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[Tutorialsplanet.NET] Udemy - Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs

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Tutorialsplanet NET Udemy - Deep Learning Computer Vision™ CNN OpenCV YOLO SSD & GANs

文件列表
  1. 1. Introduction/1. Course Introduction.mp490.2MB
  2. 10. Data Augmentation Build a Cats vs Dogs Classifier/1. Data Augmentation Chapter Overview.mp43.93MB
  3. 10. Data Augmentation Build a Cats vs Dogs Classifier/2. Splitting Data into Test and Training Datasets.mp4103.82MB
  4. 10. Data Augmentation Build a Cats vs Dogs Classifier/3. Train a Cats vs. Dogs Classifier.mp444.78MB
  5. 10. Data Augmentation Build a Cats vs Dogs Classifier/4. Boosting Accuracy with Data Augmentation.mp443.5MB
  6. 10. Data Augmentation Build a Cats vs Dogs Classifier/5. Types of Data Augmentation.mp452.5MB
  7. 11/1. Introduction to the Confusion Matrix & Viewing Misclassifications.mp42.53MB
  8. 11/2. Understanding the Confusion Matrix.mp493.02MB
  9. 11/3. Finding and Viewing Misclassified Data.mp444.36MB
  10. 12/1. Introduction to the types of Optimizers, Learning Rates & Callbacks.mp43.43MB
  11. 12/2. Types Optimizers and Adaptive Learning Rate Methods.mp467.23MB
  12. 12/3. Keras Callbacks and Checkpoint, Early Stopping and Adjust Learning Rates that Pl.mp451.14MB
  13. 12/4. Build a Fruit Classifier.mp492.94MB
  14. 13/1. Intro to Building LeNet, AlexNet in Keras & Understand Batch Normalization.mp42.76MB
  15. 13/2. Build LeNet and test on MNIST.mp432.12MB
  16. 13/3. Build AlexNet and test on CIFAR10.mp442.16MB
  17. 13/4. Batch Normalization.mp423.16MB
  18. 13/5. Build a Clothing & Apparel Classifier (Fashion MNIST).mp456.33MB
  19. 14/1. Chapter Introduction.mp42.9MB
  20. 14/2. ImageNet - Experimenting with pre-trained Models in Keras (VGG16, ResNet50, Mobi.mp482.05MB
  21. 14/3. Understanding VGG16 and VGG19.mp414.44MB
  22. 14/4. Understanding ResNet50.mp49.76MB
  23. 14/5. Understanding InceptionV3.mp414.35MB
  24. 15/1. Chapter Introduction.mp42.26MB
  25. 15/2. What is Transfer Learning and Fine Tuning.mp444.91MB
  26. 15/3. Build a Monkey Breed Classifier with MobileNet using Transfer Learning.mp4135.31MB
  27. 15/4. Build a Flower Classifier with VGG16 using Transfer Learning.mp481.96MB
  28. 16. Design Your Own CNN - LittleVGG Build a Simpsons Character Classifier/1. Chapter Introduction.mp41.85MB
  29. 16. Design Your Own CNN - LittleVGG Build a Simpsons Character Classifier/2. Introducing LittleVGG.mp411.46MB
  30. 16. Design Your Own CNN - LittleVGG Build a Simpsons Character Classifier/3. Simpsons Character Recognition using LittleVGG.mp499.6MB
  31. 17. Advanced Activation Functions and Initializations/1. Chapter Introduction.mp42.08MB
  32. 17. Advanced Activation Functions and Initializations/2. Dying ReLU Problem and Introduction to Leaky ReLU, ELU and PReLUs.mp432.33MB
  33. 17. Advanced Activation Functions and Initializations/3. Advanced Initializations.mp414.96MB
  34. 18/1. Chapter Introduction.mp44.57MB
  35. 18/2. Build an Emotion, Facial Expression Detector.mp4201.96MB
  36. 18/3. Build EmotionAgeGender Recognition in our Deep Surveillance Monitor.mp4260.78MB
  37. 19/1. Chapter Overview on Image Segmentation & Medical Imaging in U-Net.mp42.93MB
  38. 19/2. What is Segmentation And Applications in Medical Imaging.mp429.64MB
  39. 19/3. U-Net Image Segmentation with CNNs.mp430.46MB
  40. 19/4. The Intersection over Union (IoU) Metric.mp442.96MB
  41. 19/5. Finding the Nuclei in Divergent Images.mp4171.06MB
  42. 2. Introduction to Computer Vision & Deep Learning/1. Introduction to Computer Vision & Deep Learning.mp43.12MB
  43. 2. Introduction to Computer Vision & Deep Learning/2. What is Computer Vision and What Makes it Hard.mp460.12MB
  44. 2. Introduction to Computer Vision & Deep Learning/3. What are Images.mp458.79MB
  45. 2. Introduction to Computer Vision & Deep Learning/4. Intro to OpenCV, OpenVINO™ & their Limitations.mp441.45MB
