| 001. Convolution Revisited.mp4 | 27.2 MB | ||
| 001. Course Overview.mp4 | 48.8 MB | ||
| 001. Example Setup.mp4 | 23.7 MB | ||
| 001. Gray-Scale Images.mp4 | 37.2 MB | ||
| 001. Image Classification Revisited.mp4 | 20.5 MB | ||
| 001. Introduction to Object Detection.mp4 | 35.7 MB | ||
| 001. Introduction to TensorFlow.mp4 | 29.5 MB | ||
| 001. LeNet.mp4 | 21.7 MB | ||
| 001. Neuron and Perceptron.mp4 | 44.5 MB | ||
| 001. Problem Setup.mp4 | 112.8 MB | ||
| 001. What Is Transfer learning.mp4 | 26.4 MB | ||
| 002. Classification Pipeline.mp4 | 43.9 MB | ||
| 002. DNN Architecture.mp4 | 27.3 MB | ||
| 002. FashionMNIST Example Plan Neural Network.mp4 | 89.4 MB | ||
| 002. Gray-Scale Images Quiz.mp4 | 4.2 MB | ||
| 002. Implementation TensorFlow Hub.mp4 | 42.6 MB | ||
| 002. Implementing Convolution in Python Revisited.mp4 | 25.6 MB | ||
| 002. Introduction to Instructor.mp4 | 13.7 MB | ||
| 002. LeNet Quiz.mp4 | 6.2 MB | ||
| 002. Project Implementation.mp4 | 71.5 MB | ||
| 002. Sliding Window Object Localization.mp4 | 34.2 MB | ||
| 002. Why Derivatives.mp4 | 38.9 MB | ||
| 002. Why Transfer Learning.mp4 | 39.1 MB | ||
| 003. Classification Pipeline Quiz.mp4 | 4.2 MB | ||
| 003. DNN Architecture Quiz.mp4 | 5.8 MB | ||
| 003. Face Verification Activity.mp4 | 4.6 MB | ||
| 003. FashionMNIST Example CNN.mp4 | 95.3 MB | ||
| 003. Gray-Scale Images Solution.mp4 | 4.9 MB | ||
| 003. ImageNet Challenge.mp4 | 42.6 MB | ||
| 003. LeNet Solution.mp4 | 8.7 MB | ||
| 003. Sliding Window Efficient Implementation.mp4 | 27.2 MB | ||
| 003. Thank You and Conclusion.mp4 | 12.3 MB | ||
| 003. Why CNN.mp4 | 82.8 MB | ||
| 003. Why Convolution.mp4 | 22 MB | ||
| 003. Why Derivatives Quiz.mp4 | 5.4 MB | ||
| 004. AlexNet.mp4 | 32.3 MB | ||
| 004. Classification Pipeline Solution.mp4 | 5.2 MB | ||
| 004. DNN Architecture Solution.mp4 | 7.1 MB | ||
| 004. Filters Padding Strides.mp4 | 27.9 MB | ||
| 004. Focus of the Course.mp4 | 21.9 MB | ||
| 004. Introduction to TensorFlow Activity.mp4 | 4.9 MB | ||
| 004. Practical Tips.mp4 | 22.7 MB | ||
| 004. RGB Images.mp4 | 30.4 MB | ||
| 004. Why Derivatives Solution.mp4 | 11 MB | ||
| 004. YOLO Introduction.mp4 | 34.7 MB | ||
| 005. FeedForward FullyConnected MLP.mp4 | 16.7 MB | ||
| 005. Padding Image.mp4 | 30.9 MB | ||
| 005. Project in TensorFlow.mp4 | 149.7 MB | ||
| 005. RGB Images Quiz.mp4 | 4.6 MB | ||
| 005. Sliding Window Implementation.mp4 | 25 MB | ||
| 005. VGG.mp4 | 19 MB | ||
| 005. What Is Chain Rule.mp4 | 29.5 MB | ||
| 005. YOLO Training Data Generation.mp4 | 27 MB | ||
| 006. Applying Chain Rule.mp4 | 38.5 MB | ||
| 006. Calculating Number of Weights of DNN.mp4 | 22.4 MB | ||
| 006. InceptionNet.mp4 | 30 MB | ||
| 006. Pooling Tensors.mp4 | 22.4 MB | ||
| 006. RGB Images Solution.mp4 | 5 MB | ||
| 006. Shift Scale Rotation Invariance.mp4 | 47 MB | ||
