Deep Learning for Image Classification in Python with CNN

سرفصل های دوره

Convolutional Neural Networks for Computer Vision With Keras and TensorFlow on Google Colab Platform : Hands-on

1. Fundamentals

1. Introduction

2. Artificial Intelligence

3.1 Course Association Rule Unsupervised Machine Learning in Python.html

3.2 Course Classification Models Supervised Machine Learning in Python.html

3.3 Course Cluster Analysis Unsupervised Machine Learning in Python.html

3.4 Course Regression Analysis Supervised Machine Learning in Python.html

3. Machine Learning

4.1 Course Learn Python 3 Programming from Scratch.html

4. Deep Learning

5. Artificial Neural Networks (Conventional Traditional)

6. Backward Propagation of Errors

7. Gradient Descent

8. Stochastic Gradient Descent

9. Convolutional Neural Networks (CNN)

10. Input Layer, Convolutional Layer

11. Pooling Layer, Activation Function Layer

12. Fully Connected Layers Dense Layer, Dropout Layer

13. Image Classification and its Applications

14. How image classification is done

15. Transfer Learning

16. Architecture of ResNet (Residual Networks)

2. Building, Evaluating and Predicting Image Classification Model

1.1 natural_scenes.zip

1. Download Dataset

2. What is inside train folder

3. What is the .hdf5 file

4. What is inside test folder

5. What is inside our_prediction folder

6. Image Classification Python Code

7. Enabling GPU in Google Colab

8. Is GPU connected to Colab notebook

9. Download TensorFlow and CUDA

10. Compare the Speed of GPU with CPU

11. Connect Google Colab with Google Drive

12. Check the Number of Images in the Dataset

13. Image Augmentation

14. Transfer Learning

15. Fine Tuning Freezing of the Layers

16. Model Compilation

17. Callbacks EarlyStopping

18. Callbacks ModelCheckpoint

19. Training

20. Testing

21. Prediction

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تولید کننده: Udemy-Training