing
- 2. Feature Engineering for Predictive Maintenance: Extracting Health Indicators from Vibration and Temperature Data
- 3. Training and Evaluating RUL Prediction Models: From Classical ML to LSTM Networks
- 4. Deploying a Real-Time CBM Dashboard: End-to-End Pipeline with Alerts and Visualization
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