Machine Learning

Definition: A subset of AI that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed.
Types:
- Supervised Learning: Algorithm learns from labeled data to make predictions.
- Unsupervised Learning: Algorithm discovers patterns in unlabeled data.
- Reinforcement Learning: Algorithm learns through trial and error by receiving rewards or penalties.
Applications: Image and speech recognition, natural language processing, recommender systems.

Definition: A field of study that focuses on enabling computers to interpret and understand visual information from the world.
Applications:
- Image classification and object detection.
- Image segmentation and tracking.
- Scene understanding and 3D reconstruction.
Techniques:
- Convolutional Neural Networks (CNNs) for image processing.
- Object Detection Architectures (YOLO, SSD, Faster R-CNN).

Definition: A subset of Machine Learning that involves the use of artificial neural networks with multiple layers to learn complex patterns in data.
Types:
- Convolutional Neural Networks (CNNs) for image and signal processing.
- Recurrent Neural Networks (RNNs) for sequential data processing.
- Generative Adversarial Networks (GANs) for generating new data.
Applications: Image and speech recognition, natural language processing, game playing.

Definition: A model inspired by the structure and function of the human brain, composed of interconnected nodes (neurons) that process and transmit information.
Components:
- Input Layer: Receives input data.
- Hidden Layers: Process and transform input data.
- Output Layer: Produces the final output.
Training: Backpropagation algorithm is used to optimize the neural network's performance.

Definition: A field of study focused on the interaction between computers and human language.
Applications:
- Text classification and sentiment analysis.
- Language translation and language generation.
- Speech recognition and dialogue systems.
Techniques:
- Tokenization and stopword removal.
- Named Entity Recognition (NER) and Part-of-Speech (POS) tagging.
- Word embeddings (Word2Vec, GloVe).

Machine learning involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed.
There are three types of machine learning: supervised, unsupervised, and reinforcement learning.
- Supervised learning involves learning from labeled data to make predictions.
- Unsupervised learning involves discovering patterns in unlabeled data.
- Reinforcement learning involves learning through trial and error by receiving rewards or penalties.
Machine learning has various applications, including image and speech recognition, natural language processing, and recommender systems.

Computer vision is a field of study that focuses on enabling computers to interpret and understand visual information from the world.
Applications of computer vision include image classification and object detection, image segmentation and tracking, and scene understanding and 3D reconstruction.
Techniques used in computer vision include convolutional neural networks (CNNs) for image processing and object detection architectures such as YOLO, SSD, and Faster R-CNN.

Deep learning is a subset of machine learning that involves the use of artificial neural networks with multiple layers to learn complex patterns in data.
There are three types of deep learning: convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
Applications of deep learning include image and speech recognition, natural language processing, and game playing.

A neural network is a model inspired by the structure and function of the human brain, composed of interconnected nodes (neurons) that process and transmit information.
A neural network consists of an input layer, hidden layers, and an output layer.
The backpropagation algorithm is used to optimize the neural network's performance during training.

Natural language processing is a field of study focused on the interaction between computers and human language.
Applications of NLP include text classification and sentiment analysis, language translation and language generation, and speech recognition and dialogue systems.
Techniques used in NLP include tokenization and stopword removal, named entity recognition (NER) and part-of-speech (POS) tagging, and word embeddings such as Word2Vec and GloVe.