TildAlice

Tutorial Series

Deep-dive into topics with our multi-part tutorial series

19 Series
87 Episodes
📚

Smart Factory with AI

AI/Deep Learning 12 episodes
  1. 1. Smart Factory Fundamentals: How AI Works in Manufacturing
  2. 2. Computer Vision for Quality Control: Defect Detection Basics
  3. 3. Predictive Maintenance 101: ML to Prevent Downtime
  4. 4. Real-Time Anomaly Detection on Production Lines with DL
  5. 5. Digital Twin: Virtual Factory Replicas with Python
  6. 6. RL for Production Scheduling: DQN Failed, PPO Saved It
  7. 7. Edge AI vs Cloud AI: Architecture Guide for Factories
  8. 8. Time Series Demand Forecasting: Why Prophet Fails
  9. 9. Object Detection and Tracking: Monitoring Assembly Line Workflows
  10. 10. Sensor Fusion and IoT Integration in Smart Manufacturing
  11. 11. Explainable AI for Factory Operations: Building Trust
  12. 12. Smart Factory AI Pipeline: End-to-End Case Study
📚

Quant Investment with Python

Data Analysis 8 episodes
  1. 1. Getting Started with Quantitative Investment in Python
  2. 2. Data Collection and Preprocessing for Quant Trading in Python
  3. 3. Quant Feature Engineering with Pandas and TA Indicators
  4. 4. Backtesting Frameworks: Building Your First Trading Strategy
  5. 5. Risk Management and Portfolio Optimization Techniques in Python
  6. 6. ML Stock Prediction: Why Most Fail and What Works
  7. 7. Pairs Trading Is Dead (Unless You Know Where to Look)
  8. 8. Real-Time Trading Systems and Deployment Best Practices
🎯

RL Complete Guide

Reinforcement Learning 6 episodes
  1. 1. Part 1: The Core of RL: Markov Decision Processes (MDP) Explained
  2. 2. Building Custom Gym Environments with OpenAI Gymnasium
  3. 3. Part 3: Policy Gradient vs. Q-Learning: Choosing the Right Agent
  4. 4. Stable Baselines3: Tips for Training Robust RL Agents
  5. 5. Reward Engineering for Financial and Robotic RL Tasks
  6. 6. Part 6: Beyond Simulation: Addressing the Sim-to-Real Gap
📊

Kaggle Financial

Data Analysis 6 episodes
  1. 1. Navigating the Landscape of Financial Datasets on Kaggle
  2. 2. Exploratory Data Analysis (EDA) for Stock Price Prediction
  3. 3. Advanced Feature Engineering for Financial Time-Series
  4. 4. Building a Credit Risk Scoring Model with Machine Learning
  5. 5. Detecting Financial Fraud using Anomaly Detection Techniques
  6. 6. Deep Learning for Algorithmic Trading: From LSTM to Transformers
📚

US Stock Market Analyzer

Data Analysis 5 episodes
  1. 1. US Stock Data Pipeline with Python and Yahoo Finance
  2. 2. Plotly vs Matplotlib for Stock Visualization
  3. 3. Financial News Sentiment Analysis for Stock Prediction
  4. 4. Portfolio Optimization with Modern Portfolio Theory
  5. 5. Real-Time Stock Dashboard with FastAPI and WebSockets
📚

Game AI with Reinforcement Learning

Reinforcement Learning 5 episodes
  1. 1. Game AI with RL: Why It Beats Traditional Methods
  2. 2. Q-Learning for Grid Worlds: Building Your First Game AI Agent
  3. 3. Deep Q-Networks (DQN): Training AI to Play Atari Games
  4. 4. PPO and A3C for Complex Game AI Environments
  5. 5. Advanced Game AI: Multi-Agent RL and Self-Play
💹

Financial NLP

AI/Deep Learning 5 episodes
  1. 1. Part 1: Sentiment Analysis of Financial News using FinBERT
  2. 2. Part 2: Mapping Market Volatility to Global News Headlines
  3. 3. Part 3: Decoding Central Bank Speeches with NLP (Fed Meetings)
  4. 4. Part 4: Extracting Alpha Signals from Social Media (Twitter/X)
  5. 5. Part 5: Automating Earnings Call Summarization with LLMs
📊

