Artificial_Neural_Network_Presentation_With_Images.pptx

Artificial Neural Network
• A Presentation on ANN
• By Mayur Pandey
• B.Tech CSE (CS36) | Roll No: 21
• Babu Banarasi Das University
[Image: A futuristic AI-themed background with a neural network visualization.]

Introduction
• Artificial Neural Networks (ANNs) are
computing systems inspired by biological
neural networks.
[Image: A simple diagram comparing biological and artificial neurons.]

Structure of ANN
• ANNs consist of input layers, hidden layers,
and output layers.
[Image: A labeled diagram showing input, hidden, and output layers of an ANN.]

How ANN Works?
• ANNs process data using weighted
connections and activation functions.
[Image: A flowchart depicting the data flow through an artificial neural network.]

Types of Neural Networks
• Feedforward, Convolutional, Recurrent, and
more.
[Image: A collage of different neural networks like Feedforward, CNN, and RNN.]

Feedforward Neural Network
• Basic type where information moves in one
direction.
[Image: Diagram of a simple feedforward network showing how data moves in one direction.]

Convolutional Neural Network
(CNN)
• Used for image processing and pattern
recognition.
[Image: Image of CNN applied to image recognition (e.g., a CNN analyzing a cat picture).]

Recurrent Neural Network (RNN)
• Suitable for sequential data processing, such
as speech and text.
[Image: Diagram showing how RNN processes sequential data with loops.]

Training a Neural Network
• Involves forward propagation, loss calculation,
backpropagation, and optimization.
[Image: Illustration of forward propagation, loss calculation, and backpropagation.]

Applications of ANN
• Used in image recognition, speech processing,
healthcare, and more.
mage: A collage showing ANN applications in healthcare, finance, robotics, and autonomous vehicles

Advantages of ANN
• Self-learning, adaptability, fault tolerance, and
efficient pattern recognition.
[Image: Infographic highlighting key advantages such as self-learning and adaptability.]

Challenges of ANN
• High computational cost, need for large
datasets, and black-box nature.
[Image: Illustration of challenges like overfitting, data requirements, and black-box nature.]

Future of Neural Networks
• Advancements in deep learning, quantum
computing, and AI ethics.
[Image: A futuristic AI image symbolizing advancements in deep learning and quantum AI.]

Conclusion
• ANNs are a crucial part of AI, with vast
potential and ongoing developments.

References
• List of sources and further reading materials.

Artificial_Neural_Network_Presentation_With_Images.pptx

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