Artifical Neural Networks (ANNs)-is a computational model that mimics the human brains information processing through interconnected nodes called neurons

 

Artifical Neural Networks (ANNs)-is a computational model that mimics the human brains information processing through interconnected nodes called neurons

 An Artifical Neural Networks (ANN) is a computational model that mimics the human brains information processing through interconnected nodes called neurons, organized in layers (input, hidden, and output). These self-learning systems are used in machine learning for tasks like pattern recognition, forecasting, and natural language processing by analyzing and adapting to vast amounts of data to make complex predictions and decisions.

Types of ANNs:

1.       Feed Forward Neural Networks (FNNs): The most basic type, where data flows in only one direction from the input layer, through hidden layers, to the output layer.  Use cases: Pattern recognition, image recognition, classification, and regression analysis.

2.       Multilayer receptions (MLPs): A more complex type of feed forward network that adds one ormore hidden layers, between the input and output layers.     

Use cases: Widely use for tasks like prediction and pattern classification.

3.       Recurrent Neural Networks (RNNs): Designed for sequential data, RNNs use loops to pass information from one layer to the next, allowing them to remember past information in sequence.

Ø  Use cases: Natural language processing, time series prediction, and machine translation.

4.       Long Short Term Memory (LSTM): A specific type of RNN that uses “memory cell” to overcome the vanishing gradient problem in traditional RNNs, making them better at handling long term dependencies in data.

Ø  Use cases: Machine translation, sentiment analysis, and text summarization.

5.       Convolution Neural Networks (CNNs): Known for their effectiveness in computer vision, CNNs use specialized “convolution Layers” to process grid like data, such as images.

Ø  Use cases: Image classification, object detection in images, and computer vision.

6.       Generative Adversarial Neural Networks (GANs): These networks consists of two competing neural networks a generator and discriminator that work to create new, realistic data that mimics a given training datasets.

Ø  Use cases: Generating realistic photos, creating new, realistic photos, creating new human poses, and generating images from text descriptions.

    How it works:

·         Architecture: an ANN consists of an input layer (receives data), one or more hidden layers (process information), and output layers (provides the final result).

·          Neurons and connection: Each Neuron is a processing unit that receives inputs from other neurons, multiples them by a specific weight and passes them through a nonlinear activation function.

·         Training: The network learns by adjusting the weights and biases between neurons using a process called training with large data sheets.

·         Back Propagation: This algorithm helps minimize the difference between the networks predicted output and the actual target value by propagating the error backward through the network to update weights and improve accuracy.

·         Prediction: Once trained, the ANN can process new, unseen data to generate predictions or classification based on patterns it has learned.

Applications:

  Image and Speech recognition

  Natural Language Processing

  Financial Forecasting

  Medical diagnosis

  Recommendation systems

                  Characteristics:

  Self learning: ANNs improve their performance as they exposed to more data,.

  Pattern Recognition: They are adept at finding indicate patterns in complex datasets.

  Parallel Processing: ANNs can process large amounts of data concurrently, making them very efficient.

  Generalization: A well trained ANN can apply its learned knowledge to new similar data it has not seen before. 

                Advantages:  

·         Non-linear data processing

·         High Dimensional Data Handling

·         Fault Tolerance

·         Feature Extraction

·         Generalization