Deep Learning: Convolutional Neural Networks

كورس لتعليم اساسيات التعلم العميق والشبكات العصبية الالتفافية للمبتدئين وحتى المستوى المتقدمسواء كنت طالباً فى علوم الحاسب او طالباً فى الهندسة أو مبرمجاً وتعشق مجال الذكاء الاصطناعى , فإن هذا الكورس سيساعدك علي فهم أساسيات التعلم الشبكات العصبيه الالتفافية و الوصول إلى مستوى محترف وسوف يركز هذا الكورس على الجوانب النظرية وراء الخوارزميات والنماذج المنتشره هذه الايام للتعلم العميقThis course is focus on the theoretical aspects of the recent convolutional neural network based methods. ######################################################################################################################################Section 1: Introduction to Convolutional Neural Network (CNN)Lecture 1: Introduction to Deep LearningLecture 2: ImageNet ChallengeLecture 3: Drawbacks of Previous Neural NetworksLecture 4: CNN Motivation & HistorySection 2: Convolutional Neural Network PropertiesLecture 5: Local ConnectivityLecture 6: Parameter SharingLecture 7: Pooling & SubsamplingSection 3: Convolution OperationLecture 8: Definition of ConvolutionLecture 9: Image Convolution ExampleLecture 10: Other FiltersSection 4: Convolutional Neural Network LayersLecture 11: Convolutional LayerLecture 12: Strided ConvolutionLecture 13: Strided Convolution with PaddingLecture 14: Convolution over VolumeLecture 15: Activation Function (ReLU)Lecture 16: Pooling LayerLecture 17: Convolutional NetworkLecture 18: BatchNormalization LayerSection 5: Convolutional Neural Network ArchitecturesLecture 19: Introduction to CNN ArchitecturesLecture 20: LeNet-5Lecture 21: AlexNet & ZFNetLecture 22: VGGNetLecture 23: GoogleNet (Inception Network)Lecture 24: Inception V2, V3, V4, Inception-ResNet-v1, Inception-ResNet-v2Lecture 25: XceptionLecture 26: Residual Neural Network (ResNet)Lecture 27: DenseNetSection 6: CNN for Object DetectionLecture 28: Computer Vision TasksLecture 29: Introduction to Object Localization and DetectionLecture 30: Classification + LocalizationLecture 31: Object Detection with Sliding WindowLecture 32: R-CNNLecture 33: Fast R-CNNLecture 34: Faster R-CNNLecture 35: You only look once (YOLO)Section 7: CNN for Instance SegmentationLecture 36: Instance SegmentationLecture 37: Mask R-CNNSection 8: CNN for Semantic SegmentationLecture 38: Semantic SegmentationLecture 39: Semantic Segmentation with Sliding WindowLecture 40: Fully Convolutional NetworkLecture 41: Up-sampling with Transposed ConvolutionLecture 42: Fully Convolutional Network: Skipping Connections.