Neural networks in deep learning are extensively used in solving problems in supervised learning and reinforcement learning. The functioning of the neurons in our brain inspires neural networks. There are different types of neural networks in machine learning and deep learning, based on a simple concept – given some parameters, there is a way to combine them to predict a certain result.
For example, knowing the pixels of an image there will be a way to know what number is written. The input data are sequentially passing through different “layers” in which a series of learning rules modulated by a weight function is applied. After going through the last layer, the results are compared with the “correct” result, and the parameters are adjusted.
Learn more – What is Deep Learning?
In deep learning, the number of hidden layers, mostly non-linear, can be large; Let’s say about 1000 layers. These networks are optimized using the gradient descent method, which also minimizes the loss function. Training data sets are an important part of Deep Learning models. Deep learning deals with training large neural networks with complex input and output transformations.
Neural Networks Mapping
Neural networks in machine learning and deep learning are functions that have inputs like x1, x2, x3 … that are transformed into outputs like z1, z2, z3, etc. Weights and biases change from layer to layer. Deep learning can be extensively used in supervised learning problems. Here, we have a data set with a desired set of outputs.
The above image suggests the usage of a backpropagation algorithm to get a correct output prediction. The deep neural networks can be trained to classify the patterns and images from the data sets, basis parameters like features, facial structure, etc.
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How to Choose Neural networks in Deep Learning?
The basis of choosing a neural network is what we want to achieve, whether we wish to build a classifier or find patterns in the data, the type of chosen neural network will vary. To extract patterns from a set of unlabeled data, we use a restricted Boltzmann machine or an automatic encoder. You can consider the below points while choosing a machine learning and deep learning:
- Use recurring network or recursive neural tensor network for tasks like word processing, sentiment analysis, named entity analysis, and recognition
- Use the recurring network for language models that work at the character level
- Use deep belief network DBN or convolutional networks for pattern and image recognition
- Use an RNTN or a convolutional network for object recognition
- Use the recurring network for voice recognition
Let’s discuss the major types of neural networks in Deep Learning –
Deep Belief Networks
Deep Belief Networks (DBN) is a generative model using a deep architecture of multiple stacks of Restricted Boltzmann machines (RBM) and is formed by an intelligent training method. We have a new model that finally solves the problem of the disappearance of the gradient. Geoff Hinton invented RBMs and Deep Belief Nets as an alternative to backward spread.
A DBN is similar in structure to a Multi-Layer Perceptron but very different when it comes to training. it is the training that allows DBNs to outperform their shallow counterparts.
A DBN can be viewed as an RBM stack where the hidden layer of an RBM is the visible layer of the RBM above it. The first RBM is trained to rebuild your entrance as accurately as possible.
Generative Adversarial Networks
The Generative Adversarial Networks or GANs are deep neural networks comprising two networks, facing each other. GANs have a wide range of usage, as network scanning learns to mimic any data distribution. GANs can be taught to create parallel worlds strikingly similar to ours in any domain images, music, speech, prose.
GAN has –
- Generator – A neural network to generate new data instances
- Discriminator – Evaluates the data instances to determine their authenticity
Recurrent Neural Networks
RNNS are neural networks in which data can flow in any direction. These networks are used in language modeling or natural language processing (NLP). RNNs use sequential information. In a normal neural network, all inputs and outputs are assumed to be independent of each other. If we want to predict the next word in a sentence, we have to know what words came before it.
RNNs are named recurring as they repeat the same task for each element of a sequence, and the output is based on the previous calculations. RNNs have a “memory” that captures information about what has been previously calculated.
Long-term memory networks (LSTM) are the most frequently used RNNs. Along with convolutional neural networks, RNNs have been used as part of a model to generate descriptions of unlabeled images. It’s quite surprising how well this seems to work.
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Convolutional Deep Neural Networks
If we increase the number of layers in a neural network, the network becomes more complex and allows the modeling of complicated tasks.
CNN is a type of Artificial Neural Network with supervised learning that processes its layers imitating the visual cortex of the human eye to identify different characteristics in the inputs that ultimately make it able to identify objects and “see”. For this, CNN contains several specialized hidden layers with a hierarchy: this means that the first layers can detect lines, curves and are trained until they reach deeper layers. The deeper layers recognize complex shapes such as a face or the silhouette of an animal. CNNs are widely used in computer vision and are applied in acoustic modeling for automatic speech recognition.
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