AI Tutorial
Neural Networks in Machine Learning & AI (Algorithm, Types, Uses)
Table of Contents
- Introduction
- What are Neural Networks in Machine Learning?
- Key Terms Used in Neural Networks in AI
- Types of Neural Networks
- Uses and Applications of Neural Networks
- How Do Neural Networks Work?
- Neural Network Algorithm
FAQs About Neural Networks
Neural networks learn by adjusting their weights and biases in response to the data they are trained on. This is typically done using a method called backpropagation, combined with an optimization technique like gradient descent.
Deep learning is a subset of machine learning that uses neural networks with multiple layers (deep neural networks) to analyze various factors in large volumes of data. Neural networks can be both shallow (few layers) and deep (many layers).
Neural networks can make predictions or classify data based on their training. However, they don't "decide" in the human sense but rather output results based on their learned patterns.
The amount of data required depends on the complexity of the task. Generally, more data leads to better performance, especially for deep learning models. However, advances like transfer learning can help mitigate the need for large datasets.
Neural networks can be biased if the data they are trained on is biased. It's crucial to use diverse and representative datasets to minimize bias.
They are used in various domains like image and speech recognition, natural language processing, medical diagnosis, financial forecasting, and many others.
Overfitting occurs when a neural network learns the training data too well, including its noise and outliers, leading to poor performance on new, unseen data.
Traditional programming involves explicitly coding instructions. In contrast, neural networks learn to perform tasks by examining and learning from data, making them more adaptable to complex problems where explicit programming is infeasible.
Building a basic neural network has become more accessible thanks to libraries like TensorFlow and PyTorch. However, designing effective neural networks for complex tasks requires expertise in machine learning and data science.
Limitations include their need for large amounts of data, computational intensity, vulnerability to adversarial attacks, and often, their lack of interpretability.
