neuralnetwork.pptx
- 1. Neural Network Sherin Rappai Assistant Professor Department of Computer Science
- 2. What is an Artificial Neural Network? • Artificial Neural Networks are the computational models that are inspired by the human brain. • Many of the recent advancements have been made in the field of Artificial Intelligence, including Voice Recognition, Image Recognition, Robotics using Artificial Neural Networks. • Artificial Neural Networks are the biologically inspired simulations performed on the computer to perform certain specific tasks like – • Clustering • Classification • Pattern Recognition
- 3. • Artificial Neural Networks, in general – is a biologically inspired network of artificial neurons configured to perform specific tasks. • These biological methods of computing are considered to be the next major advancement in the Computing Industry.
- 4. What is a Neural Network? • The term ‘Neural’ is derived from the human nervous system’s basic functional unit ‘neuron’ or nerve cells that are present in the brain and other parts of the human body. • A neural network is a group of algorithms that certify the underlying relationship in a set of data similar to the human brain. • The neural network helps to change the input so that the network gives the best result without redesigning the output procedure.
- 5. Parts of Neuron and their Functions
- 6. • Function of Dendrite It receives signals from other neurons (accepts input). • Soma (cell body) It sums all the incoming signals to generate input (process input). • Axon Structure When the sum reaches a threshold value, neuron fires and the signal travels down the axon to the other neurons (turns the processed inputs into outputs). • Synapses Working (Electro chemical contact between neurons) • The point of interconnection of one neuron with other neurons. The amount of signal transmitted depend upon the strength (synaptic weights) of the connections. • The connections can be inhibitory (decreasing strength) or excitatory (increasing strength) in nature. • So, neural network, in general, is a highly interconnected network of billions of neuron with trillion of interconnections between them.
- 7. Artificial Neural Network • Artificial Neural Network (ANNs) are programs designed to solve any problem by trying to mimic the structure and the function of our nervous system. • Neural networks are based on simulated neurons, Which are joined together in a variety of ways to form networks. • Neural network resembles the human brain in the following two ways: - • A neural network acquires knowledge through learning. • A neural network’s knowledge is stored within the interconnection strengths known as synaptic weight.
- 9. Types of Neural Networks
- 11. Feed Forward networks • The data flow from the input to the output units is strictly feed forward. • The data processing can extend over multiple layers of units but there are no feedback connections extending from the outputs of units to the inputs of units in the same layer or previous layers. • A multilayer feed-forward network consists of an input layer, one or more hidden layers and an output layer. • The neurons in the input layer connect the elements of the input vector or input pattern to the next layer of the network. The hidden layers do the processing or computation.
- 12. • Usually, the activation function of the hidden layers will be nonlinear. The output of neurons in each layer is given as the input to the next layer. The set of outputs from the output layer forms the overall response of the network to the given input pattern.
- 13. Recurrent neural network • It is a dynamic network which contains feedback connections. • Feedback is said to exist in a dynamic system whenever the output of an element in the system influences in part the input applied to that particular element, thereby giving one or more closed paths to the signal transmission. • The learning capability of the network has increased with such loops. After applying a new input, the network output is calculated and fed back to adjust the input. Then the output is calculated again, and the process is repeated until the output becomes constant.
- 15. Terminology
- 16. Artificial Neural Network Model
- 17. Training Algorithms For Artificial Neural Networks • Gradient Descent Algorithm • This is the simplest training algorithm used in case of supervised training model. In case, the actual output is different from target output, the difference or error is find out. The gradient descent algorithm changes the weights of the network in such a manner to minimize this mistake.
- 18. Back Propagation algorithm • It is an error reducing algorithm used in artificial neural networks. Artificial neural networks are networks based on the human’s nerve system. • These networks contain well defined set of inputs and outputs. • The network is used to describe the complex relationship between the inputs and outputs of the network. • The name comes from the complexity of the network because the human’s nerves system is so much complex which is known by all of us.
- 19. • In this method, the traversal is from output node to various input nodes and hence called back propagation algorithm. • At each node the errors are analyzed and the error with minimum gradient weight is chosen and is referred to as local minima. • So at the end of the traversal we will have a set of local minimas. • Again an analysis is made on the local minimas and an error is chosen which have minimum gradient weight. This final minima is called global minima.
- 21. Artificial Neural Network Architecture • A typical Neural Network contains a large number of artificial neurons called units arranged in a series of layers.
- 23. • Input layer – It contains those units (Artificial Neurons) which receive input from the outside world on which network will learn, recognize about or otherwise process. • Output layer – It contains units that respond to the information about how it’s learned any task. • Hidden layer – These units are in between input and output layers. The job of the hidden layer is to transform the input into something that output unit can use in some way. • Most Neural Networks are fully connected that means to say each hidden neuron is fully linked to every neuron in its previous layer(input) and to the next layer (output) layer.
Editor's Notes
- #13: Non-linear : output does not change in direct proportion to a change in any of the inputs Input layer: This layer consists of the neurons that receive inputs and pass them on to the other layers. The number of neurons in the input layer should be equal to the attributes or features in the dataset. Output layer: The output layer is the predicted feature and depends on the type of model you’re building. Hidden layer: In between the input and output layer, there are hidden layers based on the type of model. Hidden layers contain a vast number of neurons which apply transformations to the inputs before passing them. As the network is trained, the weights are updated to be more predictive. Neuron weights: Weights refer to the strength or amplitude of a connection between two neurons. Weights are often initialized to small random values, such as values in the range 0 to 1.