Deep Learning Algorithms in PythonWhat is Deep Learning?Deep Learning is a sub-branch of artificial intelligence (AI) used to teach and process data based on the human brain. Deep Learning models recognise complex patterns in pictures, text, sounds, and other data formats to give efficient results and accuracy. Deep learning is used to automate human intelligence-based tasks. Different tasks can be used with the help of deep learning, such as speech-to-text converters, image recognition, visual recognition, fraud detection, self-driving cars, etc. Deep learning is based on artificial neural networks. It is used to learn different patterns and relationships between different features in the data. It is built similarly to the human brain structure, consisting of different layers of nodes used to process and transform the data. Deep learning models have different layers, like the input layer, various interconnected hidden layers, and output layers. Every single layer is connected to each other and contains signals or neurons from the previous layer as an input. The output of each layer works as an input for the next layer till we get the output from the final output layer. Deep learning is expanding its significance in almost every branch, like computer vision, reinforcement learning, natural language processing, and many more. Deep learning can be used with supervised, unsupervised, and reinforcement machine learning models. Different layers in deep learning models:
Why Deep Learning?Deep Learning uses a lot of data, including labelled and unlabelled data. Deep Learning Algorithms reduce data pre-processing, which is involved in machine learning. These algorithms can also process unstructured data, like text and images. Deep learning uses supervised, unsupervised, and reinforcement learning. Deep learning can be used in extracting features without any intervention from humans. In comparison to traditional machine learning models, deep learning takes less human intervention, giving more accuracy. It is more volatile than machine learning models and can sort data sets with more variations. Different Algorithms in Deep LearningDeep learning uses different algorithms for processing the data. This includes:
Convolutional Neural Networks (CNNs):A Convolutional Neural Network is a deep learning model that extracts features from a grid-like matrix dataset. The data is processed by breaking the images into different tiles or frames overlapped with each other rather than feeding the entire image to the model. Different layers are involved in CNN, including convolutional, ReLU, Pooling, and Fully Connected layers. Convolutional Layer: This layer is the building block of the deep learning model. A filter of fixed size (in the shape of a square) is used, which performs the operation of convolution. It has weighted inputs, and when applied to the input image, it produces outputs. The output of one filter acts as an input for the other layer. When the filter tile moves to the entire image, it activates the neurons and gives the output in a feature map. Rectified Linear Unit Layer (ReLU): The convolutional neural network has a ReLU layer that produces feature maps from the convolutional layer and is passed into an activation function. The activation function will decide which neuron will be activated or not. It is used to increase the nonlinearity of the data set. Pooling Layers: The output produced from the convolutional layer forms an array, which is huge in size. Thus, to reduce its size, we use max pooling for the down sampling of the array. It will keep the relevant features and discard the others from the array. Fully connected layer: This layer is used to classify and identify the images. It is formed when the flattened matrix (the reduced dimension matrix) obtained from the pooling layer is fed as the input in the network. Applications of Convolutional Neural Network Convolutional Neural Networks can be used in various tasks like facial recognition, advertisements, climate analysis, social media analysis, etc. Moreover, it is used for driverless cars, natural language processing, etc. Benefits of CNN CNN gives high accuracy, more precision, and better results than other algorithms, especially for image and object recognition. Drawbacks of CNN CNN needs a high cost for the implementation, as it needs high computation power to train the data. Recurrent Neural Networks (RNNs)Recurrent Neural Network is an artificial neural network that uses sequential or time series data for various tasks, like natural language processing, text generation, speech recognition, etc. The directed cycles in the Recurrent neural network among the interconnected nodes. Recurrent Neural Networks uses previous inputs to gather information and process further sequences. Like any other algorithm in deep learning, it has different layers, including an input layer, hidden layers, and an output layer. Working of Recurrent Neural Network The Recurrent Neural Network consists of multiple activation functions, fixed for each step in the working. It uses backpropagation to train the data. The hidden states of the network take the previous data and use it for further layers. It produces an output, copies it, and reverts to the neural network again to process more for the next layers. The hidden states get updated every time the network learns from the past data. Applications of Recurrent Neural Network Recurrent Neural Networks can be used for various complex tasks like time series forecasting, speech recognition, machine translation, image and face recognition, etc. Benefits of Recurrent Neural Network The recurrent neural network uses previous data for the processing. It captures all the information throughout the process. Its ability to recognise the previous inputs all the time makes it more efficient for time series forecasting. It can also be used with convolutional neural networks to make it more efficient. Drawbacks of Recurrent Neural Networks Recurrent Neural Networks face challenges while training the data. RNN cannot process long sequences with ReLU or tanh activation function. AutoencodersAutoencoders are a type of feedforward unsupervised algorithm in which both input and output are identical. This algorithm is similar to Principal component Analysis (PCA) in machine learning. Autoencoders are used for the conversion of multi-dimensional data to low-dimensional data. The autoencoders are trained in such a manner that they duplicate the data of the input layer to the output layer. Working of Autoencoders Autoencoders have three main components: encoder, decoder, and the code.
