Lecture 13 – Deep Learning for Data Mining Foundations, Architectures & Applications

Lecture 13 provides a complete guide to Deep Learning for Data Mining, covering neural network foundations, CNNs, RNNs, LSTMs, Autoencoders, feature learning, backpropagation, training techniques, real-world applications, and case studies.

Deep Learning represents one of the most powerful and transformative advancements in modern Data Mining. Unlike traditional machine learning models that rely heavily on handcrafted features, Deep Learning automatically learns patterns, features, and relationships from raw data. Whether it is images, text, audio, time-series, or sensor logs deep neural networks excel at understanding high-dimensional, complex datasets.

In this lecture, students will understand the architecture, functioning, and practical applications of deep learning models that now power global technology platforms.

Introduction to Deep Learning

Why Deep Learning is Transforming Data Mining

Deep Learning has changed Data Mining because it can:

• Learn features automatically
• Handle massive datasets
• Process raw data directly
• Capture non-linear relationships

Unlike traditional ML (SVM, Decision Trees, Naive Bayes), Deep Learning works exceptionally well for:

• Image mining
• Speech mining
• Text mining
• Video analysis
• IoT sensor data
• High-dimensional data

Traditional ML vs Deep Learning

Feature	Traditional ML	Deep Learning
Feature Extraction	Manual	Automatic
High-Dimensional Data	Poor performance	Excellent
Scalability	Medium	High
Accuracy	Good	Highest
Computation	Less	GPU-intensive

Biological Inspiration

Deep Learning is inspired by the brain.

Artificial Neurons

A neuron receives input, multiplies by weights, adds bias, and passes through activation.

Formula:

y = activation(Wx + b)

Neural Network Layers

Text Diagram:

Input → [Hidden Layer 1] → [Hidden Layer 2] → Output

Types of layers:

• Dense
• Convolutional
• Recurrent
• Pooling
• Normalization

Lecture 12 – Data Warehousing and OLAP for Data Mining

Deep Neural Network Architecture

Input Layer

Receives raw data:

• Pixels
• Words
• Numbers

Hidden Layers

Learn patterns and representations.

Activation Functions

Popular activations:

• ReLU
• Sigmoid
• Tanh
• Softmax

Output Layer

Depends on task:

• Softmax → classification
• Sigmoid → binary classification
• Linear → regression

Gradient Descent & Backpropagation

Loss Functions

Used to measure model error.

Examples:

• Cross-entropy
• MSE

Optimization Algorithms

• Stochastic Gradient Descent (SGD)
• Adam
• RMSProp

Deep Learning Models for Data Mining

1. Multilayer Perceptron (MLP)

Good for:

• Tabular data
• Simple classification
• Regression

2. Convolutional Neural Networks (CNNs)

CNNs use filters to extract patterns.

Used for:

• Image mining
• Video analysis
• Document classification

CNN Diagram:

Image → Convolution → ReLU → Pooling → Dense → Output

3. Recurrent Neural Networks (RNNs)

Good for sequential patterns.

Used for:

• Time-series
• Text sequences
• Speech signals

4. LSTMs & GRUs

Fix RNN limitations by solving vanishing gradients.

Used for:

• Long sequences
• Language modeling
• Medical time-series

5. Autoencoders

Used for:

• Dimensionality reduction
• Anomaly detection
• Noise removal

Diagram:

Input → Encoder → Bottleneck → Decoder → Reconstruction

Feature Learning & Representation Learning

Why Deep Learning Learns Features Automatically

Because layers stack transformations.

Example:
Image classification:

• Layer 1 → edges
• Layer 2 → textures
• Layer 3 → shapes
• Layer 4 → objects

Embeddings

Vector representations for:

• Words
• Items
• Users
• Nodes in graphs

Used in:

• Recommendation systems
• NLP

PyTorch Documentation

Training Deep Learning Models

Data Preparation

Steps:

1. Clean data
2. Normalize
3. Split into training/testing
4. Create batches

Regularization

Prevent model overfitting.

Techniques:

• Dropout
• L2 regularization
• Early stopping

Hyperparameter Tuning

Key hyperparameters:

• Learning rate
• Batch size
• Number of layers
• Number of neurons
• Activation functions

Deep Learning for Large-Scale Data Mining

Big Data + Neural Networks

Large datasets require:

• GPUs
• TPUs
• Distributed training

Distributed Training

Frameworks:

• TensorFlow
• PyTorch
• Horovod
• Ray

Applications in Real-World Data Mining

Image-Based Data Mining

Used for:

• Medical imaging
• OCR
• License plate detection

NLP & Text Mining

Models:

• BERT
• GPT
• LSTM networks

Applications:

• Chatbots
• Search engines
• Sentiment analysis

Time-Series Mining

Used for:

• Stock prediction
• Weather forecasting
• IoT sensor analysis

Fraud & Anomaly Detection

Autoencoders + LSTM detect suspicious patterns.

Recommender Systems

Deep learning enhances collaborative filtering using embeddings.

Case Studies

Tech Industry

Google uses CNNs + Transformers for:

• Vision models
• Search ranking
• Speech recognition

Healthcare

Deep Learning helps in:

• Cancer detection
• Disease prediction
• Patient risk analysis

Finance

Used for:

• Fraud detection
• Stock forecasting
• Credit scoring

Summary

Lecture 13 provided a detailed understanding of Deep Learning for Data Mining. Students learned neural network foundations, CNNs, RNNs, LSTMs, Autoencoders, feature learning, model training, real-world applications, and case studies. Deep learning now drives nearly all modern AI systems and significantly enhances data mining performance.

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