Image applications in AI often refer to programs that utilize machine learning to perform tasks such as image recognition, image generation, image segmentation, or object detection. These applications can range from facial recognition systems, self-driving cars, medical imaging analysis to artistic style transfer, and more.
Chat applications, on the other hand, often utilize AI in the form of chatbots or virtual assistants. These programs use natural language processing (NLP) and machine learning to understand, respond to, and learn from human language in a valuable and meaningful way.
You will learn about both traditional machine learning as well as deep learning AI techniques. Both can still be extremely effective in today's tasks and have their own pros and cons.
Traditional Methods:
1. Feature Extraction Techniques: You'll learn about methods such as edge detection, corner detection, and color histograms, that can be used to extract informative characteristics from images. These features can then be fed into machine learning models for tasks such as image classification or object detection.
2. Text Preprocessing: You'll learn techniques to clean and process textual data for machine learning, such as tokenization, lemmatization, stop-word removal, and n-grams.
3. Learning Models: You'll get hands-on experience with models like Decision Trees and K-Nearest Neighbors (KNN). These models can be trained to predict outcomes based on the features extracted from the images.
Deep Learning Methods:
1. Convolutional Neural Networks (CNNs). CNNs are a class of deep learning models that are excellent for recognizing patterns in images because they can process data with grid-like topology and are capable of capturing spatial and temporal dependencies.
2. Recurrent Neural Networks and Transformers (ChatGPT)! These are used to understand the context in sentences by taking into account the particular order of words. They are also used to generate responses in chat applications.