🧠 Deep Learning Models You Should Know

Deep Learning is a powerful subset of Machine Learning that allows systems to learn complex patterns from data using neural networks.

When I started learning Deep Learning as part of my Data Science journey, I realized that different problems need different neural network architectures.
This blog covers the most important deep learning models, what they are best at, and where they are used in real life.

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1️⃣ Feedforward Neural Networks (FNN)

Feedforward Neural Networks are the simplest form of neural networks.

Information flows in one direction only:
Input → Hidden Layers → Output

There are no loops or memory.

🔹 Where are FNNs used?

• Structured / tabular data

• Classification problems

• Regression problems

🔹 Example:

Predicting house prices based on:

• Area

• Number of rooms

• Location

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2️⃣ Convolutional Neural Networks (CNN)

CNNs are designed to work with images and spatial data.

Instead of looking at the entire image at once, CNNs:

• Extract edges

• Detect shapes

• Identify patterns

This makes them extremely powerful for vision tasks.

🔹 Where are CNNs used?

• Image classification

• Face recognition

• Medical image analysis

• Object detection

🔹 Example:

Detecting whether an image contains a cat or a dog.

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3️⃣ Recurrent Neural Networks (RNN)

RNNs are designed for sequential data — where order matters.

Unlike FNNs, RNNs have a memory that remembers previous inputs.

🔹 Where are RNNs used?

• Time series forecasting

• Text generation

• Speech recognition

🔹 Example:

Predicting tomorrow’s temperature based on previous days.

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4️⃣ Long Short-Term Memory (LSTM)

LSTM is a special type of RNN designed to handle long-term dependencies.

Standard RNNs struggle when sequences are long.
LSTMs solve this using gates:

• Forget gate

• Input gate

• Output gate

🔹 Where are LSTMs used?

• Stock price prediction

• Language modeling

• Machine translation

🔹 Example:

Predicting stock trends using data from the past few months.

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5️⃣ Gated Recurrent Unit (GRU)

GRU is a lighter and faster alternative to LSTM.

It combines gates and reduces complexity while still maintaining good performance.

🔹 Where are GRUs used?

• Real-time NLP applications

• Chat systems

• Speech processing

🔹 Example:

Real-time chatbot response generation.

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6️⃣ Autoencoders

Autoencoders are used for unsupervised learning.

They work in two parts:

• Encoder → compresses data

• Decoder → reconstructs data

The goal is to learn meaningful representations.

🔹 Where are Autoencoders used?

• Anomaly detection

• Noise removal

• Data compression

🔹 Example:

Detecting fraudulent transactions by learning normal behavior.

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7️⃣ Generative Adversarial Networks (GANs)

GANs consist of two neural networks:

• Generator → creates fake data

• Discriminator → checks if data is real or fake

They compete with each other — like a game.

🔹 Where are GANs used?

• Image generation

• Deepfakes

• Art generation

🔹 Example:

Generating realistic human faces that don’t exist.

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8️⃣ Transformer Models

Transformers are the foundation of modern NLP and LLMs.

They rely on:

• Attention mechanism

• Parallel processing

Transformers replaced RNNs for most NLP tasks.

🔹 Where are Transformers used?

• Chatbots (ChatGPT)

• Translation

• Text summarization

🔹 Example:

Answering questions in natural language.

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🧩 Summary Table

Model	Best For
FNN	Tabular data
CNN	Images
RNN	Sequences
LSTM	Long sequences
GRU	Fast sequential tasks
Autoencoder	Anomaly detection
GAN	Data generation
Transformer	NLP & LLMs

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🌱 Final Thoughts

Each deep learning model is designed for a specific type of problem.
Understanding why and when to use each architecture is far more important than memorizing names.

Deep Learning is not magic — it’s structured thinking implemented through neural networks.

