Introduction to Deep Learning; Foundations: ML & Neural Networks; Key Architectures & Training; Applications Across Domains; Future Trends & Ethical Considerations

Deep Learning: Foundations & Future

Mathematics

Deep Learning: Foundations &amp; Future

Exploring core concepts, architectures, and applications of modern AI.

Agenda

• Introduction to Deep Learning

• Foundations: ML & Neural Networks

• Key Architectures & Training

• Applications Across Domains

• Future Trends & Ethical Considerations

The Dawn of Deep Learning: A Revolution in AI

Deep learning, a powerful subfield of machine learning, automatically learns complex, hierarchical patterns from vast amounts of data. This capability has driven groundbreaking advancements in areas like computer vision (e.g., image recognition) and natural language processing (e.g., large language models).

AI and art: a luminous brain merging technology and creativity.

Machine Learning: The Foundation

Traditional ML relies on manually engineered features. Paradigms like supervised (linear regression), unsupervised (K-means), and reinforcement learning (Q-learning) identify patterns from this data.

Traditional ML: Manual Feature Engineering

Deep Learning: Automatic Feature Extraction

Traditional ML relies on human experts to hand-craft features from raw data. This labor-intensive process, called feature engineering, directly impacts model performance. For example, manually defining 'edge detectors' for image recognition.

Deep learning models automatically learn hierarchical features directly from raw data. Multi-layered neural networks extract increasingly complex representations. A CNN learns edges, then shapes, then objects without human input.

Mature organoid: layered neurons, complex branching, hierarchical features.

Why 'Deep' Learning? The Power of Layers

• "Deep" refers to many hidden layers in the network.

• Layers learn features hierarchically, from simple to complex.

• Early layers detect basic shapes (edges, corners).

• Later layers combine features for complex objects (faces).

Biological Inspiration: The Neuron

Artificial neural networks are inspired by the human brain's fundamental building block: the neuron. Dendrites receive signals, the soma processes them, and if a threshold is met, the axon transmits an electrical impulse.

Neuron structure: dendrites, cell body, axon, and axon terminal.

Function: Algebra inputs transform into outputs.

Artificial Neuron: The Perceptron Model

• Basic unit of Artificial Neural Networks (ANNs).

• Receives inputs (x) with associated weights (w).

• Calculates weighted sum of inputs plus bias (b).

• Step function outputs 0 or 1 for classification.

Activation Functions: Introducing Non-linearity

Activation functions introduce non-linearity, enabling neural networks to learn complex patterns beyond simple linear models. ReLU is common for hidden layers. Sigmoid and Softmax are used for binary and multi-class classification, respectively.

Neural Network Architecture: From Input to Output

Neural networks process information sequentially. Input layers receive data (e.g., pixels), hidden layers learn features, and output layers produce predictions (e.g., class probabilities). Data flows unidirectionally.

Hierarchical connections in a network, like neural network data flow.

Forward Propagation: Making Predictions

Forward propagation is how a neural network makes a prediction. Input data flows layer by layer, with each neuron calculating a weighted sum and applying an activation function. This process culminates in the final output.

Neural pathways of vision: complex visual system network.

Quadratic functions: maximum and minimum values illustrated.

Loss Functions: Quantifying Error

• Measure prediction error versus actual values.

• Guide network learning to improve accuracy.

• MSE for regression; Cross-Entropy for classification.

• Training aims to minimize this calculated loss.

Backpropagation: The Learning Engine

Backpropagation is the core algorithm for training neural networks. It calculates gradients of the loss function by propagating errors backward, layer by layer, using the chain rule. This adjusts weights and biases for learning.

Gradient Descent: Optimizing Weights

Gradient Descent (e.g., SGD, Adam) iteratively adjusts network weights and biases. It moves opposite to the loss function's gradient, minimizing error. This process optimizes model performance effectively.

Sphere rolls, cube slides down a slope.

Analyzing data and probability forecasting on dual monitors.

Hyperparameters: Guiding the Learning Process

• Learning Rate: Step size for updating model weights.

• Batch Size: Samples processed before one weight update.

• Epochs: Complete passes over the entire training dataset.

• Network Depth: Number of hidden layers; impacts model capacity.

Deep Learning: Foundations & Future

Introduction to Deep Learning; Foundations: ML & Neural Networks; Key Architectures & Training; Applications Across Domains; Future Trends & Ethical Considerations

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