Master Deep Learning Computer Vision™ CNN, SSD, YOLO & GANs
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       Video Length : 26h30m0s
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  • Curriculum
  • 1. Introduction
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      Course Introduction
      10m0s
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      Introduction to Computer Vision & Deep Learning
      10m0s
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      Introduction to Computer Vision & Deep Learning
      10m0s
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      What is Computer Vision and What Makes it Hard
      10m0s
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      What are Images?
      10m0s
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      Intro to OpenCV, OpenVINO™ & their Limitations
      10m0s
  • 2. Setup Your FREE Deep Learning Development Virtual Machine
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      Setting up your Deep Learning Virtual Machine (Download Code, VM & Slides here!)
      10m0s
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      Optional - Manual Setup of Ubuntu Virtual Machine
      10m0s
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      Optional - Setting up a shared drive with your Host OS
      10m0s
  • 3. Get Started! Handwriting Recognition, Simple Object Classification OpenCV Demo
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      Get Started! Handwriting Recognition, Simple Object Classification OpenCV Demo
      10m0s
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      Experiment with a Handwriting Classifier
      10m0s
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      Experiment with a Image Classifier
      10m0s
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      OpenCV Demo – Live Sketch with Webcam
      10m0s
  • 4. Free OpenCV Tutorial - Identify Shapes, Car and Pedestrian Detection
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      Setup OpenCV
      10m0s
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      What are Images?
      10m0s
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      How are Images Formed
      10m0s
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      Storing Images on Computers
      10m0s
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      Getting Started with OpenCV - A Brief OpenCV Intro
      10m0s
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      Grayscaling - Converting Color Images To Shades of Gray
      10m0s
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      Understanding Color Spaces - The Many Ways Color Images Are Stored Digitally
      10m0s
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      Histogram representation of Images - Visualizing the Components of Images
      10m0s
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      Creating Images & Drawing on Images - Make Squares, Circles, Polygons & Add Text
      10m0s
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      Transformations, Affine And Non-Affine - The Many Ways We Can Change Images
      10m0s
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      Image Translations - Moving Images Up, Down. Left And Right
      10m0s
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      Rotations - How To Spin Your Image Around And Do Horizontal Flipping
      10m0s
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      Scaling, Re-sizing and Interpolations - Understand How Re-Sizing Affects Quality
      10m0s
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      Image Pyramids - Another Way of Re-Sizing
      10m0s
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      Cropping - Cut Out The Image The Regions You Want or Don't Want
      10m0s
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      Arithmetic Operations - Brightening and Darkening Images
      10m0s
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      Bitwise Operations - How Image Masking Works
      10m0s
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      Blurring - The Many Ways We Can Blur Images & Why It's Important
      10m0s
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      Sharpening - Reverse Your Images Blurs
      10m0s
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      Thresholding (Binarization) - Making Certain Images Areas Black or White
      10m0s
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      Dilation, Erosion, Opening/Closing - Importance of Thickening/Thinning Lines
      10m0s
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      Edge Detection using Image Gradients & Canny Edge Detection
      10m0s
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      Perspective & Affine Transforms - Take An Off Angle Shot & Make It Look Top Down
      10m0s
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      Mini Project 1 - Live Sketch App - Turn your Webcam Feed Into A Pencil Drawing
      10m0s
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      Segmentation and Contours - Extract Defined Shapes In Your Image
      10m0s
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      Sorting Contours - Sort Those Shapes By Size
      10m0s
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      Approximating Contours & Finding Their Convex Hull - Clean Up Messy Contours
      10m0s
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      Matching Contour Shapes - Match Shapes In Images Even When Distorted
      10m0s
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      Mini Project 2 - Identify Shapes (Square, Rectangle, Circle, Triangle & Stars)
      10m0s
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      Line Detection - Detect Straight Lines E.g. The Lines On A Sudoku Game
      10m0s
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      Circle Detection
      10m0s
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      Blob Detection - Detect The Center of Flowers
      10m0s
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      Mini Project 3 - Counting Circles and Ellipses
      10m0s
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      Object Detection Overview
      10m0s
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      Feature Description Theory - How We Digitally Represent Objects
      10m0s
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      Finding Corners - Why Corners In Images Are Important to Object Detection
      10m0s
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      SIFT, SURF, FAST, BRIEF & ORB - Learn The Different Ways To Get Image Features
      10m0s
