A neural network is a computational model inspired by the way biological neural networks in the human brain operate. It is a key component of deep learning, a subset of machine learning. Neural networks consist of interconnected nodes, also known as neurons, organized into layers. The three main types of layers in a neural network are:
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Input Layer: This layer receives the initial input data. Each node in this layer represents a feature or input variable.
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Hidden Layers: These layers, positioned between the input and output layers, contain neurons that process the input data using weights that are adjusted during training. The depth and complexity of the hidden layers contribute to the network's ability to learn intricate patterns and representations.
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Output Layer: This layer produces the final output of the network based on the processed information from the hidden layers. The number of nodes in this layer depends on the nature of the task (e.g., binary classification, multi-class classification, regression).
Neural networks learn from data through a process known as training. During training, the model adjusts its weights iteratively to minimize the difference between predicted outputs and actual targets. This is typically done using optimization algorithms and a defined loss function.
Neural networks are capable of learning complex and non-linear relationships in data, making them powerful tools for tasks such as image recognition, natural language processing, and various types of pattern recognition. Different architectures, such as convolutional neural networks (CNNs) for images or recurrent neural networks (RNNs) for sequential data, are designed to address specific types of problems.
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Goal - Train a neural network model to predict whether a person will have diabetes or not based on different features.
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Dataset - diabetes.csv
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Tool used - Google Colab
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Model Accuracy - 77.5%
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Goal - Train a neural network model to classify text and find out whether a review is positive or negative.
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Dataset - wine-reviews.csv
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Tool used - Google Colab
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Model Accuracy - 84%