Deep learning and neural network is probably the best topic in a research of machine learning. Companies like Facebook, Google and Baidu are investing in that field of research. The researchers believe that machine learning will be highly influencing human life in near future. A human task will automate using robots with a negligible margin of error. The best freelance graphic design jobs guide is to neural network and deep learning available on the internet today.
What is deep learning?
Deep learning can understand an algorithm, which is composed of a hidden layer of multiple neural networks. It works on an unsupervised data that provides an accurate result rather than traditional ML algorithm. Input data is passed via this algorithm and then passed through much nonlinearity before it delivers output. This algorithm is allowed to go deeper in the network (high abstraction level) without end up of writing a lot of duplicate code. As it goes very deeper, it filters a complex feature and combined with that previous layer and gets a better result.
Algorithms like SVM, Naïve Bayes and Decision Trees are a shallow algorithm. These are involved write a lot of duplicate code and causing trouble reuse of previous computation. Deep learning via neural network takes a very closer step to artificial intelligence.
Why deep learning?
- Classic machine learning is depending upon the designed feature that requires human expertise and complex to optimize.
- Deep learning is bypass feature engineering and it takes an advantage of data loss and flexible hierarchical models
- Deep learning is recently achieved as a strike performance and improved in a diverse field like game playing, image classification, natural language processing and speech recognition.
Deep learning and neural network:
The basis of deep learning research is an artificial neural network when the computational framework is interconnected into inspired nodes by the biological neural network. The deep is an aspect of deep learning that refers to the multi-layer architecture of the neural network, which is continuing multiple hidden layers of nodes between the input and output nodes.
Although the freelance writing jobs has a long history, which is trained to the deep neural network that is recently become feasible with the improved training technique, a large amount of labeled data and inexpensive parallel hardware.
What is the neural network?
A neural network has begun in the year of 1980. It has gained reignited interest in the recent time. A neural network is original as a biological phenomenon. A neural network is one type of networks, which is interconnected to neurons and maintained high coordination level to transmit and receive messages to the brain and spinal cord. In the machine learning, a neural network is referred as an artificial neural network.
Artificial neural network is namely suggested as a network of artificial that creates neuron and then adapt the cognitive skills to function such as a human brain. The application of artificial neuron network likes image recognition, soft sensor, time series predictions, anomaly detection and voice recognition.
Convolution neural network:
A neural network is operated on the pixel values. They are an organized weight of input into the matrix that represents a feature. The matrix weight for a feature is very small, but the future may occur anywhere and anyplace in the image, so they can apply only same matrix weight to many locations in the image. This sharing weight of parameters is simplified training and based on the conventional neural network. You can more information about the freelance graphic design jobs and conventional neural network, please look at an overview of neural network.
Each convolution operation is represented as a feature of an image that produces a matrix, which is usually small rather than input. A convolution layer is a convolution neural network produced many matrix outputs stacked in the volume. This volume serves as an input from one to another conventional layer, which is detected to complex high-level feature in the image.
What is deep learning in neural network?
An activation function of deep learning in a neural network is a non-linear, enabling to learn difficult and features of the non liner system. In addition to the input and output layer, an architecture of deep learning has a stack of the hidden layer between input and output layer. Deep learning neural network is capable of extracting deep benefits out of data; hence it is named as deep learning. There are many different types of neural networks. They are:
- Feed forward neural network: It has the first and most simple artificial neural network type. In this network, information is moved from one forward direction to another. From an input node, data is going through any hidden node and then passed to the output node. There is no loop or cycle in the network.
- Recurrent neural network: This network is comprised into bidirectional data flow that is information in a network flow from the later processing stage to an earlier stage. A recurrent neural network is used as a general sequence processor. Hop field network (similar to attract based network) is historic interest and it is not a general recurrent neural network, which is not designed to the basic sequence pattern process. Instead of it can require a stationary input. It is a recurrent neural network in which all the connections are symmetric and its invented by the John Hop field since 1982.
- Radial basic function in network: It is used to perform an interpolation in the multi-dimensional space. The radial basis function is one of the functions that have built in a distance criterion with respect to center. Radial basis function has two processing layer. First one is that input mapped into each radial basis function in a hidden layer. An activation function is chosen in radial basis function that is a Gaussian function contrary to the feed forward network where the activation function is a sigmoid.
Artificial intelligence is an extremely powerful and exciting in that field. It is going to become important and ubiquitous to move forward. Then it will certainly continue to have significant impact on the modern society. Artificial neural network and deep learning technique are one of the most capable of artificial intelligence tools, which is used to solve complex problems. Continue to develop and leveraged in your future. The freelance writing jobs like a scenario, which is unlike to any time as soon as possible, they will watch an exciting progression of artificial intelligence applications and techniques. Deep learning is used for hierarchical abstraction to learn data features. Learning is transferred into conventional neural network that is effectively applied to classification of radiology images.