### Deep Learning vs. Machine Learning

**Machine Learning (ML):**
1. **Definition:** Machine Learning is a subset of artificial intelligence that involves training algorithms to make predictions or decisions without being explicitly programmed.
2. **Data Dependency:** ML algorithms can work with smaller datasets and often require feature extraction by domain experts.
3. **Algorithms:** Includes techniques such as linear regression, decision trees, support vector machines, and k-nearest neighbors.
4. **Interpretability:** ML models are generally more interpretable, meaning the decision-making process can be understood and explained.
5. **Computation:** Requires less computational power compared to deep learning, making it more suitable for simpler applications.

**Deep Learning (DL):**
1. **Definition:** Deep Learning is a subset of machine learning that uses neural networks with many layers (deep neural networks) to analyze various types of data.
2. **Data Dependency:** DL models typically require large amounts of data to perform well and can automatically extract features from raw data.
3. **Algorithms:** Primarily involves neural networks, such as convolutional neural networks (CNNs) for image data and recurrent neural networks (RNNs) for sequential data.
4. **Interpretability:** DL models are often seen as black boxes because their decision-making process is less transparent and harder to interpret.
5. **Computation:** Requires significant computational resources, including GPUs, to handle the complex calculations involved in training deep neural networks.

### Key Differences:
– **Complexity:** Deep learning involves more complex architectures and computations than traditional machine learning.
– **Data Requirements:** Deep learning generally requires more data to achieve high performance, while machine learning can work with smaller datasets.
– **Feature Engineering:** Machine learning often requires manual feature engineering, whereas deep learning automates feature extraction.
– **Applications:** Machine learning is used in applications like recommendation systems and fraud detection, while deep learning excels in tasks such as image and speech recognition.

In summary, while both deep learning and machine learning aim to create models that can learn from data, deep learning is more powerful for handling large, complex datasets and automatically extracting features, at the cost of requiring more data and computational power. Machine learning, on the other hand, is more versatile for a wider range of applications and typically easier to interpret.

Normalization in relational database design is the process of organizing data to reduce redundancy and improve data integrity. It involves structuring a database to ensure data dependencies are logical, thus enhancing efficiency and consistency.

First Normal Form (1NF)

1NF requires that each column contains only atomic values, meaning each cell has a single value, and each record is unique. This simplifies data manipulation and querying.

Second Normal Form (2NF)

2NF builds on 1NF by ensuring that all non-key attributes are fully dependent on the primary key. This eliminates partial dependencies, reducing redundancy by separating data into related tables.

Third Normal Form (3NF)

3NF further refines the structure by ensuring that non-key attributes are dependent only on the primary key and not on other non-key attributes. This removes transitive dependencies, further reducing redundancy and potential anomalies.

Importance

Normalization improves database integrity by organizing data logically, which reduces redundancy and the risk of anomalies during data operations. It enhances efficiency by optimizing the database structure, leading to faster query performance and easier maintenance. Overall, normalization is essential for a clean, efficient, and reliable database system.