This document provides an overview of deep learning, including definitions of AI, machine learning, and deep learning. It discusses neural network models like artificial neural networks, convolutional neural networks, and recurrent neural networks. The document explains key concepts in deep learning like activation functions, pooling techniques, and the inception model. It provides steps for fitting a deep learning model, including loading data, defining the model architecture, adding layers and functions, compiling, and fitting the model. Examples and visualizations are included to demonstrate how neural networks work.

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DEEP LEARNING
by
Mrs.Balne Sridevi
M.Tech(Ph.D),MISTE,MCSI
Asst. Professor
Dept. of CSE
Balaji Institute of Technology and Science

2. Contents
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 AI, ML and DL?
 Why Deep Learning
 Neural Network Models
 Schematic Diagram of Inception Model
 Activation Functions
 Pooling Techniques
 Demo

3. AI,ML & DL
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5. Why Deep Learning?
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7. Neural Network Models
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 Artificial Neural Network (ANN)
 Convolution Neural Network (CNN)
 Recurrent Neural Network (RNN)

8. Neural Network Model
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9. ANN Architecture
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10. CNN Architecture
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11. RCNN
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12. Schematic Diagram of Inception Model

13. Activation Functions
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 Sigmoid Activation Function
 ReLu Activation Function

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16. Max Pooling
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19. Average Pooling with stride 2

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23. Basic Steps to Fit a Model
 Linear Regression
 NN Model
 Load Data
 Define a model
 Add Layers, Activation Function
 Compile Model
 Fit Model
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24. Linear Regression
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25. Scatter() And Show() Methods
 plt.scatter(x,y,color=‘r’)
Where r is red in color
 plt.show() is used to display the x and y
coordinates
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26. How Neural Network Works?
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27. How Neural Network Works?
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28. Load Data
 Import pandas Library
 Create CSV File
 Load Data by using read_csv() method
 Syntax: data_frame = pd. read_csv(‘ file
name.csv’)
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29. Define Model
 Import model from keras library
 Import layers from keras library
 Define model as model=Sequential()
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30. Add Layers and Activation Functions
 model.add(Any_net_Inception_model(Number of
layers), activation Function=‘function_name’)
 Inception Models are Dense,Imagenet,Residual
Net V1, V2
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31. Compile Model
 model.compile(loss,optimizer,metrics)
 Loss={binary_crossentropy (multi Label ) or
categorical_crossentropy (multi classes))
 Optimizers={adam or adagrad or sgd etc..}
 metrics={accuracy ,count etc..}
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32. Fit a Model
 model.fit(x,y,epochs,batch_size)
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45. A Deep Dream of Neural
Network
https://www.youtube.com/watch?v=sh-MQboWJug
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46. Thank You
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