Preview: 02 momentum | deep-learning

Deep Learning Fundamentals

5 modules · 23 lessonsPreview

01

Learning Basics

Limitations of Classical Machine LearningFree
Why Neural Networks Exist (Beyond Traditional ML)Free
Non-Linearity: Why Activation Layers MatterSign in
Forward Propagation: How Neural Networks Transform DataSign in
Backpropagation: How Networks Learn from MistakesSign in
Gradient Descent Variants: Batch, Stochastic, and Mini-Batch Optimization MethodsSign in
Epochs, Batches, and Iterations: The Rhythm of Learning
The Training Pipeline: From Input Data to Learning and Evaluation

02

Feature Extraction

From Manual Features to Learned RepresentationsFree
Feature Extraction in Computer VisionSign in
Feature Extraction in Natural Language Processing

03

Generalization

Underfitting and Overfitting: Learning Too Little vs Learning Too MuchFree
Controlling Complexity: L1 and L2 RegularizationSign in
Dropout: Preventing Overfitting Through Randomized Neural NetworksSign in
Early Stopping: Preventing Overtraining

04

Optimization

Optimizers: Understanding the Need for Better OptimizationFree
Momentum: Accelerating Learning in the Right Direction
Adagrad: Adapting Learning to ParametersSign in
RMSProp: Fixing the Learning Rate Decay Problem
Adam: Combining Momentum and Adaptation

05

Stable Learning

Vanishing and Exploding GradientsFree
Weight Initialization: Starting Model Training with Stable Weights and GradientsSign in
Batch Normalization: Stabilizing and Accelerating Learning

Course contentsShowHide

Course overview

Deep Learning Fundamentals

5 modules · 23 lessonsPreview

01

Learning Basics

Limitations of Classical Machine LearningFree
Why Neural Networks Exist (Beyond Traditional ML)Free
Non-Linearity: Why Activation Layers MatterSign in
Forward Propagation: How Neural Networks Transform DataSign in
Backpropagation: How Networks Learn from MistakesSign in
Gradient Descent Variants: Batch, Stochastic, and Mini-Batch Optimization MethodsSign in
Epochs, Batches, and Iterations: The Rhythm of Learning
The Training Pipeline: From Input Data to Learning and Evaluation

02

Feature Extraction

From Manual Features to Learned RepresentationsFree
Feature Extraction in Computer VisionSign in
Feature Extraction in Natural Language Processing

03

Generalization

Underfitting and Overfitting: Learning Too Little vs Learning Too MuchFree
Controlling Complexity: L1 and L2 RegularizationSign in
Dropout: Preventing Overfitting Through Randomized Neural NetworksSign in
Early Stopping: Preventing Overtraining

04

Optimization

Optimizers: Understanding the Need for Better OptimizationFree
Momentum: Accelerating Learning in the Right Direction
Adagrad: Adapting Learning to ParametersSign in
RMSProp: Fixing the Learning Rate Decay Problem
Adam: Combining Momentum and Adaptation

05

Stable Learning

Vanishing and Exploding GradientsFree
Weight Initialization: Starting Model Training with Stable Weights and GradientsSign in
Batch Normalization: Stabilizing and Accelerating Learning

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Course overview

Deep Learning Fundamentals

5 modules · 23 lessonsPreview

01

Learning Basics

Limitations of Classical Machine LearningFree
Why Neural Networks Exist (Beyond Traditional ML)Free
Non-Linearity: Why Activation Layers MatterSign in
Forward Propagation: How Neural Networks Transform DataSign in
Backpropagation: How Networks Learn from MistakesSign in
Gradient Descent Variants: Batch, Stochastic, and Mini-Batch Optimization MethodsSign in
Epochs, Batches, and Iterations: The Rhythm of Learning
The Training Pipeline: From Input Data to Learning and Evaluation

02

Feature Extraction

From Manual Features to Learned RepresentationsFree
Feature Extraction in Computer VisionSign in
Feature Extraction in Natural Language Processing

03

Generalization

Underfitting and Overfitting: Learning Too Little vs Learning Too MuchFree
Controlling Complexity: L1 and L2 RegularizationSign in
Dropout: Preventing Overfitting Through Randomized Neural NetworksSign in
Early Stopping: Preventing Overtraining

04

Optimization

Optimizers: Understanding the Need for Better OptimizationFree
Momentum: Accelerating Learning in the Right Direction
Adagrad: Adapting Learning to ParametersSign in
RMSProp: Fixing the Learning Rate Decay Problem
Adam: Combining Momentum and Adaptation

05

Stable Learning

Vanishing and Exploding GradientsFree
Weight Initialization: Starting Model Training with Stable Weights and GradientsSign in
Batch Normalization: Stabilizing and Accelerating Learning

Course contentsShowHide

Course overview

Deep Learning Fundamentals

5 modules · 23 lessonsPreview

01

Learning Basics

Limitations of Classical Machine LearningFree
Why Neural Networks Exist (Beyond Traditional ML)Free
Non-Linearity: Why Activation Layers MatterSign in
Forward Propagation: How Neural Networks Transform DataSign in
Backpropagation: How Networks Learn from MistakesSign in
Gradient Descent Variants: Batch, Stochastic, and Mini-Batch Optimization MethodsSign in
Epochs, Batches, and Iterations: The Rhythm of Learning
The Training Pipeline: From Input Data to Learning and Evaluation

02

Feature Extraction

From Manual Features to Learned RepresentationsFree
Feature Extraction in Computer VisionSign in
Feature Extraction in Natural Language Processing

03

Generalization

Underfitting and Overfitting: Learning Too Little vs Learning Too MuchFree
Controlling Complexity: L1 and L2 RegularizationSign in
Dropout: Preventing Overfitting Through Randomized Neural NetworksSign in
Early Stopping: Preventing Overtraining

04

Optimization

Optimizers: Understanding the Need for Better OptimizationFree
Momentum: Accelerating Learning in the Right Direction
Adagrad: Adapting Learning to ParametersSign in
RMSProp: Fixing the Learning Rate Decay Problem
Adam: Combining Momentum and Adaptation

05

Stable Learning

Vanishing and Exploding GradientsFree
Weight Initialization: Starting Model Training with Stable Weights and GradientsSign in
Batch Normalization: Stabilizing and Accelerating Learning