Data Analysis Foundations

Data Analysis 4 episodes
  1. 1. Interactive Dashboards with Plotly and Streamlit
  2. 2. Part 2: Statistical Rigor: Why Your Correlation Might Be Spurious
  3. 3. Part 3: Handling Imbalanced Data: SMOTE, ADASYN, and Beyond
  4. 4. XAI in Practice: SHAP and LIME for Model Interpretation
📚

Tesla 15-Year Stock Analysis

Data Analysis 4 episodes
  1. 1. Tesla Stock 15-Year Data Analysis: 4 Volatility Regimes
  2. 2. Tesla Stock Key Events 2010-2025: Splits, S&P 500, Musk
  3. 3. Tesla Stock Analysis: Volatility, Distribution, and Outliers
  4. 4. Tesla Stock Prediction: LSTM vs ARIMA (32% Difference)
🥇

Gold Price Forecasting with Data Analysis and Deep Learning

Data Analysis 4 episodes
  1. 1. Gold Price Patterns: 10 Years of Market Data Analysis
  2. 2. ARIMA vs LSTM on Gold Prices: 7-Day vs 30-Day Forecast
  3. 3. LSTM vs Transformer for Gold Forecasting: Who Wins?
  4. 4. Gold Price Prediction: Jupyter Notebook to REST API
📚

CBM Portfolio Project

CBM/PHM 4 episodes
  1. 1. CBM from Scratch: Sensor Data Collection and Preprocessing
  2. 2. Feature Engineering for Predictive Maintenance: Health Indicators
  3. 3. RUL Prediction: Classical ML vs LSTM Training Guide
  4. 4. Real-Time CBM Dashboard: Pipeline with Alerts and Viz
🎙️

Whisper and On-device AI Optimization

AI/Deep Learning 4 episodes
  1. 1. Whisper Model Quantization for Mobile Deployment
  2. 2. On-Device Whisper: ONNX and Core ML Inference Guide
  3. 3. Real-time Whisper Is a Battery Nightmare (Here's How to Fix It)
  4. 4. Whisper Architecture: How OpenAI's Speech Model Works
📉

Stock Price Data Analysis

Data Analysis 3 episodes
  1. 1. yfinance vs pandas-datareader: Python Stock Data in 2026
  2. 2. Technical Indicators in Practice: MA, RSI, and MACD
  3. 3. Stock Time Series: Why ARIMA Failed and LSTM Barely Worked
🐍

Python Decorators

Python Tips 3 episodes
  1. 1. Python Decorators for Beginners: @syntax Explained
  2. 2. Custom Python Decorators: Args, Returns, and Patterns
  3. 3. Advanced Python Decorators: Classes, Stacking, Production
🔧

RUL Prediction

CBM/PHM 3 episodes
  1. 1. PHM Intro: What Is RUL Prediction? Concepts to Data Prep
  2. 2. RUL Prediction in Python: Linear Regression to LSTM
  3. 3. Deploying RUL Models: Optimization and PHM Integration
🤖

Upbit Trading Bot

Python Tips 3 episodes
  1. 1. Building Your First Upbit Trading Bot: Setup and Authentication
  2. 2. Upbit Trading Bot Part 2: Market Analysis and Order Execution
  3. 3. Advanced Trading Bot Features: Risk Management and Backtesting
💻

Claude Code

DevTools & Productivity 3 episodes
  1. 1. Claude Code: AI-Powered Dev in Your Terminal
  2. 2. Advanced Claude Code: Multi-Agent Workflows and Skills
  3. 3. Claude Code in Production: Best Practices and Automation
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Weights & Biases Tutorial

AI/Deep Learning 3 episodes
  1. 1. ML Experiment Tracking with W&B: Lessons from 50 Failed Runs
  2. 2. W&B Sweeps: Bayesian vs Grid Search Benchmark (ResNet-18)
  3. 3. W&B Artifacts and Team Collaboration: Model Version Control
📚

paddleocr initialization time vs easyocr

Computer Vision 2 episodes
  1. 1. PaddleOCR vs EasyOCR Benchmark: Speed, Size, and Accuracy
  2. 2. PaddleOCR Optimization: Caching, Warmup, and Quantization Benchmarks
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