Introduction To Deep Learning (Weeks 11)

This section introduces the fundamentals of deep learning, focusing on neural networks, their limitations, and their advantages over traditional machine learning.

Neural Network Fundamentals

This section introduces Neural Networks, detailing their evolution, foundational concepts, and how they overcome the limitations of traditional machine learning.

Limitations Of Traditional Machine Learning For Complex Data

Traditional machine learning algorithms face significant challenges when dealing with complex, high-dimensional, or unstructured data.

Feature Engineering Burden For Unstructured Data

This section discusses the challenging nature of feature engineering for unstructured data in traditional machine learning.

Scalability To High Dimensions ('curse Of Dimensionality')

The 'Curse of Dimensionality' refers to challenges that arise when analyzing data in high dimensions, particularly how traditional machine learning algorithms struggle to find patterns in sparse data.

Inability To Learn Hierarchical Representations

Traditional machine learning struggles with complex, high-dimensional data due to its flat learning structure, failing to recognize hierarchical relationships.

Handling Of Sequential/temporal Data

This section highlights the challenges traditional machine learning models face when handling sequential or temporal data, emphasizing the necessity for specialized approaches found in deep learning.

Perceptrons To Multi-Layer Perceptrons (Mlps)

This section introduces the evolution of neural networks from Perceptrons to Multi-Layer Perceptrons (MLPs), highlighting their mechanisms and capabilities in dealing with complex data.

The Perceptron: The Simplest Neural Network

The Perceptron is the foundational building block of neural networks, acting as a binary linear classifier that processes inputs through weighted sums and an activation function to produce binary outputs.

Multi-Layer Perceptrons (Mlps): The Foundation Of Deep Learning

Multi-Layer Perceptrons (MLPs) are neural networks comprising multiple layers of interconnected nodes, enabling them to learn intricate relationships in the data and overcome the limitations of single-layer perceptrons.

Activation Functions

Activation functions are crucial components that introduce non-linearity into neural networks, enabling them to learn complex patterns from data.

Forward Propagation & Backpropagation (Intuition)

This section explains the concepts of Forward Propagation and Backpropagation, essential processes in neural networks for making predictions and learning from errors.

Forward Propagation: Making A Prediction

Forward propagation is the process that allows a neural network to make predictions by passing input data through the network's layers.

Backpropagation: Learning From Error

Backpropagation is the algorithm used by neural networks to learn by adjusting weights based on prediction errors.

Optimizers: Guiding The Learning Process

Optimizers are essential algorithms that adjust weights and biases in neural networks to minimize error and facilitate learning during backpropagation.

Gradient Descent: The Fundamental Principle

Gradient Descent is an optimization algorithm used to minimize loss in neural networks by adjusting weights in the direction of the gradient.

Stochastic Gradient Descent (Sgd)

Stochastic Gradient Descent (SGD) is an optimization algorithm that updates weights for each training example, enabling faster convergence in large datasets while addressing local minima more effectively than standard gradient descent.

Adam (Adaptive Moment Estimation)

Adam is a widely used optimizer in deep learning due to its adaptive learning rate capabilities, combining gradients from past weights and their squared averages.

Rmsprop (Root Mean Square Propagation)

RMSprop is an adaptive learning rate optimizer that helps neural networks overcome issues with vanishing and exploding gradients.

Introduction To Tensorflow/keras: Building And Training Simple Mlps

This section introduces TensorFlow and Keras, focusing on their role in building and training Multi-Layer Perceptrons (MLPs).

What Is Tensorflow?

TensorFlow is an open-source platform for machine learning, providing tools and libraries for building and deploying ML applications.

What Is Keras?

Keras is a high-level Neural Networks API designed for fast experimentation with deep learning models.

Building And Training Simple Mlps With Tensorflow/keras

This section outlines how to use TensorFlow and Keras to build and train simple Multi-Layer Perceptrons (MLPs) for deep learning tasks.

Constructing And Training Multi-Layer Perceptrons With Different Activation Functions And Optimizers

Prepare Data For Deep Learning

This section discusses the preparation of data for deep learning, emphasizing the transition from traditional machine learning and highlighting key processes such as feature scaling and splitting datasets.

Construct And Train A Baseline Multi-Layer Perceptron (Mlp)

This section focuses on constructing and training a baseline Multi-Layer Perceptron (MLP) using TensorFlow/Keras, emphasizing the understanding of neural networks and their components.

Experiment With Different Activation Functions

This section explores the vital role of activation functions in neural networks, detailing various types such as Sigmoid, ReLU, and Softmax and their implications for model performance.

Experiment With Different Optimizers

This section covers various optimization algorithms used in neural network training, specifically focusing on Stochastic Gradient Descent (SGD), Adam, and RMSprop.

Visualize Training History And Overfitting

This section focuses on understanding how to visualize the training history of neural networks to identify signs of overfitting.

Final Model Evaluation And Interpretation

This section highlights the essential steps for evaluating and interpreting the performance of deep learning models effectively.