Neural Networks ECE481 Fall 2024 PDF

Neural Networks ECE481 Dr. Bashir Ghariba Fall, 2024 02/09/2025 Neural Networks 1 02/09/2025 Neural Networks 2  Course Outlines Proposal Lecture 1: Introduction to Neural Networks Lecture 2: Basic Concepts of Neural...

Neural Networks ECE481 Dr. Bashir Ghariba Fall, 2024 02/09/2025 Neural Networks 1 02/09/2025 Neural Networks 2  Course Outlines Proposal Lecture 1: Introduction to Neural Networks Lecture 2: Basic Concepts of Neural Networks Lecture 3: Feedforward Neural Networks Lecture 4: Backpropagation and Training Lecture 5: Advanced Neural Network Architectures Lecture 6: Regularization Techniques Lecture 7: Optimization Algorithms Lecture 8: Transfer Learning and Fine-Tuning Lecture 9: Generative Models Lecture 10: Neural Networks in Natural Language Processing (NLP) Lecture 11: Ethics and Bias in AI Lecture 12: Future Trends in Neural Networks 02/09/2025 Neural Networks 3  Lecture 1: Introduction 02/09/2025 Neural Networks 4 02/09/2025 Neural Networks 5  Basics of Neural Networks Neural networks or artificial neural networks are fundamental tools in machine learning, powering many state-of-the-art algorithms and applications across various domains, including computer vision, natural language processing, robotics, and more. A neural network consists of interconnected nodes, called neurons, organized into layers. Each neuron receives input signals, performs a computation on them using an activation function, and produces an output signal that may be passed to other neurons in the network. Activation function determines the output of a neuron given its input. These functions introduce nonlinearity into the network, enabling it to learn complex patterns in data. 02/09/2025 Neural Networks 6 Cont… A neural network is a computational model inspired by the way biological neural networks in the human brain process information. It consists of interconnected layers of nodes (or neurons), each of which performs simple computations. Here’s a breakdown of its key components: Key Components 1. Neurons: The basic units that receive inputs, process them, and produce outputs. Each neuron applies an activation function to its input to determine its output. 2. Layers: Input Layer: The first layer that receives the input data. Hidden Layers: Intermediate layers where computations take place. A network can have one or more hidden layers. Output Layer: The final layer that produces the result or prediction. 3. Weights and Biases: Each connection between neurons has an associated weight, which adjusts the strength of the signal. Biases allow the model to fit the data better. 4. Activation Functions: Functions that introduce non-linearity into the model, allowing it to learn complex patterns. Common examples include: ReLU (Rectified Linear Unit) Sigmoid Tanh 02/09/2025 Neural Networks 7 Cont.. 5. Loss Function: Measures how well the neural network’s predictions match the actual outcomes. Common loss functions include Mean Squared Error for regression tasks and Cross-Entropy for classification tasks. 6. Optimization Algorithm: Adjusts the weights and biases to minimize the loss function. The most common algorithm is Stochastic Gradient Descent (SGD) and its variants. 02/09/2025 Neural Networks 8  Why Artificial Neural Networks? ANN plays a very important role in the same due to the following reasons: With the help of neural networks, we can find the solution of such problems for which algorithmic method is expensive or does not exist. Neural networks can learn by example, hence we do not need to program it at much extent. Neural networks have the accuracy and significantly fast speed than conventional speed. 02/09/2025 Neural Networks 9 02/09/2025 Neural Networks 10  Areas of Application 1. Computer Vision (CV) Image Classification: Identifying objects within images (e.g., recognizing cats vs. dogs). Object Detection: Locating and classifying multiple objects within an image (e.g., pedestrians in autonomous driving). Image Segmentation: Dividing images into segments for detailed analysis (e.g., medical imaging). 2. Natural Language Processing (NLP) Text Classification Sentiment Analysis Machine Translation 3. Speech Recognition 4. Healthcare 5. Character Recognition 6. Signature Verification Application 7. Human Face Recognition 02/09/2025 Neural Networks 11 02/09/2025 Neural Networks 12 02/09/2025 Neural Networks 13 02/09/2025 Neural Networks 14 02/09/2025 Neural Networks 15 02/09/2025 Neural Networks 16 02/09/2025 Neural Networks 17 02/09/2025 Neural Networks 18 02/09/2025 Neural Networks 19 02/09/2025 Neural Networks 20 02/09/2025 Neural Networks 21 02/09/2025 Neural Networks 22 02/09/2025 Neural Networks 23 02/09/2025 Neural Networks 24 02/09/2025 Neural Networks 25 02/09/2025 Neural Networks 26 02/09/2025 Neural Networks 27 02/09/2025 Neural Networks 28 02/09/2025 Neural Networks 29