Deep Learning Coursera

This repository contains programming assignments and lecture notes from Andrew Ng's foundational deep learning course specialization on Coursera. The materials cover core neural network training techniques including optimization algorithms, normalization methods, regularization approaches, parameter initialization strategies, and learning rate scheduling to improve model convergence and generalization.

The coursework explores design principles where successive neural network layers learn progressively more abstract feature representations from input data. It provides guidance on selecting open-source, community-driven deep learning frameworks that minimize code complexity and development effort.

The training methodology encompasses comprehensive optimization algorithms, normalization techniques, regularization methods, hyperparameter tuning strategies, data splitting approaches, and augmentation techniques to maximize model performance and generalization. Topics include Adam optimization combining momentum with adaptive learning rates, batch normalization for stabilizing training, L2 regularization for reducing overfitting, and various hyperparameter search strategies such as random search and logarithmic sampling.

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Deep Learning Courses - Programming assignments and lecture notes from Andrew Ng's Coursera deep learning specialization.

Batch Normalization - Normalizes layer activations using mini-batch statistics to stabilize and accelerate neural network training.

Hierarchical Feature Computations - Build deeper layers that compute more complex input features than earlier layers in a neural network.

Feature Scale Normalization - Scale input data to a standard range so the cost function becomes easier and faster to optimize during gradient descent.

Learning Rate Decay Schedules - Reduces the step size during training according to a schedule to improve convergence near the optimum.

Training Hyperparameter Configurations - Set learning rate, number of iterations, layer sizes, and number of layers to control the training process.

Momentum Optimizers - Implements momentum-based optimization algorithms that accelerate gradient descent convergence.

Adam Optimizers - Combine momentum and adaptive learning rates to handle sparse gradients and noisy data effectively.

Overfitting Reduction Techniques - Applies regularization techniques like L2 and dropout to penalize large weights and prevent overfitting.

Training and Testing Splits - Covers dividing datasets into training, development, and test portions for model evaluation.

Mini-Batch Processing - Covers mini-batch gradient descent optimization for processing training data in small groups to speed up iterations.

L2 Regularization - Penalizes large weights by adding a squared norm term to the loss function to reduce overfitting.

Neural Network Initializers - Randomly initializes weight matrices and bias vectors for each layer based on its dimensions.

Training Data Augmentation - Generates additional training examples from existing data to reduce overfitting and improve generalization.

Inference Normalization - Use exponentially weighted averages of mini-batch statistics from training to normalize new examples during inference.

Deep Learning Frameworks - Choose a programming framework that reduces code volume and remains open-source and community-driven.

Weight Initialization Methods - Randomly initialize weight matrices and bias vectors for each layer based on layer dimensions.

Computational Budget Strategies - Choose between babysitting one model or training many in parallel based on available computational power.

Random Hyperparameter Search - Teaches random hyperparameter search strategies for efficient tuning of model parameters.

Exponential Moving Average Weight Updates - Maintains a running average of recent values using a decay factor to smooth noisy sequences.

Value Caching - Store intermediate values from forward propagation and pass them to backward propagation for derivative calculation.

Logarithmic Hyperparameter Sampling - Implements logarithmic sampling strategies for hyperparameters like momentum beta to efficiently explore value ranges.

Impact Prioritization - Focus tuning effort on hyperparameters like learning rate that strongly affect training, while de-emphasizing less critical ones.

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