| 006. Transfer Learning Activity.mp4 | 5 MB | ||
| 006. YOLO Anchor Boxes.mp4 | 43.6 MB | ||
| 007. CNN Example.mp4 | 23 MB | ||
| 007. Calculating Number of Weights of DNN Quiz.mp4 | 4.5 MB | ||
| 007. GoogLeNet.mp4 | 18.4 MB | ||
| 007. Gradients of MaxPooling Layer.mp4 | 33.9 MB | ||
| 007. Reading and Showing Images in Python.mp4 | 37.8 MB | ||
| 007. Shift Scale Rotation Invariance Exercise.mp4 | 58.6 MB | ||
| 007. YOLO Algorithm.mp4 | 29.5 MB | ||
| 008. Calculating Number of Weights of DNN Solution.mp4 | 7.1 MB | ||
| 008. Convolution and Pooling Details.mp4 | 27 MB | ||
| 008. Gradients of MaxPooling Layer Quiz.mp4 | 5.8 MB | ||
| 008. Person Detection.mp4 | 43.1 MB | ||
| 008. Reading and Showing Images in Python Quiz.mp4 | 4.1 MB | ||
| 008. Resnet.mp4 | 31.3 MB | ||
| 008. YOLO Non-Maxima Suppression.mp4 | 32.6 MB | ||
| 009. Classical CNNs Activity.mp4 | 4.9 MB | ||
| 009. Gradients of MaxPooling Layer Solution.mp4 | 8.2 MB | ||
| 009. HOG Features.mp4 | 38 MB | ||
| 009. MaxPooling Exercise.mp4 | 11.7 MB | ||
| 009. Number of Neurons Versus Number of Layers.mp4 | 25.1 MB | ||
| 009. RCNN.mp4 | 12.4 MB | ||
| 009. Reading and Showing Images in Python Solution.mp4 | 4.4 MB | ||
| 010. Converting an Image to Grayscale in Python.mp4 | 29.9 MB | ||
| 010. Discriminative Versus Generative Learning.mp4 | 22.6 MB | ||
| 010. Gradients of Convolutional Layer.mp4 | 38 MB | ||
| 010. HOG Features Exercise.mp4 | 28.6 MB | ||
| 010. NonVectorized Implementations of Conv2d and Pool2d.mp4 | 69.2 MB | ||
| 010. YOLO Activity.mp4 | 5.3 MB | ||
| 011. Converting an Image to Grayscale in Python Quiz.mp4 | 4.4 MB | ||
| 011. Deep Neural Network Architecture Activity.mp4 | 7.9 MB | ||
| 011. Extending to Multiple Filters.mp4 | 16 MB | ||
| 011. Hand Engineering Versus CNNs.mp4 | 30.1 MB | ||
| 011. Universal Approximation Theorem.mp4 | 31.4 MB | ||
| 012. Converting an Image to Grayscale in Python Solution.mp4 | 5.4 MB | ||
| 012. Extending to Multiple Layers.mp4 | 27.4 MB | ||
| 012. Object Detection Activity.mp4 | 18.5 MB | ||
| 012. Why Depth.mp4 | 12.8 MB | ||
| 013. Decision Boundary in DNN.mp4 | 22.1 MB | ||
| 013. Extending to Multiple Layers Quiz.mp4 | 7.8 MB | ||
| 013. Image Formation.mp4 | 19.4 MB | ||
| 014. Decision Boundary in DNN Quiz.mp4 | 6 MB | ||
| 014. Extending to Multiple Layers Solution.mp4 | 26.1 MB | ||
| 014. Image Formation Quiz.mp4 | 4.4 MB | ||
| 015. Decision Boundary in DNN Solution.mp4 | 12.9 MB | ||
| 015. Image Formation Solution.mp4 | 4.5 MB | ||
| 015. Implementation in NumPy ForwardPass.mp4 | 30.7 MB | ||
| 016. BiasTerm.mp4 | 28.8 MB | ||
| 016. Image Blurring 1.mp4 | 45.6 MB | ||
| 016. Implementation in NumPy BackwardPass 1.mp4 | 25 MB | ||
| 017. BiasTerm Quiz.mp4 | 4.5 MB | ||
| 017. Image Blurring 1 Quiz.mp4 | 5.1 MB | ||
| 017. Implementation in NumPy BackwardPass 2.mp4 | 17.1 MB | ||
