ANNs

An artificial neural network (ANN) is a neural network artificially created by applying the concepts of artificial intelligence, machine learning, and deep learning.

Artificial neural networks

An artificial neural network (ANN) is a computational model that is inspired by the way biological neural networks work. It is composed of a large number of interconnected processing nodes, or neurons, that can communicate with each other.

ANNs are used to recognize patterns, make predictions, and solve problems. They are commonly used in a wide variety of applications, including speech recognition, image classification, and object detection.

Activation functions

In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs.

There are a number of different activation functions that can be used, each with their own advantages and disadvantages. The most common activation functions are linear, step, sigmoid, tanh, and ReLU.

Linear activation functions simply return the input value; they are typically used in regression problems. Step activation functions return either 0 or 1; they are typically used in classification problems with two classes. Sigmoid activation functions return values between 0 and 1; they are typically used in classification problems with more than two classes (i.e., multi-class classification). Tanh activation functions return values between -1 and 1; they are similar to sigmoid activation functions but tend to work better in practice. ReLU activation functions return 0 for all negative input values and the input value for all positive input values; they are typically used in deep neural networks.

There is no right or wrong choice of activation function; it depends on the specific problem you are trying to solve. It is often helpful to try differentactivation functions to see which works best on your data set.

Backpropagation

Backpropagation is a method used to calculate the error gradient in neural networks. The method propagates the error backward from the output layer to the hidden layer and then calculates the weight updates.

CNNs

CNNs or Convolutional Neural Networks are one of the most popular neural networks being used today. They are most commonly used in image recognition and classification.

Convolutional neural networks

Convolutional neural networks (CNNs) are a type of neural network that have proven very effective in areas such as image recognition and classification. CNNs are similar to traditional neural networks in that they are made up of layers of interconnected nodes, or neurons, but they also have a unique layer called a convolutional layer.

The convolutional layer is where the CNN learns to recognize patterns in data, and it is this ability that makes CNNs so effective at image recognition. In a traditional neural network, each node is only connected to a few other nodes in the next layer, but in a CNN, each node is connected to all the nodes in the next layer. This allows the CNN to learn much more complex patterns than a traditional neural network.

Pooling layers

The pooling layer is a method of reducing the dimensionality of data, and it is most commonly used in Convolutional Neural Networks (CNNs). A pooling layer typically follows a convolutional layer in a CNN architecture. It works by partitioning the input data into non-overlapping regions, and then computing a summary statistic (usually the max or average) for each region. This reduces the size of the input data, and therefore also the number of parameters that need to be learned by the model. Pooling also has the effect of making the model invariant to small translations in the input data.

Fully connected layers

Fully connected layers are the most traditional type of neural network layer and the one you are probably most familiar with. A fully connected layer takes all of the inputs from the previous layer, multiplies them by weights, and passes them through an activation function. The output of each neuron in the fully connected layer is then passed to every neuron in the next layer.

RNNs

Recurrent neural networks (RNNs) are a type of neural network where the output from the previous time step is fed as input to the current time step. This makes them ideal for modeling time series data, such as stock prices or weather data. RNNs are also used in natural language processing tasks, such as translating text from one language to another or generating descriptions of images.

Recurrent neural networks

Recurrent neural networks (RNNs) are a type of ANNs used for handling sequential data such as speech, text, time series etc. The RNN model can be unrolled to form a directed graph like the one shown below. The advantage with RNN is that it can preserve the sequence information in the data as compared to feed-forward neural networks (FFNNs). This enables the RNN model to perform well on problems where the order matters.

LSTM

LSTM stands for Long Short-Term Memory. It is a special type of neural network that is designed to model long-term dependencies. LSTMs are very effective at modeling time series data, such as stock prices, temperature changes, etc.

GRU

GRU stands for gated recurrent unit. GRUs are a type of recurrent neural network (RNN) that are designed to better model long-term dependencies in data than traditional RNNs. GRUs have two gates – a reset gate and an update gate. These gates help the model better understand when it should reset its internal state, and when it should update its internal state based on new input data.