课程目录

• 1. Introduction
• 1. Course content
• 1. Course content.srt
• 2. Google Drive folder.html
• 10. Object detection with YOLO
• 1. Plan of attack
• 1. Plan of attack.srt
• 1.1 Source code – Google Colab File.html
• 10. Additional reading.html
• 2. YOLO – intuition
• 2. YOLO – intuition.srt
• 3. Downloading and compiling Darknet
• 3. Downloading and compiling Darknet.srt
• 4. Testing the detector
• 4. Testing the detector.srt
• 5. Darknet and GPU
• 5. Darknet and GPU.srt
• 6. Threshold and ext_output parameters
• 6. Threshold and ext_output parameters.srt
• 7. Detecting objects in videos
• 7. Detecting objects in videos.srt
• 8. HOMEWORK.html
• 9. Homework solution
• 9. Homework solution.srt
• 11. Recognition of gestures and actions
• 1. Plan of attack
• 1. Plan of attack.srt
• 1.1 Source code – Google Colab File.html
• 10. HOMEWORK.html
• 11. Homework solution
• 11. Homework solution.srt
• 12. Additional reading.html
• 2. Gestures and actions recognition – intuition
• 2. Gestures and actions recognition – intuition.srt
• 3. Importing the libraries and the image
• 3. Importing the libraries and the image.srt
• 4. Loading the pre-trained neural network
• 4. Loading the pre-trained neural network.srt
• 5. Predicting body points 1
• 5. Predicting body points 1.srt
• 6. Predicting body points 2
• 6. Predicting body points 2.srt
• 7. Detecting gestures in images
• 7. Detecting gestures in images.srt
• 8. Detecting gestures in videos 1
• 8. Detecting gestures in videos 1.srt
• 9. Detecting gestures in videos 2
• 9. Detecting gestures in videos 2.srt
• 12. Deep dream
• 1. Plan of attack
• 1. Plan of attack.srt
• 1.1 Source code – Google Colab File.html
• 10. HOMEWORK.html
• 11. Homework solution
• 11. Homework solution.srt
• 12. Additional reading.html
• 2. Deep dream – intuition
• 2. Deep dream – intuition.srt
• 3. Loading the InceptionNet network
• 3. Loading the InceptionNet network.srt
• 4. Loading and preprocessing the image
• 4. Loading and preprocessing the image.srt
• 5. Getting the activations
• 5. Getting the activations.srt
• 6. Calculating the loss
• 6. Calculating the loss.srt
• 7. Gradient ascent 1
• 7. Gradient ascent 1.srt
• 8. Gradient ascent 2
• 8. Gradient ascent 2.srt
• 9. Generating images
• 9. Generating images.srt
• 13. Style transfer
• 1. Plan of attack
• 1. Plan of attack.srt
• 1.1 Source code – Google Colab File.html
• 10. Training the neural network 2
• 10. Training the neural network 2.srt
• 11. Visualizing the result
• 11. Visualizing the result.srt
• 12. HOMEWORK.html
• 13. Homework solution
• 13. Homework solution.srt
• 14. Additional reading.html
• 2. Style transfer – intuition
• 2. Style transfer – intuition.srt
• 3. Loading VGG19 network
• 3. Loading VGG19 network.srt
• 4. Loading and pre-processing the images
• 4. Loading and pre-processing the images.srt
• 5. Building the neural network 1
• 5. Building the neural network 1.srt
• 6. Building the neural network 2
• 6. Building the neural network 2.srt
• 7. Building the neural network 3
• 7. Building the neural network 3.srt
• 8. Building the neural network 4
• 8. Building the neural network 4.srt
• 9. Training the neural network 1
• 9. Training the neural network 1.srt
• 14. GANs (Generative adversarial networks)
• 1. Plan of attack
• 1. Plan of attack.srt
• 1.1 Homework solution.html
• 1.2 Source code – Google Colab File.html
• 10. HOMEWORK.html
• 11. Homerwork solution
• 11. Homerwork solution.srt
• 12. Additional reading.html
• 2. GANs – intuition
• 2. GANs – intuition.srt
• 3. Loading the dataset
• 3. Loading the dataset.srt
• 4. Building the generator 1
• 4. Building the generator 1.srt
• 5. Building the generator 2
• 5. Building the generator 2.srt
• 6. Building the discriminator
• 6. Building the discriminator.srt
• 7. Calculating the loss
• 7. Calculating the loss.srt
• 8. Training the GAN 1
• 8. Training the GAN 1.srt
• 9. Training the GAN 2
