This document provides an overview of deep learning and convolutional neural networks (CNNs). It discusses topics like artificial neural networks, CNN architecture including convolution, ReLU, pooling and fully connected layers. It also explains how CNNs work by scanning images through these layers and detecting patterns. Code examples in Python are given to demonstrate preprocessing data, building a CNN model, training it and making predictions. Key concepts like softmax and cross-entropy functions used for classification are also overviewed.

1. Deep Learning
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Topics
Artificial Neural Network
Convolution Neural Network
Recurring Neural Network

4. Deep Learning
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5. Deep Learning

6. Deep Learning
Synapses

7. The Activation Function

8. Types of Activation Functions
Rectifier Leaky Rectifier
Hyperbolic Tangent

9. Types of Activation Functions

10. How Neural Networks Work?
X1
X2
X3
X4
Y
Area in (feet)2
Bedrooms
Distance to City
(Miles)
Age
INPUT LAYER OUTPUT LAYER
W2
W1
W3
W4
Price = W1X1 + W2X2 + W3X3 + W4X4

11. X1
X2
X3
X4
Y
Area in (feet)2
Bedrooms
Distance to City
(Miles)
Age
INPUT LAYER OUTPUT LAYER
H2
H3
H4
H5
H1
HIDDEN LAYER

12. Feed Forward Neural Network/Perceptron

13. Perceptron Learning Rule

14. Perceptron Learning Rule

15. Gradient Descent
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16. Gradient Descent
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17. 17

18. Stochastic Gradient Descent
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19. Stochastic Gradient Descent
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20. Gradient Descent-Backward Propagation
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21. Training the ANN with Gradient Descent
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22. Building ANN
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23. Building ANN
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24. Building ANN
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25. Building ANN
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26. Building ANN
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27. Homework
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28. Convolution Neural Network
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What are Convolution Neural Networks?
Step 1  Convolution Operation
Step 1b  ReLU Layer
Step 2  Pooling
Step 3  Flattening
Step 4  Full Connection
Softmax and Cross-Entropy

29. Convolution Neural Network
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• How do our brains work?
• How do convolutional neural networks work?
• How do they scan images?
• How do neural networks read facial expressions?
• What are the steps that constitute the convolutional
neural network's process?

30. What are CNN?
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31. Convolution NN
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32. Convolution NN
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33. Convolution NN
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34. Sheepdog or mop?
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35. Donald Trump or Raw Chicken
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36. Test Set Example
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37. Why are CNNs so important?
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38. How do CNNs Operate?
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39. Convolution NN
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40. How CNNs scan images?
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41. How CNNs scan images?
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42. Detecting Facial Expressions
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43. Convolution NN
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44. Convolution NN
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45. The Convolution Operation
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46. How does the Convolution Operation work?
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47. How does the Convolution Operation work?
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48. How does the Convolution Operation work?
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49. How does the Convolution Operation work?
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50. How does the Convolution Operation work?
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51. How does the Convolution Operation work?
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52. How does the Convolution Operation work?
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53. How does the Convolution Operation work?
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54. How does the Convolution Operation work?
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55. How does the Convolution Operation work?
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56. The Convolution Operation
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57. The Convolution Operation
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58. The Convolution Operation
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59. The Convolution Operation
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60. The Convolution Operation
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61. The Convolution Operation
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62. The Convolution Operation
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63. The Convolution Operation
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64. Step 1b: ReLU
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65. Step 1b: ReLU
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66. Step 1b: ReLU
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67. Step 1b: ReLU
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68. Step 1b: ReLU
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69. Step 2: MaxPooling
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70. Step 2: MaxPooling
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71. Types of Pooling
1. Mean Pooling
2. Max Pooling
3. Sum Pooling
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72. How Pooling works?
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73. How Pooling works?
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74. How Pooling works?
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75. How Pooling works?
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76. How Pooling works?
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77. How Pooling works?
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78. How Pooling works?
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79. How Pooling works?
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80. How Pooling works?
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81. How Pooling works?
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82. Convolution and Pooling
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83. The Number Game
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84. Step 3 Flattening
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85. Step 3 Flattening
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86. Step 3 Flattening
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87. Step 4 Full Connection
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88. Step 4 Full Connection
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89. Class Recognition
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90. Class Recognition
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91. Class Recognition
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92. Example
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93. Example
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94. The Convolution Process: A Quick Recap
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95. Python code for CNN
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# Importing the libraries
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
tf.__version__
# Part 1 - Data Preprocessing
# Preprocessing the Training set
train_datagen = ImageDataGenerator(rescale = 1./255, shear_range =
0.2,zoom_range = 0.2,horizontal_flip = True)
training_set = train_datagen.flow_from_directory
('dataset/training_set',target_size = (64, 64),batch_size = 32,
class_mode = 'binary')
# Preprocessing the Test set
test_datagen = ImageDataGenerator(rescale = 1./255)
test_set = test_datagen.flow_from_directory('dataset/test_set',
target_size = (64, 64),batch_size = 32,class_mode = 'binary')

96. Python code for CNN
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# Part 2 - Building the CNN
# Initialising the CNN
cnn = tf.keras.models.Sequential()
# Step 1 - Convolution
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu',
input_shape=[64, 64, 3]))
# Step 2 - Pooling
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
# Adding a second convolutional layer
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
# Step 3 - Flattening
cnn.add(tf.keras.layers.Flatten())
# Step 4 - Full Connection
cnn.add(tf.keras.layers.Dense(units=128, activation='relu'))
# Step 5 - Output Layer
cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))

97. Python code for CNN
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# Part 3 - Training the CNN
# Compiling the CNN
cnn.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Training the CNN on the Training set and evaluating it on the Test set
cnn.fit(x = training_set, validation_data = test_set,epochs= 25)

98. Python code for CNN
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# Part 4 - Making a single prediction
import numpy as np
from tensorflow.keras.preprocessing import image
test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.jpg', target_size =
(64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = cnn.predict(test_image)
training_set.class_indices
if result[0][0] == 1:
prediction = 'dog'
else:
prediction = 'cat'
print(prediction)

99. Softmax and Cross-Entropy Functions
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100. Softmax Function
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101. Cross-Entropy
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102. Demonstration
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103. Demonstration
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104. Demonstration
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