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      Mini Project 5 - Object Detection - Detect A Specific Object Using Your Webcam
      10m0s
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      Histogram of Oriented Gradients - Another Novel Way Of Representing Images
      10m0s
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      HAAR Cascade Classifiers - Learn How Classifiers Work And Why They're Amazing
      10m0s
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      Face and Eye Detection - Detect Human Faces and Eyes In Any Image
      10m0s
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      Mini Project 6 - Car and Pedestrian Detection in Videos
      10m0s
  • 5. Neural Networks Explained
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      Neural Networks Chapter Overview
      10m0s
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      Machine Learning Overview
      10m0s
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      Neural Networks Explained
      10m0s
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      Forward Propagation
      10m0s
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      Activation Functions
      10m0s
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      Training Part 1 – Loss Functions
      10m0s
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      Training Part 2 – Backpropagation and Gradient Descent
      10m0s
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      Backpropagation & Learning Rates – A Worked Example
      10m0s
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      Regularization, Overfitting, Generalization and Test Datasets
      10m0s
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      Epochs, Iterations and Batch Sizes
      10m0s
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      Measuring Performance and the Confusion Matrix
      10m0s
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      Review and Best Practices
      10m0s
  • 6. Convolutional Neural Networks (CNNs) Explained
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      Introduction to Convolutional Neural Networks (CNNs)
      10m0s
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      Convolutions & Image Features
      10m0s
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      Convolutions & Image Features
      10m0s
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      Depth, Stride and Padding
      10m0s
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      ReLU
      10m0s
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      Pooling
      10m0s
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      The Fully Connected Layer
      10m0s
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      Training CNNs
      10m0s
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      Designing Your Own CNN
      10m0s
  • 7. CNNs in Keras: Build a Handwritten Digit Classifier & a simple Image Classifier
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      Building a CNN in Keras
      10m0s
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      Introduction to Keras & Tensorflow
      10m0s
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      Building a Handwriting Recognition CNN
      10m0s
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      Loading Our Data
      10m0s
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      Getting our data in ‘Shape’
      10m0s
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      Hot One Encoding
      10m0s
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      Building & Compiling Our Model
      10m0s
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      Training Our Classifier
      10m0s
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      Plotting Loss and Accuracy Charts
      10m0s
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      Saving and Loading Your Model
      10m0s
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      Displaying Your Model Visually
      10m0s
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      Building a Simple Image Classifier using CIFAR10
      10m0s
  • 8. Visualizing What CNNs 'see' & Filter Visualizations
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      Introduction to Visualizing What CNNs 'see' & Filter Visualizations
      10m0s
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      Saliency Maps & Class Activation Maps
      10m0s
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      Saliency Maps & Class Activation Maps
      10m0s
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      Filter Visualizations
      10m0s
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      Heat Map Visualizations of Class Activations
      10m0s
  • 9. Data Augmentation: Build a Cats vs Dogs Classifier
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      Data Augmentation Chapter Overview
      10m0s
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      Splitting Data into Test and Training Datasets
      10m0s
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      Build a Cats vs. Dogs Classifier
      10m0s
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      Boosting Accuracy with Data Augmentation
      10m0s
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      Types of Data Augmentation
      10m0s
  • 10. Confusion Matrix, Classification Report & Viewing Misclassifications
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      Introduction to the Confusion Matrix & Viewing Misclassifications
      10m0s
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      Understanding the Confusion Matrix
      10m0s
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      Finding and Viewing Misclassified Data
      10m0s
  • 11. Types of Optimizers, Learning Rates & Callbacks: Build a Fruit Classifier
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      Introduction to the types of Optimizers, Learning Rates & Callbacks
      10m0s
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      Types Optimizers and Adaptive Learning Rate Methods
      10m0s
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      Keras Callbacks and Checkpoint, Early Stopping and Adjust Learning Rates that Pl
      10m0s
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      Build a Fruit Classifier
      10m0s
  • 12. Build LeNet, AlexNet in Keras, Batch Normalization: Build a Fashion Classifier
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      Intro to Building LeNet, AlexNet in Keras & Understand Batch Normalization
      10m0s
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      Build LeNet and test on MNIST
      10m0s
