Deep Learning (DL)
- Inspired by the Human Brain: Deep learning models, particularly deep neural networks, are inspired by how the human brain processes information through neurons and synapses.
- Beats Humans in Some Tasks: Deep learning has surpassed human performance in tasks like image recognition.
- Data-Hungry: DL models require massive datasets to generalize well, which is why they thrive in the era of big data.
Machine Learning (ML)
- Used Everywhere: Machine learning powers applications like sales forecasting systems, recommendation systems (Netflix, Amazon), and even medical diagnosis.
- Self-Improving: ML models improve with more data and better feature engineering, making them dynamic and ever-evolving.
- Not Just Deep Learning: ML includes simpler models like decision trees and linear regression, which sometimes outperform deep learning on small datasets.
Convolutional Neural Networks (CNNs)
- Mimics the Visual Cortex: CNNs process images similarly to how the human brain interprets visual data, using layers to detect edges, textures, and objects.
- Uses Filters/Kernels: Unlike traditional ML models, CNNs use filters to extract features from images, making them efficient for image recognition.
- Key in Deepfake Technology: CNNs are used in generative adversarial networks (GANs) to create realistic deepfake images and videos.
Deep Neural Networks (DNNs)
- More Hidden Layers, More Accuracy: DNNs have multiple hidden layers that enable them to learn complex patterns, but this also increases training time and the need for more computational resources.
- Overfitting Risk: If not properly regularized, DNNs can memorize training data instead of generalizing, leading to poor real-world performance.
- Used in Self-Driving Cars: DNNs help autonomous vehicles recognize road signs, pedestrians, and other vehicles in real-time.
Artificial Neural Networks (ANNs)
- Origin Dates Back to 1943: The concept of ANNs was introduced by Warren McCulloch and Walter Pitts in their paper on neural networks and the human brain.
- Basis of Modern AI: ANNs laid the foundation for deep learning and many AI advancements, including language models like ChatGPT.
- Works with Any Type of Data: Unlike CNNs (which specialize in images), ANNs can process structured (tabular) and unstructured (text, audio) data.
Reinforcement Learning (RL)
- Robots Learn by Trial and Error: RL trains robots to walk, grasp objects, and navigate environments.
- Used in Stock Trading: RL is applied in algorithmic trading to optimize investment strategies.
Transformer Models
- BERT Improves Google Search: Google’s search engine became smarter by understanding context thanks to the BERT model.
- ChatGPT Uses Transformers: ChatGPT and other large language models are built on the transformer architecture.
- Can Write Code, Poems, and Stories: Transformers like GPT-4 generate human-like text, even writing articles, books, and scripts.
Recurrent Neural Networks (RNNs) & Long Short-Term Memory (LSTM)
- Google Translate utilizes RNNs: They help translate languages by considering word order and context.
- LSTMs Fix Memory Issues in RNNs: LSTMs allow AI to remember long-term dependencies, making them ideal for speech recognition.
- Used in Predicting Weather: LSTMs help forecast long-term weather patterns based on past data.
Bayesian Networks
- Used in Medical Diagnosis: They help doctors assess the probability of diseases based on symptoms.
- Powers Intelligent Fraud Detection: Financial institutions use Bayesian Networks to detect fraudulent transactions.
- Can Predict Natural Disasters: Bayesian models analyze seismic data to predict.
The Evolution of AI Models & Genetic Algorithms (GAs)
- Used to Evolve AI Models: Some AI models evolve through genetic algorithms, optimizing themselves over generations.
- NASA Uses Them for Spacecraft Design: NASA has used evolutionary algorithms to design efficient spacecraft antennas.
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