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Neural Networks Course And Certification

Neural Networks Course, Neural Networks Certificate, Neural Networks Training. 

What is Neural Networks? 

Neural Networks are parallel computing devices basically designed to function like a human brain. The concept and main objective is to develop a system to perform various computational tasks faster and smatter than traditional systems.

Neural Network can also be thought of as a series of algorithms that attempts to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. With this knowledge, we can say neural networks refer to systems of neurons, either organic or artificial in nature.

How do Neural Networks Work?

Neural Networks are a set of algorithms that are modeled loosely after the human brain, that is designed to recognize patterns. They work similarly to the human brain's neural network. 

Neural Networks interpret sensory data with a kind of machine perception, labelling or clustering raw input. The patterns neural networks recognizes are numerical, contained in vectors, into which all real-world data will be interpreted. It can either be images, sound, text or time series.  

A "Neuron" in a Neural Network is a mathematical function that gathers and classifies information according to a definite architecture. The network has a strong similarity to statistical methods such as curve fitting and regression analysis.

Neural Networks are usually arranged in layers of nodes. Layers are made up of a number of interconnected 'nodes' which has an 'activation function'. Afterward, patterns are introduced to the network through the 'input layer', which communicates to one or more 'hidden layers' where the real processing is done through a system of weighted 'connections'.

Most Neural Networks contain some form of 'learning rule' which modifies the weights of the connections according to the input patterns that it is presented with. With this knowledge, we can say a Neural network learns by example presented as do their biological counterparts. A real-life example is a child learns to recognize dogs from examples of dogs seen from pictures.

So each node in a layer is a perceptron and is comparable to multiple linear regression. The perceptron passes the signal generated by a multiple linear regression into an activation function that may be nonlinear.

Perceptrons are usually arranged in interconnected layers in a multi-layered perceptron (MLP). The input layer collects input patterns while the output layer has classifications or output signals to which input patterns may map. For example, the patterns may comprise a list of quantities for technical indicators about security; potential outputs could be "buy," "hold" or "sell."

Although there are various different kinds of learning rules used by neural networks, this demonstration is concerned only with one; the delta rule. The delta rule is frequently employed by the most common class of Neural Networks known as 'Backpropagation Neural Networks' (BPNNs). Back Propagation is an abbreviation for the backward propagation of error.

Using Delta Rule, 'learning' is a supervised process that occurs with each cycle or 'epoch' (i.e. each time the network is presented with a new input pattern) through a forward activation flow of outputs, and the backward error propagation of weight adjustments. To be clear, when a neural network is initially presented with a pattern it makes a random 'guess' as to what it might be. Afterward, it compares how far its answer was from the original one and makes an appropriate adjustment to its connection weights. 

How Neural Networks Learn: 

Typically, Neural Networks are originally trained and fed with large amounts of data. The training process consists of providing input and telling the network what the output should be.

For instance, to build a network that identifies the faces of actors, the initial training might be a series of pictures, including actors, non-actors, masks, statuary and animal faces. Each input is followed by the matching identification, such as actors' names, "not actor" or "not human" information. The provided answers let the model adjust its internal weightings to learn how to do its job better.

Neural Networks with their deep learning cannot be programmed directly for a particular task, unlike other algorithms. In this case, they usually have the requirement, similar to a child's developing brain, needed to learn the information. 

There are three learning methods utilized by Neural Network:

Supervised Learning: This learning strategy is the simplest, as there is a labeled dataset, which the computer goes through, and the algorithm gets modified until it can process the dataset to get the desired result.

Unsupervised Learning: This strategy gets used in cases where there is no labeled dataset available to learn from. The neural network analyzes the dataset, and then a cost function then tells the neural network how far off of target it was. The neural network then adjusts to increase the accuracy of the algorithm.

Reinforced Learning: In this algorithm, the neural network is reinforced for positive results, and punished for a negative result, forcing the neural network to learn over time.

Applications of Neural Networks

Neural Networks are widely used on various applications like:

1. Financial Operations,

2. Enterprise Planning,

3. Trading and Transaction Operations

4. Business Analytics and

5. Product Maintenance.

Neural Networks are also employed in business applications such as:

1. Forecasting 

2. Marketing Research Solutions

3. Fraud Detection

4. Risk Assessment

5. Future Occurence Predictions

A Neural Network evaluates price data and unearths opportunities for making trade decisions based on the data analysis. The networks can distinguish subtle nonlinear interdependencies and patterns other methods of technical analysis cannot. According to research, the accuracy of neural networks in making price predictions for stocks differs. Some models predict the correct stock prices 50 to 60 percent of the time while others are accurate in 70 percent of all instances. Some have posited that a 10 percent improvement in efficiency is all an investor can ask for from a neural network.

There will always be data sets and task classes that are better analyzed by using previously developed algorithms. It is not so much the algorithm that matters; it is the well-prepared input data on the targeted indicator that ultimately determines the level of success of a neural network.

Advantages of Neural Networks

Some of the Advantages of Neural Networks are:

1. Parallel processing abilities mean the network can perform more than one job at a time.

2. Information is stored on an entire network, not just a database.

3. The ability to learn and model nonlinear, complex relationships helps model the real-life relationships between input and output.

4. Fault tolerance means the corruption of one or more cells of the ANN will not stop the generation of output.

5. Gradual corruption means the network will slowly degrade over time, instead of a problem destroying the network instantly.

6. The ability to produce output with incomplete knowledge with the loss of performance is based on how important the missing information is.

7. No restrictions are placed on the input variables, such as how they should be distributed.

8. Machine learning means the ANN can learn from events and make decisions based on the observations.

9. The ability to learn hidden relationships in the data without commanding any fixed relationship means an ANN can better model highly volatile data and non-constant variance.

10. The ability to generalize and infer unseen relationships on unseen data means ANNs can predict the output of unseen data.

This course covers the basic concept and terminologies involved in Artificial Neural Network. Sections of this course also explain the architecture as well as the training algorithm of various networks used in ANN.

Neural Networks Course Online: 

Basic Concepts

Building Blocks

Learning & Adaptation

Supervised Learning

Unsupervised Learning

Learning Vector Quantization

Adaptive Resonance Theory

Kohonen Self-Organizing Feature Maps

Associate Memory Network

Hopfield Networks

Boltzmann Machine

Brain-State-in-a-Box Network

Optimization Using Hopfield Network

Other Optimization Techniques

Genetic Algorithm

Applications of Neural Networks

Exams and Certification

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