---
# System prepended metadata

title: AI - Guild
tags: [AI, ML, DL]

---

# AI - Guild
:::info
[TOC]
:::
![image](https://hackmd.io/_uploads/S1c3a7jvge.png)

<br/>

## What's AI?
**Artificial Intellogence (AI)** is a branch of Computer Science that is concerned with building **smart & intelligent** Machines.

- **Non-Intelligent Machines**: Machines that cannot make decisions or try things on their own. They work only on strict instructions. e.g: watch、bike ...
- **Intelligent Machines**: Like ChatGPT、Google Assistant、autonomous cars... They make people feel as if it is a human being and perform tasks with smarter decisions.

<br/>

### A. Machine Learning
**Machine Learning (ML)** is a technique to implement AI that can learn from experiences (data) by themselves ++without being explicitly programmed++.
- ![Machine-Learning-Techniques](https://hackmd.io/_uploads/rJ0NbNjwge.png)
- **Types of ML**
    | Supervised Learning | Unsupervised Learning | Reinforcement Learning |
    |-|-|-|
    | ML algorithm learns from **Labelled Data**. | ML algorithm learns from **Unabelled Data**. | Iintelligent agents take actions in an environment to **maximize its rewards**. |
    | e.g: Use labelled fruit pictures to train ML to distinguish them. | e.g: Use fruit pictures to train ML to group the pictures itself without prerequisites. | Main Aspects:<br/>1. Environment<br/>2.Agent<br/>3. Action<br/>4. Reward |
    - **Supervised Learning**:
        1. ++Classification++: Predicting a class or **discrete values**.
            - Eg: Male/Female; Apple/Orange; True/False
            a. ==Decision Tree Classification==
            b. ==Random Forest Classification==
            c. ==K-nearest Neighbor==
        2. ++Regression++: Predicting a quantity or **continuous values**.
            - Eg: Salary; Age; Price; Temperature.
            a. ==Logistic Regression==
            b. ==Polynomial Regression==
            c. ==Support Vector Machines==

    > - **Semi-Supervised Learning**
    
    - **Unsupervised Learning**:
        1. ++Clustering++: Grouping the similar data points.
            a. ==K-Means Clustering==
            b. ==Hierarchical Clustering==
        2. ++Association++: Find important relationship between data points.
            a. ==Apriori==
            b. ==Eclat==
        4. ++Dimensionality Reduction++: ==Principal Component Analysis (PCA)==


<br/>

### B. Deep Learning
**Deep Learning (DL)** is a ++subfield of Machine Learning++ that uses **Artificial Neural Networks** to learn hierarchical representations from data (from low-level to high-level features).

- ![image](https://hackmd.io/_uploads/SyPgZ4svel.png)
- **Common Architectures**:
  | Type | Description | Example Use Case |
  |-|-|-|
  | ==**Feedforward Neural Network (FNN)**== | Basic ANN with input → hidden → output layers. | Simple classification tasks. |
  | ==**Convolutional Neural Network (CNN)**== | Extracts spatial features using convolution layers. | Image recognition, object detection. |
  | ==**Recurrent Neural Network (RNN)**== | Processes sequential data, remembers previous states. | Time-series forecasting, language modeling. |
  | ==**Long Short-Term Memory (LSTM)**== | Specialized RNN for long-term dependencies. | Speech recognition, translation. |
  | ==**Generative Adversarial Network (GAN)**== | Two networks compete: generator vs discriminator. | Image generation, data augmentation. |
  | ==**Transformer**== | Attention-based architecture, replaces RNNs in NLP. | Machine translation, Chatbots, LLMs. |
    - **Applications**:
      1. **++Computer Vision (CV)++**: image classification, object detection, medical plots.
      2. **++Natural Language Processing (NLP)++**: sentiment analysis, chatbots/translation.
      3. **++Speech & Audio++**: speech recognition, voice synthesis.
      4. **++Autonomous Systems++**: self-driving cars, robotics.
      5. **++Generative AI++**: image synthesis, text-to-image, music generation.