• 9. Training the GAN 2.srt
• 15. Image segmentation
• 1. Plan of attack
• 1. Plan of attack.srt
• 1.1 Source code – Google Colab File.html
• 10. Segmentation in videos 2
• 10. Segmentation in videos 2.srt
• 11. HOMEWORK.html
• 12. Homerwork solution
• 12. Homerwork solution.srt
• 13. Additional reading.html
• 2. Image segmentation – intuition
• 2. Image segmentation – intuition.srt
• 3. Downloading the repository
• 3. Downloading the repository.srt
• 4. Importing the libraries(1)
• 4. Importing the libraries
• 4. Importing the libraries.srt
• 5. Loading the pre-trained neural network(1)
• 5. Loading the pre-trained neural network
• 5. Loading the pre-trained neural network.srt
• 6. Detecting objects
• 6. Detecting objects.srt
• 7. Removing the background 1
• 7. Removing the background 1.srt
• 8. Removing the background 2
• 8. Removing the background 2.srt
• 9. Segmentation in videos 1
• 9. Segmentation in videos 1.srt
• 16. Final remarks
• 1. Final remarks
• 1. Final remarks.srt
• 2. Face detection
• 1. Plan of attack
• 1. Plan of attack.srt
• 1.1 Source code – Google Colab File.html
• 10. Homework solution
• 10. Homework solution.srt
• 11. HOG (Histrograms of Oriented Gradients) – intuition
• 11. HOG (Histrograms of Oriented Gradients) – intuition.srt
• 12. Face detection with HOG and Dlib
• 12. Face detection with HOG and Dlib.srt
• 13. Face detection with CNN and Dlib
• 13. Face detection with CNN and Dlib.srt
• 14. HOMEWORK – Haarcascade x HOG x CNN.html
• 15. Homework solution
• 15. Homework solution.srt
• 16. Anaconda and PyCharm
• 16. Anaconda and PyCharm.srt
• 17. Face detection on the webcam
• 17. Face detection on the webcam.srt
• 18. Additional reading.html
• 2. Images and pixels
• 2. Images and pixels.srt
• 3. Cascade classifier – intuition
• 3. Cascade classifier – intuition.srt
• 4. Loading and pre-processing the image
• 4. Loading and pre-processing the image.srt
• 5. Face detection with Haarcascade and OpenCV
• 5. Face detection with Haarcascade and OpenCV.srt
• 6. Haarcascades parameters 1
• 6. Haarcascades parameters 1.srt
• 7. Haarcascades parameters 2
• 7. Haarcascades parameters 2.srt
• 8. Eye detection with haarcascades
• 8. Eye detection with haarcascades.srt
• 9. HOMERWORK – detecting other objects.html
• 3. Face recognition
• 1. Plan of attack
• 1. Plan of attack.srt
• 1.1 Source code – Google Colab File.html
• 10. Detecting facial points
• 10. Detecting facial points.srt
• 11. Detecting facial descriptors 1
• 11. Detecting facial descriptors 1.srt
• 12. Detecting facial descriptors 2
• 12. Detecting facial descriptors 2.srt
• 13. Calculating distances between faces
• 13. Calculating distances between faces.srt
• 14. Recognizing faces with Dlib 1
• 14. Recognizing faces with Dlib 1.srt
• 15. Recognizing faces with Dlib 2
• 15. Recognizing faces with Dlib 2.srt
• 16. HOMEWORK.html
• 17. Homework solution
• 17. Homework solution.srt
• 18. Face recognition on the webcam
• 18. Face recognition on the webcam.srt
• 19. Additional reading.html
• 2. LBPH algorithm – intuition
• 2. LBPH algorithm – intuition.srt
• 3. Loading the faces dataset
• 3. Loading the faces dataset.srt
• 4. Preprocessing the images
• 4. Preprocessing the images.srt
• 5. Training the LBPH classifier
• 5. Training the LBPH classifier.srt
• 6. Recognizing faces with LBPH
• 6. Recognizing faces with LBPH.srt
• 7. Evaluating the LBPH classifier
• 7. Evaluating the LBPH classifier.srt
• 8. LBPH parameters
• 8. LBPH parameters.srt
• 9. LBPH parameters – implementation
• 9. LBPH parameters – implementation.srt
• 4. Object tracking
• 1. Plan of attack
• 1. Plan of attack.srt
• 2. Object tracking vs. object detection
• 2. Object tracking vs. object detection.srt
• 3. KCF and CSRT algorithms
• 3. KCF and CSRT algorithms.srt
• 4. Object tracking with KCF
• 4. Object tracking with KCF.srt
• 5. Object tracking with CSRT
• 5. Object tracking with CSRT.srt
• 6. HOMEWORK.html
