مبانی هوش مصنوعی و یادگیری عمیق

1.1. History of AI

14

2.1. AI, ML, and Deep learning

13

3.1. Applications of artificial intelligence and types of AI

16

4.1. Supervised and Unsupervised Learning

12

1.2. Getting started in scikit-learn with iris dataset

29

2.2. Image as a data

21

3.2. Persian digit dataset

18

4.2. Persian digit classification

19

5.2. generalization, overfitting, and underfitting

14

1.3. Linear Regression

15

2.3. Linear Models and Regularization methods

25

3.3. Unsupervised learning(Transformations)

11

4.3. Unsupervised learning(Clustring)

12

5.3. Cross validation, Model complexity and Gridsearch

39

6.3. Model Evaluation and Confusion matrix

06

1.4. What is perceptron and introduction to neural network

15

2.4. Nonlinearity,activation function, and cost function

20

3.4. Optimization, gradient descent, and backpropagation

23

4.4. How define a model in Keras and tensorflow

15

5.4. Batch, Mini-Batch and Stochastic Gradient decent

15

6.4. Simple Neural Network in Keras

20

7.4. Sparse label, from logits and flatten layer

18

8.4. Overfitting and dropout

16

1.5. A brief history of CNN

17

2.5. Layer hirarchy in convolutional neural network

17

3.5. Convolution kernel

17

4.5. Padding and stride in convolution

14

5.5. Convolution on rgb image and simple network

21

6.5. Average and max pooling operation

10

7.5. LeNet-5 architecture

05

1.6. Calculating the number of Parameters and shapes in CNNs

12

2.6. Convolution Neural Network in Tensorflow Keras

06

3.6. Learning rate

08

4.6. Batch Normalization

18