<br/>

## Conclusion

![image](https://hackmd.io/_uploads/rJkYYSiwxl.png)

<br/>

### 🤖 Machine Learning (ML)

- Features require **manual design and selection**, depending on the expertise of engineers or data scientists.
- **Heavily relies on domain knowledge**.
- Allows **explicit control over the number and type of features**.
- ++Example++: Image recognition, features like colors, shapes must be manually extracted.
- **Advantages**:
    - Transparent feature process with high interpretability.
    - Suitable for small datasets and low computational resources.
- **Disadvantages**:
    - **May miss important or subtle features**.
    - Performance depends on the quality of manually crafted features.

<br/>

### 🧠 Deep Learning (DL)

- Features are **automatically learned and extracted by the model** (end-to-end).
- **Minimal need for manual feature engineering**; just provide raw data.
- Feature dimensionality and complexity are **automatically determined by the NN**.
- ++Example++: CNN automatically learns edges → textures → high-level semantic features.
- **Advantages**:
    - Can directly handle raw inputs such as images, speech, and text.
    - Highly effective, especially for complex tasks and large datasets.
- **Disadvantages**:
    - Training requires more time, **large data**, and **high computational power**.
    - Feature learning is not transparent; **interpretability is low**.

<br/>

### 📝 Tabulate  Comparison
#### A. Definition

| Category | Machine Learning (ML) | Deep Learning (DL) |
|-|-|-|
| Core | Algorithms learn from data to make predictions | Neural networks with multiple layers learn features automatically |
| Relationship | A subset of AI | A subset of ML |
| Data Amount | Less data needed (thousands) | Requires large datasets (millions) |
| Feature Engineering | Manual feature extraction needed | Learns features automatically (e.g., CNN for images) |

<br/>

#### B. Training and Model

| Category | Machine Learning (ML) | Deep Learning (DL) |
|-|-|-|
| Examples | SVM, Decision Trees, KNN, Random Forest | CNN, RNN, Transformer, GAN |
| Training | Usually faster | Slower (needs GPU) |
| Model Depth | Shallow or moderate depth | Deep (nonlinear layers) |
| Interpretability | Easier to explain | Hard to interpret (black box) |
| Complexity | Lower | Higher |

<br/>

#### C. Application Areas

| Use Case | ML Preferred ✅ | DL Preferred ✅ |
|-|-|-|
| Tabular data (finance, health) | ✅ | ⚠️ (mostly overkill) |
| Image/speech recognition | ⚠️ (needs feature design) | ✅ (e.g., CNN, RNN) |
| Natural Language Processing | ⚠️ (BoW, TF-IDF) | ✅ (e.g., BERT, GPT) |
| Small datasets | ✅ | ⚠️ (prone to overfitting) |

:::success
- **ML** is better when data is limited, interpretability matters, or for structured data.
- **DL** excels in complex tasks like vision and NLP, but needs more compute and data.
:::

<br/>

## Related Modules
### 1️⃣ [Python 基礎語法系列](/rirdIkdWRh6Thlx93gJ6YQ)
### 2️⃣ [Python - Numpy](/ckWTq6gBQR6xJovnlF5ZVg)
### 3️⃣ [Python - Pandas](/AhWjfIcIReqHAQcfSe1Qcw)
### 4️⃣ [Python - Pyplot](/Q4zQ9_z1TjuyyvqsEY7p_g)
> - ++**[📊 Python - Plotly Official Website](https://plotly.com/python/)**++
> - ++**[🐟 Python - Seaborn Official Website](https://seaborn.pydata.org/)**++

<!-- 🧩 Python - Scikit-learn
🔬 Python - Scipy
📒 Jupyter Notebook Guild
📦 Python Env Management (conda/venv)
🌐 Streamlit / Dash Guild
 -->
 
<br/>

## Continue Reading
:::info
⏭️ Next Chapter: &nbsp; **[AI - Collecting Data](/Rb_Yvhh0Rdq3SADF6z7APA)**
:::

<br/>

:::spoiler Relevant Resource
[Machine Learning Course With Python](https://www.youtube.com/watch?v=bY__YW-xknU&list=PLfFghEzKVmjsNtIRwErklMAN8nJmebB0I)
[Deep Learning Tutorial Videos](https://www.youtube.com/watch?v=6M5VXKLf4D4&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip)
:::