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🔗 You can link this blog to:

• Neural Networks Explained

• Transformers Explained

• Chain-of-Thought Reasoning

• Prompt Engineering

🌀 Unsupervised Learning: How Machines Discover Patterns on Their Own

After understanding Supervised Learning (where models learn using labeled data), the next big concept in Machine Learning is Unsupervised Learning.

This time, the story is different — there are no labels, no correct answers, and no teacher guiding the model.

The model is left with raw data and one goal:

👉 Find hidden patterns, groups, or structures automatically.

This capability is what makes unsupervised learning incredibly powerful in exploratory analysis, recommendations, anomaly detection, and customer segmentation.

Let’s break it down in the simplest way possible.

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🌱 What Is Unsupervised Learning? 

Unsupervised learning is a machine learning method where a model learns patterns from unlabeled data.

There is:

• No target variable

• No outputs to predict

• No “right answer” given

The model must discover structure purely from the input data.

Think of it like:
🔍 Exploring a new city without a map
🔍 Finding similarities naturally
🔍 Grouping things based on relationships

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🎯 What Unsupervised Learning Tries to Do

Unsupervised algorithms try to discover:

✔ Patterns
✔ Groups (clusters)
✔ Similarities
✔ Outliers
✔ Structures
✔ Important features
✔ Density regions

Basically, they help us understand data when we don’t know what we are looking for yet.

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🔍 Types of Unsupervised Learning

1️⃣ Clustering (Grouping Similar Items)

The algorithm groups data points based on similarity.

Examples:

• Customer segmentation

• Market segmentation

• Grouping documents

• Image grouping

• Finding similar products

Popular Algorithms :

• K-Means Clustering

• Hierarchical Clustering

• DBSCAN

• Gaussian Mixture Models (GMM)

💡 K-Means groups customers with similar buying patterns.
💡 DBSCAN finds clusters with irregular shapes.

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2️⃣ Dimensionality Reduction

Used when data has too many features.

These algorithms reduce the number of variables while keeping the important information.

Examples:

• Visualizing high-dimensional data

• Noise reduction

• Preprocessing before ML models

• Feature extraction

Popular Algorithms:

• PCA (Principal Component Analysis)

• t-SNE

• UMAP

• Autoencoders

💡 PCA is used heavily for simplifying datasets before training models.

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3️⃣ Association Rule Learning

This finds relationships between items.

Examples:

• Market Basket Analysis

• “People who bought X also bought Y”

• Amazon & Flipkart recommendations

Algorithms:

• Apriori

• ECLAT

• FP-Growth

💡 If a customer buys bread, they often buy butter too.

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4️⃣ Anomaly Detection

Identify unusual or rare patterns.

Examples:

• Fraud detection

• Network intrusion detection

• Detecting manufacturing defects

• Finding abnormal health data

Algorithms:

• Isolation Forest

• One-Class SVM

• Local Outlier Factor (LOF)

💡 Used widely in cybersecurity and banking.

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🧠 How Unsupervised Learning Works (Simple Steps)

Let’s take clustering as an example:

1️⃣ You give the model unlabeled data
2️⃣ It measures similarity between data points
3️⃣ It groups similar points together
4️⃣ It outputs cluster labels (Cluster 1, 2, 3…)
5️⃣ You interpret the pattern

There is no accuracy or F1-score, because there is no ground truth to compare with.

So evaluation is done using:

• Silhouette Score

• Davies-Bouldin Index

• Cluster cohesion metrics

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📘 Real-Life Examples You Already Use

✔ Spotify / YouTube
Clusters songs/videos by listening behavior

✔ Credit Card Fraud Detection
Detects unusual transactions

✔ E-commerce Recommendations
“Similar items” come from clustering

✔ Google Photos
Groups faces using unsupervised learning

✔ Marketing Teams
Segment customers without labels

✔ Healthcare
Cluster patients with similar symptoms

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🧪 Simple Example (Easy to Visualize)

Imagine you have the following data:

Customer	Age	Annual Spend
C1	22	₹25,000
C2	24	₹27,000
C3	46	₹1,20,000
C4	48	₹1,10,000

You run K-Means with k=2.

