1. What is a Neural Network

A neural network is a computer system inspired by the human brain. It consists of interconnected nodes (neurons) that work together to process information and make decisions.

Imagine a web of interconnected processing units, mimicking the structure of the human brain. That’s essentially what a neural network is! These networks learn by processing information through layers, just like how our brains learn from experiences.

Internal Functioning

• Input Layer: Receives data like images, text, or numbers.
• Hidden Layers: Process and transform the data, extracting features and patterns. Each layer builds upon the previous one’s understanding.
• Output Layer: Generates a response based on the processed information.
• Neurons: These are the basic units. Each neuron receives input, processes it, and produces an output.
• Layers: Neurons are organized in layers. The input layer receives information, hidden layers process it, and the output layer produces the final result.
• Weights and Bias: Neurons have weights that adjust during learning, affecting the strength of connections. Bias helps in fine-tuning.
• Activation Function: It determines if a neuron should “fire” or not, influencing the output.

 

Think of it like this: You show a neural network pictures of cats and dogs. It analyzes features like fur, whiskers, and ears, building connections in its hidden layers. Eventually, it learns to distinguish cats from dogs and outputs the correct label when shown a new picture.

Applications of Neural Networks:

• Image Recognition: Identifying objects or people in images.
• Speech Recognition: Converting spoken language into text.
• Recommendation Systems: Suggesting products, movies, etc., based on user preferences.
• Medical Diagnosis: Assisting in the analysis of medical images or patient data.

2. Deep Learning

Deep learning is a subset of machine learning. It involves using neural networks with many layers (deep neural networks) to learn and make decisions. Deep learning is a type of neural network with many hidden layers. This allows for complex relationships and patterns to be learned, making it even more powerful than simpler networks.

Think of it like building a deeper well: The more layers you add, the more information the network can access and learn from, leading to more accurate and nuanced results.

Deep Learning Applications:

• Natural Language Processing: Understanding and generating human language.
• Speech recognition: Siri, Alexa, Google Assistant.
• Computer vision: Object detection, image segmentation.
• Recommender systems: Netflix, Amazon recommendations.
• Drug discovery: Predicting molecule properties, designing new drugs.
• Autonomous Vehicles: Helping cars make decisions based on visual and sensor data.
• Gaming: Improving the intelligence of non-player characters in games.

 

See a video on neural networks here:

 

 

3. Generative AI

Generative AI is a type of artificial intelligence that can create new content, like images, music, or text, based on patterns it learned during training. Generative AI uses deep learning to create new content, like text, images, or even music. It can learn the underlying patterns and relationships within existing data to generate something entirely new.

Imagine a paintbrush controlled by a neural network: The network analyzes famous paintings, learns brushstrokes and color palettes, and then creates its own unique artworks.

Applications:

• Art and Design: Generating unique artworks, designs, or styles.
• Content Creation: Producing realistic images, videos, or text based on given input.
• Drug Discovery: Creating molecular structures for potential new drugs.

In summary, neural networks process information like the human brain, deep learning involves complex networks for advanced tasks, and generative AI creates new content based on learned patterns. These technologies find applications in various fields, from image recognition to drug discovery.

See a video on Generative AI here:

 

Published by Active Learning, Dec 2023