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**Problem Before Neural Networks**
- Unless the specific steps that the computer needs to follows are known the computer cannot solve a problem.
- This restricts the problem solving capability of conventional computers to problems that we already understand and know how to solve.

→ The computer follows a set of instructions in order to solve a problem.

**After Neural Networks**
- With Neural Networks computers can do things that we don't exactly know how to do.

→ Neural Networks learn by examples. They cannot be programmed to perform a specified task.

**Motivation Behind Neural Networks**
→ The building block of a neural net is the neuron. An artificial neuron works much the same way the biological one does.

**What are Artificial Neural Networks ?**
→ Artificial Neural Networks (ANNs) are computing systems inspired by the biological neural networks that constitute animal brains. Such system learn (progressively improve performance) to do tasks by considering examples, generally without task-specific programming.

**How Artificial Neural Network Work?**
→ To get started, I will explain artificial neuron called a perceptron.

**Modes In Perceptron**
- Training Mode :- In the training mode, the neuron can be trained to file (or not), for particular input patterns.

- Using Mode :- In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output.

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