The model groups:

• Young low-spending customers → Cluster 1

• Older high-spending customers → Cluster 2

No labels needed.
The algorithm automatically discovers these patterns.

🎯 Supervised Learning: How Machines Learn From Labeled Data

In Data Science and Machine Learning, one of the most fundamental concepts you will hear again and again is Supervised Learning.

It’s the foundation behind spam filters, fraud detection, disease prediction, recommendation systems — and almost every ML model you see in real life.

Let’s break it down in the simplest and clearest way possible.

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🌱 What is Supervised Learning? 

Supervised learning is like teaching a child with examples.

You show the model:

• Input → the features

• Output → the correct answer (label)

The model observes thousands of such input–output pairs…
…and learns the relationship between them.

That’s why it’s called supervised — the labels supervise the learning.

✔ Example

Input: photo of a dog
Label: “dog”
→ Model learns to recognize dogs.

Input: customer data
Label: “will churn / will not churn”
→ Model learns to predict customer churn.

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🧠 How Supervised Learning Works 

1️⃣ Collect Labeled Data
Each row must have inputs (X) and output/target (y).
Example:

• X = house size, location, rooms

• y = price

2️⃣ Split Data
Training Set (80%) → model learns
Test Set (20%) → model’s accuracy is evaluated

3️⃣ Choose an Algorithm
Depending on the problem (we’ll see below).

4️⃣ Train the Model
The model tries to map:
Inputs → Output

5️⃣ Evaluate
Using metrics such as accuracy, F1-score, RMSE, etc.

6️⃣ Predict
Once trained, the model predicts labels for new, unseen data.

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🔍 Types of Supervised Learning

Supervised learning has only two main categories:

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1️⃣ Classification — Predicting a Category

The output is discrete (fixed classes).

Examples:

• Spam / Not Spam

• Fraud / Not Fraud

• Disease: Yes / No

• Sentiment: Positive / Negative / Neutral

• Product category

• Loan Approved / Rejected

Common Algorithms:

• Logistic Regression

• Decision Trees

• Random Forest

• Support Vector Machine (SVM)

• Naive Bayes

• K-Nearest Neighbors

• Neural Networks for classification

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2️⃣ Regression — Predicting a Number

The output is continuous.

Examples:

• House price prediction

• Sales forecasting

• Temperature prediction

• Stock price estimation

• Age estimation

Common Algorithms:

• Linear Regression

• Polynomial Regression

• Random Forest Regressor

• Gradient Boosting Regressor

• SVR (Support Vector Regression)

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📘 When to Use Supervised Learning

Use it when:
✔ You have labeled data
✔ You want to predict something specific
✔ You can define clear input and output
✔ Accuracy is measurable

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⚡ Real-Life Use Cases 

• Gmail Spam Detection → Classification

• Netflix Recommendations → Classification

• Credit Risk Scoring → Classification

• Uber Ride Price Prediction → Regression

• Insurance Premium Calculation → Regression

• Medical Diagnosis → Classification

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🧪 A Simple Example 

Imagine you have data:

Size (sq ft)	Bedrooms	Location Score	Price
1000	2	7	₹55L
1500	3	8	₹80L
1800	3	9	₹95L
2200	4	7	₹1.15Cr

Here,

• Features (X): Size, Bedrooms, Location Score

• Target (y): Price

A regression model learns the relationship.
Then, given a new house, it predicts a price.

This is supervised learning in action.

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🌟 Final Thoughts

Supervised learning is the backbone of Machine Learning.
Once you understand:

• what labeled data is

• how models learn patterns

• and the difference between classification & regression

…you unlock the foundation for almost every ML model you will build in the future.

⚙️Oracle Vector Search for AI: Indexes, Embeddings & Semantic Retrieval

Over the past few weeks, I’ve been learning a lot about Retrieval-Augmented Generation (RAG), embeddings, and how modern AI systems actually “retrieve” the right context before answering.
And during this journey — especially while preparing for the Oracle AI Vector Search Professional certification — one thing became very clear:

👉 None of this works without a vector database.

