The document discusses the history and components of artificial neural networks. It notes several influential early works in the field from the 1940s to 1960s. It then explains that a basic neural network consists of an input layer, hidden layers, and an output layer composed of interconnected neurons. The document outlines different types of neural network architectures and applications of neural networks in areas like pattern classification, prediction, and computer vision tasks. It concludes by stating that neural networks can produce accurate results for problems in medical diagnosis, business analysis, and other domains when optimized properly.

2.  Mcclouh & Pits (1943)
 Hebb‘s (1949)
 Widrow & Hoff (1959)
 Hopfield Network(1962)
 Kohnen & Andresen(1972)

3. Artificial intelligence.
Artificial neural network is a non-linear computational system.
“NEURAL NETWORK”.
Better solving complex problems.
Input layer, hidden layers and output layer.

4. A NEURAL NETWORK

5. A BIOLOGICAL NEURON

6. Fundamental block is Neuron.
 A simple neural network consists of :
• three layers,
• activation function,
• learning technique,
• weights.
All these layers consist of neurons which are
interconnected to form a network.

9. • Feed Forward Network
• Feedback Network

10. • One direction
• No loops
• No feedback

12.  Natural Language Processing problems that can be
solved using neural networks. As we showed,
neural networks have many applications such as
 Pattern Classification
 Prediction And Financial Analysis
 Control and Optimization
 Face recognition
 In many cases, neural networks methods
outperform other methods.

13.  Artificial neural networks have application in
many areas as medical, business, agriculture, for
prediction purpose, classification purpose and
many others areas.
 By using artificial neural networks is that the
results are MORE ACCURATE, MEASURABLE
AND ERRORLES.
 Artificial Neural Networks can use cure and
prevent from lung cancer disease and also useful
in other industrial area. Performance of ANN can
be improved by using suitable optimization
technique as Ant colony Optimization.

14. PRESENTED BY :
PRIYA JOT