  46. 20. Principles of Object Detection/1. Chapter Introduction.mp43.4MB
  47. 20. Principles of Object Detection/2. Object Detection Introduction - Sliding Windows with HOGs.mp451.65MB
  48. 20. Principles of Object Detection/3. R-CNN, Fast R-CNN, Faster R-CNN and Mask R-CNN.mp4135.87MB
  49. 20. Principles of Object Detection/4. Single Shot Detectors (SSDs).mp412.45MB
  50. 20. Principles of Object Detection/5. YOLO to YOLOv3.mp433.08MB
  51. 21. TensorFlow Object Detection API/1. Chapter Introduction.mp42.45MB
  52. 21. TensorFlow Object Detection API/2. TFOD API Install and Setup.mp447.56MB
  53. 21. TensorFlow Object Detection API/3. Experiment with a ResNet SSD on images, webcam and videos.mp483.55MB
  54. 21. TensorFlow Object Detection API/4. How to Train a TFOD Model.mp474.21MB
  55. 22/1. Chapter Introduction.mp42.53MB
  56. 22/2. Setting up and install Yolo DarkNet and DarkFlow.mp451.66MB
  57. 22/3. Experiment with YOLO on still images, webcam and videos.mp4104.48MB
  58. 22/4. Build your own YOLO Object Detector - Detecting London Underground Signs.mp4159.11MB
  59. 23. DeepDream & Neural Style Transfers Make AI Generated Art/1. Chapter Introduction.mp41.5MB
  60. 23. DeepDream & Neural Style Transfers Make AI Generated Art/2. DeepDream – How AI Generated Art All Started.mp483.97MB
  61. 23. DeepDream & Neural Style Transfers Make AI Generated Art/3. Neural Style Transfer.mp4124.75MB
  62. 24/1. Generative Adverserial Neural Networks Chapter Overview.mp44.65MB
  63. 24/2. Introduction To GANs.mp485.34MB
  64. 24/3. Mathematics of GANs.mp427.16MB
  65. 24/4. Implementing GANs in Keras.mp496.35MB
  66. 24/5. Face Aging GAN.mp446.76MB
  67. 26. The Computer Vision World/1. Chapter Introduction.mp43.2MB
  68. 26. The Computer Vision World/2. Alternative Frameworks PyTorch, MXNet, Caffe, Theano & OpenVINO.mp422.98MB
  69. 26. The Computer Vision World/3. Popular APIs Google, Microsoft, ClarifAI Amazon Rekognition and others.mp48.58MB
  70. 26. The Computer Vision World/4. Popular Computer Vision Conferences & Finding Datasets.mp419.59MB
  71. 26. The Computer Vision World/5. Building a Deep Learning Machine vs. Cloud GPUs.mp428.22MB
  72. 3/1. Setting up your Deep Learning Virtual Machine (Download Code, VM & Slides here!).mp477.4MB
  73. 4/1. Get Started! Handwriting Recognition, Simple Object Classification OpenCV Demo.mp47.48MB
  74. 4/2. Experiment with a Handwriting Classifier.mp467.3MB
  75. 4/3. Experiment with a Image Classifier.mp427.3MB
  76. 4/4. OpenCV Demo – Live Sketch with Webcam.mp441.17MB
  77. 5. OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection/1. Setup OpenCV.mp413.91MB
  78. 5. OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection/14. Image Pyramids - Another Way of Re-Sizing.mp414.22MB
  79. 5. OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection/17. Bitwise Operations - How Image Masking Works.mp428.88MB
  80. 5. OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection/19. Sharpening - Reverse Your Images Blurs.mp417.33MB
  81. 5. OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection/2. What are Images.mp415.99MB
  82. 5. OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection/26. Sorting Contours - Sort Those Shapes By Size.mp4106.17MB
  83. 5. OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection/3. How are Images Formed.mp421.25MB
  84. 5. OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection/32. Blob Detection - Detect The Center of Flowers.mp432.23MB
  85. 5. OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection/33. Mini Project 3 - Counting Circles and Ellipses.mp451.89MB
  86. 5. OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection/34. Object Detection Overview.mp430.8MB
  87. 5. OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection/4. Storing Images on Computers.mp442.31MB
  88. 5. OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection/5. Getting Started with OpenCV - A Brief OpenCV Intro.mp474.9MB