The code layer is also known as the bottleneck. The autoencoder algorithm is built, which helps to analyse the observed data and relevant information and decide which part needs to be discarded. The bottleneck layer remembers and recognises the input data after passing through the network. Applications of Autoencoders The autoencoders are used for image compression, colouring, denoising images, etc. It can also be used in detecting diseases like breast cancer. It is also used in healthcare, which uses imaging technology (imaging the human body's interior). Benefits of Autoencoders Using autoencoder deducts the computational cost of the functions. It is easy to manage the autoencoders, making it a cheaper algorithm. Limitations of Autoencoders There are chances that the data can be lost from the original input image after encoding. This algorithm is not very efficient for reconstructing complex images. Long Short Term Memory Networks (LSTMs)Long Short Term Memory Networks (LSTMs) are a kind of Recurrent Neural Networks that recognise and store long-term dependencies. As its name suggests, it recalls the information for long periods. LSTM can be used for time series forecasting as it can recall the memory for a longer period. This algorithm has a chain-like structure with four different layers used for communication. LSTMs are also used in speech recognition, music composition, and many more. Working of Long Short Term Memory Networks (LSTMs) The LSTM contains several memory blocks named cells. The cell layer shifts itself to the next cell. LSTM decides which information it will keep and which it will discard. It replaces irrelevant information. It simultaneously updates the cell values. Then, produces the output. Application of LSTM LSTM network is used to detect anomalies in huge network traffic data, time-series prediction, caption generation, etc. Benefits of LSTM The LSTM model is handy and easy to use while modelling high sequence dependencies and chronological sequences. Drawbacks of LSTM The LSTM model tends more towards overfitting. It needs high computation and resources for training the LSTM model. It takes more time to process than other algorithms. Multilayer Perceptrons (MLPs)The Multilayer Perceptrons algorithm is the most basic and one of the oldest algorithms used in deep learning. MLPs consist of multiple perceptron layers with activation functions. It is based on the feedforward neural networks. MLPs have an equal number of input and output layers. This algorithm is used for speech, image recognition, machine translation, etc. Working of MLPs The input layer of the network gets the data. Then, the perceptron layers connect in a graph to pass the signals in only one direction. Then, the input gets computed with the weights between the input layer and the hidden layers. It uses activation functions to decide the nodes to be activated. Then, it trains the model, which further gets the correlation and learns the dependencies. Application of MLP The Multilayer Perceptron is used for compressing data on social media sites like Instagram and Facebook. It helps to load the images in a weak network. It is also used for other applications like speech recognition, data compression, etc. Benefits of MLP This algorithm is irrespective of the probability density function. Unlike other algorithms, which depend on the probability density function. This algorithm gives the decision function directly. Drawbacks of MLP It gives the output in only 0 or 1, as it has a hard limit function. Accuracy may be affected as the algorithm can get stuck in the local minimum. Radial Basis Function Networks (RBFNs)Radial Basis Function Networks are a type of feedforward neural network which uses radial basis activation function. This network contains an input layer, a hidden layer, and an output layer. Generally, it is used for problems like classification, regression, and time-series prediction. Working of Radial Basis Function Networks The Radial Basis Function Network uses the classification method to measure the similarity in the input from the training data. It has an input vector containing radial basis function neurons. The activation function is used to get the weighted sum of the input vectors. The neurons in the hidden layer have the Gaussian functions to transfer the neurons. The output produced is inversely proportional to the distance from the neuron centre. The neuron's parameter and the radial basis function give the network output. Applications of Radial Basis Function Networks The Radial Basis Function Network is used to analyse the stock market and predict sales prices. Like other algorithms, it is used in time-series prediction, speech recognition, medical diagnosis, etc. Benefits of Radial Basis Function Networks The training is faster in the Radial Basis function network than in other algorithms, as it only depends on the feedforward neural networks. The algorithm can easily explain the roles of the hidden layers. Limitations of Radial Basis Function Networks Though the training process is fast in Radial Basis Function Networks, it takes more time for the classification. This is because every node calculates the radial basis function for the input vectors; thus, it takes more time. Self-Organising Maps (SOMs)Self-organising maps are used for data visualisation, which helps in the dimension reduction of data using self-organising artificial neural networks. It allows users to understand the high-dimensional information that humans cannot easily analyse. Working of Self Organising Maps The self-organising maps start the process by initialising weights to each input node. Then, for the training portion, a vector is decided, which finds the best node that fits with the vector, and that node is called the Best Matching Unit (BMU). Then, the SOMs check the best matching node, which tells the number of neighbours. Then, it assigns weight to the vector. The weight of the vector is directly proportional to the distance. The less the distance between the node and BMU, the more its weight changes., and learns more. Applications of Self Organising Map Self-Organising Maps can be used in image analysis, process monitoring, etc. Its ability to create powerful visualisations makes it useful for 3D modelling. It is also used to make 3D charts for the health sector. Benefits of Self Organising Maps It is easy to understand and interpret the data using Self Organising Maps. Reduction in dimensions makes it simple to find similarities in the data. Limitations of Self Organising Maps This algorithm needs a sufficient amount of neurons to cluster the input data. If less or inadequate data is passed for the training, it leads to less accurate outputs. Deep Belief NetworksDeep Belief Networks are a type of model consisting of different layers of stochastic and latent variables. The deep belief networks are a group of Boltzmann machines with different connections between the layers. Each layer makes a connection with both the previous and next layer. Working of Deep Belief Networks This algorithm is based on the Greedy Algorithm. It uses a layer-by-layer approach, which gathers all the weights. It tells about the working and dependencies of the variables in each layer. It runs on Gibbs sampling. It draws samples using the Gibbs sampling. Then, a single pass of ancestral sampling is used to draw a sample from the units. Then, a bottom-up pass is used, concluding the learning of the latent variables. Applications of Deep Belief Networks It is used in image recognition, creating images, and motion capture data. It is also used in the gaming industry for making different 3D characters, like anime. Benefits of Deep Belief Networks It can also work smoothly with small datasets. It may be labelled or semi-labelled. This model is robust in nature. Drawbacks of Deep Belief Networks It needs higher specifications of hardware to process the input data. |
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