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      Build AlexNet and test on CIFAR10
      10m0s
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      Batch Normalization
      10m0s
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      Build a Clothing & Apparel Classifier (Fashion MNIST)
      10m0s
  • 13. ImageNet & using pre-trained Models in Keras (VG16, VG19, InceptionV3, ResNet50)
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      Chapter Introduction
      10m0s
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      ImageNet - Experimenting with pre-trained Models in Keras (VGG16, ResNet50, Mobi
      10m0s
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      Understanding VGG16 and VGG19
      10m0s
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      Understanding ResNet50
      10m0s
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      Understanding InceptionV3
      10m0s
  • 14. Transfer Learning and Fine Tuning: Build a Flower and Monkey Breed Classifier
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      Chapter Introduction
      10m0s
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      What is Transfer Learning and Fine Tuning
      10m0s
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      Build a Flower Classifier with VGG16 using Transfer Learning
      10m0s
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      Build a Monkey Breed Identified with MobileNet using Transfer Learning
      10m0s
  • 15. Design Your Own CNN - LittleVGG: Build a Simpsons Character Classifier
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      Chapter Introduction
      10m0s
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      Introducing LittleVGG
      10m0s
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      Simpsons Character Recognition using LittleVGG
      10m0s
  • 16. Advanced Activation Functions and Initializations
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      Chapter Introduction
      10m0s
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      Dying ReLU Problem and Introduction to Leaky ReLU, ELU and PReLUs
      10m0s
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      Advanced Initializations
      10m0s
  • 17. Deep Surveillance: Build a Facial Emotion, Age & Gender Recognition System
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      Chapter Introduction
      10m0s
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      Build an Emotion, Facial Expression Detector
      10m0s
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      Build Emotion/Age/Gender Recognition in our Deep Surveillance Monitor
      10m0s
  • 18. Image Segmentation & Medical Imaging in U-Net: Find Nuclei in Images
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      Chapter Overview on Image Segmentation & Medical Imaging in U-Net
      10m0s
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      What is Segmentation? And Applications in Medical Imaging
      10m0s
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      U-Net: Image Segmentation with CNNs
      10m0s
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      The Intersection over Union (IoU) Metric
      10m0s
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      Finding the Nuclei in Divergent Images
      10m0s
  • 19. Principles of Object Detection
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      Chapter Introduction
      10m0s
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      Object Detection Introduction - Sliding Windows with HOGs
      10m0s
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      R-CNN, Fast R-CNN, Faster R-CNN and Mask R-CNN
      10m0s
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      Single Shot Detectors (SSDs)
      10m0s
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      YOLO to YOLOv3
      10m0s
  • 20. TensorFlow Object Detection API
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      Chapter Introduction
      10m0s
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      TFOD API Install and Setup
      10m0s
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      Experiment with a ResNet SSD on images, webcam and videos
      10m0s
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      How to Train a TFOD Model
      10m0s
  • 21. Object Detection with YOLO & Darkflow: Build a London Underground Sign Detector
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      Chapter Introduction
      10m0s
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      Setting up and install Yolo DarkNet and DarkFlow
      10m0s
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      Experiment with YOLO on still images, webcam and videos
      10m0s
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      Build your own YOLO Object Detector - Detecting London Underground Signs
      10m0s
  • 22. DeepDream & Neural Style Transfers: Make AI Generated Art
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      Chapter Introduction
      10m0s
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      DeepDream – How AI Generated Art All Started
      10m0s
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      Neural Style Transfer
      10m0s
  • 23. Generative Adversarial Networks (GANs): Age Faces to 60+ Age with our Age-cGAN
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      Generative Adverserial Neural Networks Chapter Overview
      10m0s
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      Introduction To GANs
      10m0s
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      Mathematics of GANs
      10m0s
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      Implementing GANs in Keras
      10m0s
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      Face Aging GAN
      10m0s
  • 24. The Computer Vision World
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      Chapter Introduction
      10m0s
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      Alternative Frameworks: PyTorch, MXNet, Caffe, Theano & OpenVINO
      10m0s
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      Popular APIs Google, Microsoft, ClarifAI Amazon Rekognition and others
      10m0s
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      Popular Computer Vision Conferences & Finding Datasets
      10m0s
    • videocam
      Building a Deep Learning Machine vs. Cloud GPUs
      10m0s
Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

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Master Deep Learning Computer Vision™ CNN, SSD, YOLO & GANs


Master Deep Learning Computer Vision™ CNN, SSD, YOLO & GANs

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