  89. 6. Neural Networks Explained in Detail/1. Neural Networks Chapter Overview.mp47.83MB
  90. 6. Neural Networks Explained in Detail/10. Epochs, Iterations and Batch Sizes.mp426.07MB
  91. 6. Neural Networks Explained in Detail/11. Measuring Performance and the Confusion Matrix.mp452.07MB
  92. 6. Neural Networks Explained in Detail/12. Review and Best Practices.mp427.13MB
  93. 6. Neural Networks Explained in Detail/2. Machine Learning Overview.mp452.28MB
  94. 6. Neural Networks Explained in Detail/3. Neural Networks Explained.mp423.34MB
  95. 6. Neural Networks Explained in Detail/4. Forward Propagation.mp463.34MB
  96. 6. Neural Networks Explained in Detail/5. Activation Functions.mp459.62MB
  97. 6. Neural Networks Explained in Detail/6. Training Part 1 – Loss Functions.mp458.41MB
  98. 6. Neural Networks Explained in Detail/7. Training Part 2 – Backpropagation and Gradient Descent.mp472.62MB
  99. 6. Neural Networks Explained in Detail/8. Backpropagation & Learning Rates – A Worked Example.mp499.84MB
  100. 6. Neural Networks Explained in Detail/9. Regularization, Overfitting, Generalization and Test Datasets.mp4118.38MB
  101. 7. Convolutional Neural Networks (CNNs) Explained in Detail/1. Convolutional Neural Networks Chapter Overview.mp45.14MB
  102. 7. Convolutional Neural Networks (CNNs) Explained in Detail/2. Convolutional Neural Networks Introduction.mp436.66MB
  103. 7. Convolutional Neural Networks (CNNs) Explained in Detail/3. Convolutions & Image Features.mp4102.35MB
  104. 7. Convolutional Neural Networks (CNNs) Explained in Detail/4. Depth, Stride and Padding.mp446.52MB
  105. 7. Convolutional Neural Networks (CNNs) Explained in Detail/5. ReLU.mp410.88MB
  106. 7. Convolutional Neural Networks (CNNs) Explained in Detail/6. Pooling.mp428.82MB
  107. 7. Convolutional Neural Networks (CNNs) Explained in Detail/7. The Fully Connected Layer.mp413.84MB
  108. 7. Convolutional Neural Networks (CNNs) Explained in Detail/8. Training CNNs.mp427.15MB
  109. 7. Convolutional Neural Networks (CNNs) Explained in Detail/9. Designing Your Own CNN.mp424.24MB
  110. 8. Build CNNs in Python using Keras - Handwriting Recognition (MNIST)/1. Building a CNN in Keras.mp45.62MB
  111. 8. Build CNNs in Python using Keras - Handwriting Recognition (MNIST)/10. Saving and Loading Your Model.mp429.51MB
  112. 8. Build CNNs in Python using Keras - Handwriting Recognition (MNIST)/11. Displaying Your Model Visually.mp425.44MB
  113. 8. Build CNNs in Python using Keras - Handwriting Recognition (MNIST)/12. Building a Simple Image Classifier using CIFAR10.mp474.35MB
  114. 8. Build CNNs in Python using Keras - Handwriting Recognition (MNIST)/2. Introduction to Keras & Tensorflow.mp473.69MB
  115. 8. Build CNNs in Python using Keras - Handwriting Recognition (MNIST)/3. Building a Handwriting Recognition CNN.mp411.14MB
  116. 8. Build CNNs in Python using Keras - Handwriting Recognition (MNIST)/4. Loading Our Data.mp452.92MB
  117. 8. Build CNNs in Python using Keras - Handwriting Recognition (MNIST)/5. Getting our data in ‘Shape’.mp433.8MB
  118. 8. Build CNNs in Python using Keras - Handwriting Recognition (MNIST)/6. Hot One Encoding.mp418.18MB
  119. 8. Build CNNs in Python using Keras - Handwriting Recognition (MNIST)/7. Building & Compiling Our Model.mp436.21MB
  120. 8. Build CNNs in Python using Keras - Handwriting Recognition (MNIST)/8. Training Our Classifier.mp440.78MB
  121. 8. Build CNNs in Python using Keras - Handwriting Recognition (MNIST)/9. Plotting Loss and Accuracy Charts.mp425.57MB
  122. 9/1. Introduction to Visualizing What CNNs 'see' & Filter Visualizations.mp48.25MB
  123. 9/2. Saliency Maps & Class Activation Maps.mp464.15MB
  124. 9/3. Saliency Maps & Class Activation Maps.mp480.78MB
  125. 9/4. Filter Visualizations.mp489.48MB
  126. 9/5. Heat Map Visualizations of Class Activations.mp434.21MB
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