• 7. Homework solution
• 7. Homework solution.srt
• 8. Additional reading.html
• 5. Neural networks for image classification
• 1. Plan of attack
• 1. Plan of attack.srt
• 1.1 Source code – Google Colab File.html
• 1.2 Homework solution.html
• 10. Hidden layer activation 2
• 10. Hidden layer activation 2.srt
• 11. Output layer activation
• 11. Output layer activation.srt
• 12. Error calculation (loss function)
• 12. Error calculation (loss function).srt
• 13. Basic algorithm
• 13. Basic algorithm.srt
• 14. Gradient descent and derivative
• 14. Gradient descent and derivative.srt
• 15. Output layer delta
• 15. Output layer delta.srt
• 16. Hidden layer delta
• 16. Hidden layer delta.srt
• 17. Backpropagation and learning rate
• 17. Backpropagation and learning rate.srt
• 18. Weight update with backprogation 1
• 18. Weight update with backprogation 1.srt
• 19. Weight update with backprogation 2
• 19. Weight update with backprogation 2.srt
• 2. Biological fundamentals
• 2. Biological fundamentals.srt
• 20. Bias, error and multiple outputs
• 20. Bias, error and multiple outputs.srt
• 21. Hidden layers
• 21. Hidden layers.srt
• 22. Output layer with categorical data
• 22. Output layer with categorical data.srt
• 23. Stochastic gradient descent
• 23. Stochastic gradient descent.srt
• 24. Deep learning
• 24. Deep learning.srt
• 25. Pixels and neural networks
• 25. Pixels and neural networks.srt
• 26. Importing the libraries
• 26. Importing the libraries.srt
• 27. Extracting pixels from images 1
• 27. Extracting pixels from images 1.srt
• 28. Extracting pixels from images 2
• 28. Extracting pixels from images 2.srt
• 29. Extracting pixels from images 3
• 29. Extracting pixels from images 3.srt
• 3. Artificial neuron
• 3. Artificial neuron.srt
• 30. Extracting pixels from images 4
• 30. Extracting pixels from images 4.srt
• 31. Normalizing the data
• 31. Normalizing the data.srt
• 32. Creating the train and test sets
• 32. Creating the train and test sets.srt
• 33. Building and training the neural network
• 33. Building and training the neural network.srt
• 34. Evaluating the neural network
• 34. Evaluating the neural network.srt
• 35. Saving and loading the network
• 35. Saving and loading the network.srt
• 36. Classifying one single image
• 36. Classifying one single image.srt
• 37. Extracting features from images
• 37. Extracting features from images.srt
• 38. Feature extraction with OpenCV 1
• 38. Feature extraction with OpenCV 1.srt
• 39. Feature extraction with OpenCV 2
• 39. Feature extraction with OpenCV 2.srt
• 4. Perceptron
• 4. Perceptron.srt
• 40. Feature extraction with OpenCV 3
• 40. Feature extraction with OpenCV 3.srt
• 41. Feature extraction with OpenCV 4
• 41. Feature extraction with OpenCV 4.srt
• 42. Feature extraction with OpenCV 5
• 42. Feature extraction with OpenCV 5.srt
• 43. Creating the train and test sets
• 43. Creating the train and test sets.srt
• 44. Building and training the neural network
• 44. Building and training the neural network.srt
• 45. Evaluating the neural network
• 45. Evaluating the neural network.srt
• 46. Saving, loading and classifying one single image
• 46. Saving, loading and classifying one single image.srt
• 47. HOMEWORK.html
• 48. Homework solution
• 48. Homework solution.srt
• 49. Additional reading.html
• 5. Weight update 1
• 5. Weight update 1.srt
• 6. Weight update 2
• 6. Weight update 2.srt
• 7. Introduction to multilayer neural networks
• 7. Introduction to multilayer neural networks.srt
• 8. Activation functions
• 8. Activation functions.srt
• 9. Hidden layer activation 1
• 9. Hidden layer activation 1.srt
• 6. Convolutional neural networks for image classification
• 1. Plan of attack
• 1. Plan of attack.srt
• 1.1 Homework solution.html
• 1.2 Source code – Google Colab File.html
• 10. Building and training the neural network
• 10. Building and training the neural network.srt
