🧠 Deep Learning & Bio-AI Study Archive

Context: This repository documents my self-directed learning journey in Deep Learning. It contains practical implementations, code exercises, and mini-projects derived from professional certifications, technical literature, and Kaggle competitions.

🎯 Objective

To bridge the gap between Wet-Lab Biology and Computational Intelligence. My goal is to master the implementation of Neural Networks (CNNs, RNNs) and apply them to biological datasets such as genomic sequences and medical imaging.

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📚 Key Curriculum & Projects

1. Deep Learning Specialization (Coursera)

Provider: DeepLearning.AI (Andrew Ng) | Period: Oct 2024 – Dec 2024

This folder contains assignments and personal implementations of core DL concepts.

• Applied Deep Learning: Implemented the architecture of CNNs (for imaging) and RNNs (for sequence data) using TensorFlow to extract insights from large-scale biological data.
• Model Optimization: Executed systematic hyperparameter tuning, regularization (Dropout/Batch Norm), and structured error analysis to improve model performance and generalization.

2. Everyone's Deep Learning (모두의 딥러닝)

Focus: Building the mathematical foundations. Reference: Official Source Code

• Understanding Backpropagation from scratch.
• Basic Keras implementations for regression and classification.

3. Big Data Analysis & AI Development (Winspec/NCS)

Provider: Winspec (NCS Certified) | Period: [Mar 2026 - Mar 2026]

Focus: Comprehensive data science pipeline from data acquisition to ML model deployment.

• Data Science Stack: Mastered Pandas/Numpy for complex data manipulation (preprocessing, cleaning) and Matplotlib/Seaborn for EDA (Exploratory Data Analysis).
• Machine Learning: Applied classical ML algorithms (SVM, Random Forest, XGBoost) using Scikit-learn to solve classification and regression problems.
• Practical Implementation: Conducted end-to-end projects involving web crawling, data visualization, and predictive modeling.

4. Deep Learning for the Life Sciences (O'Reilly)

Focus: Applying deep learning to genomics, microscopy, and drug discovery.

• DeepChem: Utilizing the DeepChem library for molecular property prediction.
• Projects:
  • Molecular toxicity prediction.
  • Protein binding affinity estimation.

5. Kaggle Practicals

Focus: Real-world data handling and competition practice.

• Data Preprocessing: Handling missing values, normalization, and augmentation.
• Projects: [List a specific Kaggle project here, e.g., Titanic or Pneumonia Detection]