Home Blog Learn

All Courses

Introduction to Deep Learning

Chapter 1: Neural Network Foundations

From Machine Learning to Deep Learning

Biological Inspiration: The Neuron

The Artificial Neuron: A Mathematical Model

The Perceptron: The Simplest Neural Network

Limitations of Single-Layer Perceptrons

Multi-Layer Perceptrons (MLPs): Adding Depth

Hands-on Practical: Building a Simple Perceptron Model

Chapter 2: Activation Functions and Network Architecture

The Role of Activation Functions

Sigmoid Activation

Hyperbolic Tangent (Tanh) Activation

Rectified Linear Unit (ReLU)

Variants of ReLU (Leaky ReLU, PReLU, ELU)

Choosing the Right Activation Function

Understanding Network Layers: Input, Hidden, Output

Designing Feedforward Network Architectures

Hands-on Practical: Implementing Different Activations

Chapter 3: Training Neural Networks: Loss and Optimization

Measuring Performance: Loss Functions

Common Loss Functions for Regression (MSE, MAE)

Common Loss Functions for Classification (Cross-Entropy)

Optimization: Finding the Best Weights

Gradient Descent Algorithm

Stochastic Gradient Descent (SGD)

Challenges with Gradient Descent

Hands-on Practical: Visualizing Gradient Descent

Chapter 4: Backpropagation and Advanced Optimization

Calculating Gradients: The Chain Rule

Computational Graphs

The Backpropagation Algorithm Explained

Forward Pass vs. Backward Pass

Gradient Descent with Momentum

RMSprop Optimizer

Choosing an Optimization Algorithm

Hands-on Practical: Backpropagation Step-by-Step

Chapter 5: Building and Training Deep Neural Networks

Introduction to Deep Learning Frameworks (TensorFlow/Keras, PyTorch)

Setting up the Development Environment

Preparing Data for Neural Networks

Defining a Feedforward Network Model

Weight Initialization Strategies

Compiling the Model: Loss and Optimizer Selection

Training the Model: The fit Method

Monitoring Training Progress (Loss and Metrics)

Evaluating Model Performance

Hands-on Practical: Training a Classifier on MNIST

Chapter 6: Regularization and Improving Performance

The Problem of Overfitting

Regularization Techniques Overview

L1 and L2 Regularization

Dropout Regularization

Batch Normalization

Hyperparameter Tuning Fundamentals

Strategies for Hyperparameter Search (Grid Search, Random Search)

Hands-on Practical: Applying Dropout and Early Stopping

Chapter 7: Introduction to Specialized Architectures

Limitations of Feedforward Networks

Convolutional Neural Networks (CNNs): Motivation

Core CNN Operations: Convolution

Core CNN Operations: Pooling

Typical CNN Architecture

Recurrent Neural Networks (RNNs): Motivation

The Concept of Recurrence and Hidden State

Basic RNN Architecture

Challenges with Simple RNNs (Vanishing/Exploding Gradients)

Conceptual Overview: LSTMs and GRUs

Activation Functions and Network Architecture

In the previous chapter, we established the concept of Multi-Layer Perceptrons (MLPs) as a way to overcome the limitations of single-layer models. This chapter focuses on the internal mechanisms and structural design choices that define how these networks operate and learn.

You will learn about activation functions, the non-linear components within neurons that enable MLPs to model complex relationships. We will cover standard functions such as Sigmoid, Tanh, and ReLU ( $f(x) = \max(0, x)$ ), examining their mathematical properties, benefits, and drawbacks. Understanding these functions is key to controlling the flow of information and gradients within the network.

Additionally, we will examine the structure of feedforward networks, clarifying the roles of input, hidden, and output layers. We will discuss considerations for designing network architectures, such as selecting the number of layers and units per layer, providing a foundation for building effective models. Practical examples will illustrate how to implement and compare different activation functions.

Sections

2.1 The Role of Activation Functions
2.2 Sigmoid Activation
2.3 Hyperbolic Tangent (Tanh) Activation
2.4 Rectified Linear Unit (ReLU)
2.5 Variants of ReLU (Leaky ReLU, PReLU, ELU)
2.6 Choosing the Right Activation Function
2.7 Understanding Network Layers: Input, Hidden, Output
2.8 Designing Feedforward Network Architectures
2.9 Hands-on Practical: Implementing Different Activations

© 2025 ApX Machine Learning

;