So in this blog, I want to explain vector databases in the simplest way possible, and then show how Oracle AI Vector Search implements them inside Oracle Database — using only verified, official Oracle information.

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🧠 What Are Vector Embeddings?

Vector embeddings are numerical representations of data — text, images, audio, video, code — stored as a list of numbers.

But here’s the key part:

👉 These numbers capture meaning, not just exact words.

Oracle explains it like this:

Vector embeddings describe the semantic meaning behind content such as words, documents, audio, or images.

So embeddings for:

• “doctor” and “hospital”
  are close together.

Embeddings for:

• “apple (fruit)” and “apple (company)”
  are far apart.

This is why semantic search works.

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🔢 How Oracle Stores Embeddings

Oracle Database introduces a special data type called VECTOR, built for storing embeddings efficiently.

Official Oracle documentation confirms:
✔ VECTOR type supports high-dimensional embeddings
✔ Embeddings can also be stored as RAW or BLOB
✔ Oracle applies optimized vector operations like cosine, dot product, and Euclidean distance

This is the foundation of semantic search inside Oracle DB.

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🔍 What Is a Vector Database?

A vector database is simply a system that stores embeddings and allows you to search them by meaning, not by text.

Example:

Query: “How to fix a power supply issue?”

Keyword Search → looks for the exact word “power supply”
Vector Search → finds semantically similar content like ‘battery issue’, ‘adapter failure’, ‘charging error’, etc.

This is why vector search is critical for AI.

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🏦 Oracle AI Vector Search: Vector DB Inside Oracle Database

Unlike many solutions that require a separate vector database, Oracle integrates everything directly inside Oracle Database.

Verified Oracle features include:

✔ Native VECTOR data type

Built specifically to store dense embeddings.

✔ Vector search directly in SQL

Using functions like:

• VECTOR_DISTANCE

• VECTOR_COSINE

• VECTOR_DOT_PRODUCT

✔ Combine semantic search + relational filtering

This is a huge benefit.
Example:

SELECT *
FROM support_docs
WHERE department = 'Hardware'
ORDER BY VECTOR_DISTANCE(embedding, :query_vec)
FETCH FIRST 5 ROWS ONLY;

You can apply SQL filters and semantic search in the same query.

✔ Enterprise security and reliability

Because this runs inside Oracle DB, all enterprise features apply automatically.

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🧱 Vector Indexes 

For fast similarity search, Oracle supports these index types:

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1️⃣ HNSW (Hierarchical Navigable Small World)

Verified in Oracle blogs and docs.

• Graph-based

• Fast and accurate

• Best for large datasets

You will see this used in most high-performance RAG workloads.

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2️⃣ IVF (Inverted File Index)

Also documented by Oracle.

• Clusters vectors into partitions

• Faster lookup

• Good for medium to large datasets

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3️⃣ FLAT (No Index)

Documented in Oracle docs as:

Exact search over all vectors when no index exists.

• 100% accurate

• Slow on big data

• Good for testing or small data

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⚙️ How Oracle Vector Search Fits into RAG

Oracle describes the workflow clearly:

1. Generate embeddings

Using OCI Generative AI / external embedding models.

2. Store embeddings inside Oracle Database

Using VECTOR datatype.

3. Create vector indexes

HNSW or IVF.

4. Run semantic search with SQL

(Vector similarity functions.)

5. Send retrieved context to the LLM

For grounded, factual generation.

This allows Oracle Database to act as a retrieval layer for AI applications.

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🌱 Final Thoughts

Vector databases are the backbone of modern AI applications — from chatbots to search engines to RAG copilots.

And Oracle’s approach is especially powerful because you don’t need a separate DB.
Everything — relational data, business metadata, and AI embeddings — live in the same place.