Madhu Babu Sanjeevi is a software engineer skilled in various programming languages and technologies, particularly in machine learning and neural networks. The document outlines key concepts such as the structure of neural networks, types of neural networks, and processes like forward and back propagation. It also highlights practical applications of neural networks in fields like speech recognition and natural language processing.

1. About Me
Madhu Babu Sanjeevi,
Software Engineer,
Languages C, Java, Python
Technologies Machine Learning, Deep Learning,
NLP, Big data, Mobile dev.
Developments Android, Web, Rest API
Databases SQL, Mongo DB
Madhu Sanjeevi Madhu009 Madhu.ai

2. Agenda
•1.Introduction of Neural Networks
•2.Forward and Back propagation
algorithms
•3.Different types of Neural Networks
•4.Neural Networks Demo

3. Machine Learning
Computer
Data
Program
Output
Computer
Data
Output
Program
Traditional Programming
Machine Learning

4. Supervised Learning
• 1. Training data includes both the input and the desired
results.
• 2. For some examples the correct results (targets) are
known and are given in input to the model during the
learning process.
• 3. These methods are usually fast and accurate.

5. Classification: for categorical response values, where the data can be
separated into specific “classes”.
Regression: for continuous-response values

6. Neural Networks

7. A Neuron
• A neuron is a computational unit in the neural network that
exchanges messages with each other.
input
Predicted output

8. • Neurone vs. Node

9. Work Flow of a neuron
(X1 * w1) + (X2 * w2) + (X3 * w3) + (X4 * w4) = Sum
Sum Output

10. Diagram of an NN
A simple 1 layer Neural Network with 2 output

11. Network Layers
• Input Layer - The activity of the input units represents the
raw information that is fed into the network.
• Hidden Layer - The activity of each hidden unit is
determined by the activities of the input units and the
weights on the connections between the input and the
hidden units.
• Output Layer - The behavior of the output units depends
on the activity of the hidden units and the weights
between the hidden and output units.

12. Diagram of an NN
A simple 2 layer Neural Network with 1 output

13. Feed-forward
Information flow is unidirectional
Data is presented to Input layer
Passed on to Hidden Layer
Passed on to Output layer
Information is distributed
Information processing is parallel
Internal representation (interpretation) of data

14. • Feeding data through the net:
(1 * 0.712) + (1 * 0.112) = 0.82  0.69
(1 *0.355) + (1 * 0.855) = 1.21  0.77
(1 * 0.268) + (1* 0.468) = 0.74  0.68
(0.69 * 0.116) + (0.77 * 0.329) + (0.68 * 0.708)=0.81  0.69
Output

15. Back Propagation
Forward Propagation Back Propagation

16. Back Propagation (Cont..)
Output
1.Calculate the error
Error = Actual – Predicted
2.Update Output value and Sum
3.Updates weights, output value and sum
of hidden layer
4.Finally update weights of input layer

17. Sample updated values for 1 iteration
Back Propagation (Cont..)
Repeat FB and BP for over 10000 iterations or minimum error

19. Neural Networks-Types
1. Feed forward network:
SLP (Single Layer perceptron):
MLP (Multi Layer Perceptron):
2. RNN (Recurrent Neural Network):
3. CNN (Convolution neural Network):
4.TDNN (Time Delay Neural Network) or Shared Weights Neural
Networks:
5. Hopfield Networks

20. Where can we use NN’s
• Speech Recognition
• Computer Vision
• Natural Language Processing
• Used in all Domains
(Banking, Insurance, Healthcare, etc…)
20