| 018. BiasTerm Solution.mp4 | 5.5 MB | ||
| 018. Image Blurring 1 Solution.mp4 | 4.5 MB | ||
| 018. Implementation in NumPy BackwardPass 3.mp4 | 32.6 MB | ||
| 019. Activation Function.mp4 | 30.4 MB | ||
| 019. Image Blurring 2.mp4 | 39.1 MB | ||
| 019. Implementation in NumPy BackwardPass 4.mp4 | 49.6 MB | ||
| 020. Activation Function Quiz.mp4 | 4.7 MB | ||
| 020. Image Blurring 2 Quiz.mp4 | 4.4 MB | ||
| 020. Implementation in NumPy BackwardPass 5.mp4 | 80.9 MB | ||
| 021. Activation Function Solution.mp4 | 6.3 MB | ||
| 021. Gradient Descent in CNNs Activity.mp4 | 7.8 MB | ||
| 021. Image Blurring 2 Solution.mp4 | 5.3 MB | ||
| 022. DNN Training Parameters.mp4 | 37.2 MB | ||
| 022. General Image Filtering.mp4 | 19.2 MB | ||
| 023. Convolution.mp4 | 22.8 MB | ||
| 023. DNN Training Parameters Quiz.mp4 | 4.6 MB | ||
| 024. DNN Training Parameters Solution.mp4 | 4.9 MB | ||
| 024. Edge Detection.mp4 | 29.2 MB | ||
| 025. Gradient Descent.mp4 | 29 MB | ||
| 025. Image Sharpening.mp4 | 19.6 MB | ||
| 026. Backpropagation.mp4 | 40.3 MB | ||
| 026. Implementation of Image Blurring Edge Detection Image Sharpening in Python.mp4 | 65 MB | ||
| 027. Parametric Shape Detection.mp4 | 34.2 MB | ||
| 027. Training DNN Animation.mp4 | 22.4 MB | ||
| 028. Image Processing.mp4 | 15.1 MB | ||
| 028. Weight Initialization.mp4 | 45 MB | ||
| 029. Image Processing Activity.mp4 | 10.8 MB | ||
| 029. Weight Initialization Quiz.mp4 | 4.6 MB | ||
| 030. Image Processing Activity Solution.mp4 | 44.6 MB | ||
| 030. Weight Initialization Solution.mp4 | 5.6 MB | ||
| 031. Batch MiniBatch Stochastic Gradient Descent.mp4 | 34.2 MB | ||
| 032. Batch Normalization.mp4 | 22 MB | ||
| 033. Rprop and Momentum.mp4 | 58 MB | ||
| 034. Rprop and Momentum Quiz.mp4 | 6 MB | ||
| 035. Rprop and Momentum Solution.mp4 | 6.5 MB | ||
| 036. Convergence Animation.mp4 | 32.2 MB | ||
| 037. DropOut, Early Stopping and Hyperparameters.mp4 | 51.1 MB | ||
| 038. DropOut, Early Stopping and Hyperparameters Quiz.mp4 | 5.8 MB | ||
| 039. DropOut, Early Stopping and Hyperparameters Solution.mp4 | 8.5 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 154 total files | |||
Deep Learning CNN: Convolutional Neural Networks with Python
https://FreeCourseWeb.com
Released 08/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 15h 27m | Size: 3.94 GB
Learn Convolution Neural Networks using TensorFlow, CNN for Image Recognition, and CNN for Object Detection. Understand the concepts and methodologies of CNNs with respect to data science with live coding throughout
Video description
Learn Convolution Neural Networks using TensorFlow, CNN for Image Recognition, and CNN for Object Detection. Understand the concepts and methodologies of CNNs with respect to data science with live coding throughout.
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