Solving a new problem with AI involves under standing the problem domain, collecting and preprocessing data, choosing the appropriate model and algorithm

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AI is the development of machines that mimic human intelligence. These systems can perform tasks such as learning, reasoning, and problem-solving. AI aims to create machines that can operate autonomously.

There are three main types: Narrow AI, which is task-specific; General AI, a theoretical AI capable of any task; and Super AI, which would surpass human intelligence but remains hypothetical.

AI refers to machines simulating human intelligence to perform tasks autonomously, such as speech recognition, autonomous vehicles, personalized recommendations, and medical diagnostics.

A neural network has three main types of layers: input, hidden, and output. Hidden layers vary in number depending on the model’s complexity. Each layer processes the data differently.

Common data structures include tensors, matrices, and graphs. Tensors are widely used because they store multi-dimensional data, which is essential for training deep learning models.

Forward propagation passes input through the network to get an output. Backpropagation adjusts the weights by calculating the loss gradient, improving model accuracy through iterations.