Deep Machine Learning in AI - Volume : 2

Deep Machine Learning in AI - Volume : 2

Volume : 2

#Regularization:

#Convolutional neural networks (CNNs) :

#Recurrent neural networks (RNNs) :

#Regularization:

Methods for preventing overfitting, such as dropout, weight decay, and early stopping.

Regularization is a technique used to prevent overfitting in machine learning models. Overfitting occurs when a model is trained too well on the training data, and as a result, it performs poorly on new, unseen data. Regularization helps to prevent overfitting by adding a penalty term to the loss function that the model is trying to optimize.

There are several types of regularization techniques, including:

L1 regularization: also known as Lasso regularization, adds the absolute value of the parameters to the loss function. This results in sparse solutions, where some of the parameters are exactly zero, effectively reducing the number of features.

L2 regularization: also known as Ridge regularization, adds the square of the parameters to the loss function. This results in small, non-zero parameter values, which helps to prevent overfitting.

Dropout: a technique in which a random subset of the neurons are "dropped out" during training, forcing the model to learn multiple, independent representations of the data.

Early stopping: a technique where you monitor the performance of the model on a validation set during training and stop the training when the performance starts to decrease.

Data augmentation: a technique where you artificially increase the size of the training set by applying random transformations to the images like rotation, translation and scaling.

Weight decay: a term added to the objective function which is used in optimization, it is similar to L2 regularization and it's used to shrink the weights of the parameters.

The choice of regularization technique depends on the specific problem and the properties of the data. In practice, a combination of these techniques is often used to achieve the best results.

#Convolutional neural networks (CNNs) :

A type of neural network commonly used for image and video processing tasks, such as object recognition and image segmentation.

Convolutional Neural Networks (CNNs) are a type of neural network that are commonly used for image classification and object recognition tasks. They are called "convolutional" because they use a mathematical operation called convolution to process the input data

CNNs consist of multiple layers, including:

Convolutional layers: these layers perform the convolution operation on the input data, which involves applying a set of filters to the data to extract useful features. Each filter is a small matrix that slides over the input data, and the output of the convolution is a new, filtered version of the input data. The filters are learned during training, and they are designed to detect specific features in the data, such as edges or textures.

Pooling layers: these layers are used to reduce the spatial dimensions of the data, which helps to reduce the computational complexity of the network. The most common pooling operation is max pooling, which selects the maximum value from a small window of the input data.

Fully connected layers: these layers are used to make the final prediction. They are similar to traditional neural networks, and they are connected to all the neurons in the previous layer.

Activation function: it is used to introduce non-linearity into the network. The most common activation functions are ReLU, sigmoid and tanh.

CNNs are known for their ability to learn hierarchies of features, where simple features, such as edges, are learned in the early layers, and more complex features, such as shapes or objects, are learned in the deeper layers. This allows them to achieve state-of-the-art performance on a wide range of image-related tasks, such as object detection, segmentation, and generation.

CNNs are also used in other domains such as natural language processing and speech recognition. The architecture of CNNs can be adapted to different types of data, such as text and audio, by replacing the convolutional layers with other types of layers that are more appropriate for the specific data.

#Recurrent neural networks (RNNs) :

A type of neural network commonly used for sequential data, such as speech, text, and time series data.

Recurrent Neural Networks (RNNs) are a type of neural network that are designed to process sequential data, such as time series, speech, or text. Unlike traditional neural networks, RNNs have a "memory" that allows them to take into account the previous inputs when processing the current input. This memory is implemented in the form of a hidden state, which is passed from one time step to the next.

RNNs consist of multiple layers, including:

Recurrent layers: these layers contain a set of recurrent neurons that are responsible for maintaining the hidden state. At each time step, the recurrent neurons take in the current input and the previous hidden state, and they produce a new hidden state.

Output layers: these layers take the hidden state and produce the final output, which can be a class label, a probability distribution, or a sequence of words.

Activation function: it is used to introduce non-linearity into the network. The most common activation functions are ReLU, sigmoid and tanh.

One of the main challenges with RNNs is the vanishing gradient problem, where the gradients of the parameters tend to become small as the network processes longer sequences. This can make it difficult for the network to learn long-term dependencies in the data. Several solutions have been proposed to address this problem, such as the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) architectures.

LSTM: LSTM networks are a type of RNN that have been designed to overcome the vanishing gradient problem by introducing a set of gates that control the flow of information through the network. The gates can be opened or closed, allowing the network to selectively retain or discard information from the hidden state.

GRU: GRU is a type of RNN that uses a simpler structure than LSTM to control the flow of information. It uses two gates, update and reset gate, to control the information flow.

RNNs are used in a variety of applications, such as natural language processing, speech recognition, and machine translation. They are also used in generative models, such as language models and music generation.

Follow up : Deep Machine Learning in AI - Volume : 3

Follow up : Deep Machine Learning in AI - Volume : 1