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| Archived
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4 - Introducing Convolutional Neural Networks\24 - Pooling Layers.mp4
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4 - Introducing Convolutional Neural Networks\20 - Module Overview.mp4
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4 - Introducing Convolutional Neural Networks\22 - Understanding Convolution.mp4
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4 - Introducing Convolutional Neural Networks\21 - Local Receptive Fields.mp4
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4 - Introducing Convolutional Neural Networks\27 - Module Summary.mp4
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4 - Introducing Convolutional Neural Networks\25 - Typical CNN Architecture.mp4
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4 - Introducing Convolutional Neural Networks\26 - Applying Convolutional and Pooling Layers.mp4
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4 - Introducing Convolutional Neural Networks\23 - Convolutional Layers.mp4
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image-classification-pytorch.zip |
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1 - Course Overview\01 - Course Overview.mp4
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6 - Optimizing Image Classification with Hyperparameter Tuning\40 - Setting up the CNN.mp4
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6 - Optimizing Image Classification with Hyperparameter Tuning\41 - Training the CNN.mp4
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6 - Optimizing Image Classification with Hyperparameter Tuning\46 - Module Summary.mp4
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6 - Optimizing Image Classification with Hyperparameter Tuning\39 - Preparing the CIFAR-10 Dataset.mp4
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6 - Optimizing Image Classification with Hyperparameter Tuning\44 - Choosing Convolution Kernel Sizes.mp4
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6 - Optimizing Image Classification with Hyperparameter Tuning\38 - Module Overview.mp4
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6 - Optimizing Image Classification with Hyperparameter Tuning\43 - Choosing Pooling Layers.mp4
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6 - Optimizing Image Classification with Hyperparameter Tuning\45 - Additional Convolution Layers and Different Kernel Size.mp4
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6 - Optimizing Image Classification with Hyperparameter Tuning\42 - Choosing Different Activation Functions.mp4
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7 - Performing Image Classification with Pre-trained Models\49 - Using the Resnet-18 Pretrained Model.mp4
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7 - Performing Image Classification with Pre-trained Models\50 - The Train Function to Find the Best Model Weights.mp4
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7 - Performing Image Classification with Pre-trained Models\47 - Module Overview.mp4
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7 - Performing Image Classification with Pre-trained Models\51 - Predictions Using Pretrained Models.mp4
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7 - Performing Image Classification with Pre-trained Models\53 - Summary and Further Study.mp4
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7 - Performing Image Classification with Pre-trained Models\52 - Cleaning up Resources.mp4
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7 - Performing Image Classification with Pre-trained Models\48 - Transfer Learning.mp4
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3 - Understanding the Drawbacks of Using Deep Neural Networks with Images\14 - Module Overview.mp4
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3 - Understanding the Drawbacks of Using Deep Neural Networks with Images\19 - Module Summary.mp4
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3 - Understanding the Drawbacks of Using Deep Neural Networks with Images\18 - Training a Fully Connected Image Classification Model.mp4
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3 - Understanding the Drawbacks of Using Deep Neural Networks with Images\15 - Deep Neural Networks to Work with Images.mp4
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3 - Understanding the Drawbacks of Using Deep Neural Networks with Images\16 - Loading and Processing MNIST Images.mp4
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3 - Understanding the Drawbacks of Using Deep Neural Networks with Images\17 - Setting up a Fully Connected Neural Network for Image Classification.mp4
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5 - Building Convolutional Neural Networks for Image Classification\28 - Module Overview.mp4
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5 - Building Convolutional Neural Networks for Image Classification\36 - Hyperparameter Tuning.mp4
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5 - Building Convolutional Neural Networks for Image Classification\29 - Zero Padding and Stride Size.mp4
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5 - Building Convolutional Neural Networks for Image Classification\32 - Feature Map Size Calculations.mp4
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5 - Building Convolutional Neural Networks for Image Classification\31 - Activation Functions.mp4
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5 - Building Convolutional Neural Networks for Image Classification\30 - Batch Normalization.mp4
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5 - Building Convolutional Neural Networks for Image Classification\37 - Module Summary.mp4
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5 - Building Convolutional Neural Networks for Image Classification\34 - Setting up a Convolutional Neural Network.mp4
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5 - Building Convolutional Neural Networks for Image Classification\35 - Training a CNN.mp4
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5 - Building Convolutional Neural Networks for Image Classification\33 - Preparing and Exploring Image Data.mp4
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2 - Preprocessing Images to Use in Machine Learning Models\08 - Cropping and Denoising Images.mp4
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2 - Preprocessing Images to Use in Machine Learning Models\10 - ZCA Whitening to Decorrelate Features.mp4
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2 - Preprocessing Images to Use in Machine Learning Models\11 - Image Transformations Using PyTorch Libraries.mp4
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2 - Preprocessing Images to Use in Machine Learning Models\03 - Prerequisites and Course Outline.mp4
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2 - Preprocessing Images to Use in Machine Learning Models\07 - Image Preprocessing - Resizing and Rescaling Images.mp4
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2 - Preprocessing Images to Use in Machine Learning Models\13 - Module Summary.mp4
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2 - Preprocessing Images to Use in Machine Learning Models\05 - Preprocessing Images to Train Robust Models.mp4
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2 - Preprocessing Images to Use in Machine Learning Models\12 - Normalizing Images Using Mean and Standard Deviation.mp4
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2 - Preprocessing Images to Use in Machine Learning Models\04 - Single Channel and Multichannel Images.mp4
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2 - Preprocessing Images to Use in Machine Learning Models\02 - Module Overview.mp4
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2 - Preprocessing Images to Use in Machine Learning Models\09 - Standardizing Images in PyTorch.mp4
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2 - Preprocessing Images to Use in Machine Learning Models\06 - Setting up a Deep Learning VM.mp4
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