• 11. Evaluating the neural network
• 11. Evaluating the neural network.srt
• 12. Saving and loading the network
• 12. Saving and loading the network.srt
• 13. Classifying one single image
• 13. Classifying one single image.srt
• 14. HOMEWORK.html
• 15. Homework solution
• 15. Homework solution.srt
• 16. Additional reading.html
• 2. Introduction to convolutional neural networks
• 2. Introduction to convolutional neural networks.srt
• 3. Convolutional operation
• 3. Convolutional operation.srt
• 4. Pooling
• 4. Pooling.srt
• 5. Flattening
• 5. Flattening.srt
• 6. Dense neural network
• 6. Dense neural network.srt
• 7. Importing the libraries
• 7. Importing the libraries.srt
• 8. Loading the images
• 8. Loading the images.srt
• 9. Creating the train and test dataset
• 9. Creating the train and test dataset.srt
• 7. Transfer learning and fine tuning
• 1. Plan of attack
• 1. Plan of attack.srt
• 1.1 Source code – Google Colab File.html
• 1.2 Homework solution.html
• 10. Fine tuning – implementation and evaluation
• 10. Fine tuning – implementation and evaluation.srt
• 11. Saving, loading and classifying one single image
• 11. Saving, loading and classifying one single image.srt
• 12. HOMEWORK.html
• 13. Homework solution
• 13. Homework solution.srt
• 14. Additional reading.html
• 2. Transfer learning – intuition
• 2. Transfer learning – intuition.srt
• 3. Importing the libraries and dataset
• 3. Importing the libraries and dataset.srt
• 4. Creating the train and test dataset
• 4. Creating the train and test dataset.srt
• 5. Pre-trained neural network
• 5. Pre-trained neural network.srt
• 6. Creating the custom dense layer
• 6. Creating the custom dense layer.srt
• 7. Building and training the neural network
• 7. Building and training the neural network.srt
• 8. Evaluating the neural network
• 8. Evaluating the neural network.srt
• 9. Fine tuning – intuition
• 9. Fine tuning – intuition.srt
• 8. Neural networks for classification of emotions
• 1. Plan of attack
• 1. Plan of attack.srt
• 1.1 Homerwork solution.html
• 1.2 Source code – Google Colab File.html
• 10. HOMEWORK.html
• 11. Homework solution
• 11. Homework solution.srt
• 12. Additional reading.html
• 2. Importing the libraries and images
• 2. Importing the libraries and images.srt
• 3. Creating the train and test dataset
• 3. Creating the train and test dataset.srt
• 4. Building and training the neural network
• 4. Building and training the neural network.srt
• 5. Saving and loading the model
• 5. Saving and loading the model.srt
• 6. Evaluating the neural network
• 6. Evaluating the neural network.srt
• 7. Classifying one single image
• 7. Classifying one single image.srt
• 8. Classifying multiple images
• 8. Classifying multiple images.srt
• 9. Classifying emotions in videos
• 9. Classifying emotions in videos.srt
• 9. Autoencoders
• 1. Plan of attack
• 1. Plan of attack.srt
• 1.1 Source code – Google Colab File.html
• 1.2 Homework solution.html
• 10. Convolutional autoencoders 1
• 10. Convolutional autoencoders 1.srt
• 11. Convolutional autoencoders 2
• 11. Convolutional autoencoders 2.srt
• 12. Convolutional autoencoders 3
• 12. Convolutional autoencoders 3.srt
• 13. Convolutional autoencoders 4
• 13. Convolutional autoencoders 4.srt
• 14. HOMEWORK.html
• 15. Homework solution
• 15. Homework solution.srt
• 16. Additional reading.html
• 2. Autoencoders – intuition
• 2. Autoencoders – intuition.srt
• 3. Importing the libraries and dataset
• 3. Importing the libraries and dataset.srt
• 4. Visualizing the images
• 4. Visualizing the images.srt
• 5. Preprocessing the images
• 5. Preprocessing the images.srt
• 6. Building and training a linear autoencoder
• 6. Building and training a linear autoencoder.srt
• 7. Encoding the images
• 7. Encoding the images.srt
• 8. Decoding the images
• 8. Decoding the images.srt
• 9. Encoding and decoding the test images
• 9. Encoding and decoding the test images.srt