[FCSNEW.NET] Udemy - Complete Computer Vision Bootcamp With PyTorch & Tensorflow

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[FCSNEW.NET] Udemy - Complete Computer Vision Bootcamp With PyTorch & Tensorflow

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文件列表

  • 05. computer vision (Open CV With Python)/19. Image Segmentation Using openCV.mp4 690.3 MB
  • 06. PyTorch/16. CNN Training Using a Custom Dataset.mp4 561.3 MB
  • 02. Python Prerequisites/37. Pandas-DataFrame And Series.mp4 558.5 MB
  • 02. Python Prerequisites/36. Numpy In Python.mp4 545.7 MB
  • 02. Python Prerequisites/38. Data Manipulation With Pandas And Numpy.mp4 468.7 MB
  • 05. computer vision (Open CV With Python)/20. Haar Cascade for face detection.mp4 440.3 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/6. Vanishing Gradient Problem and Sigmoid.mp4 418.6 MB
  • 11. Image Segmentation/7. Implementing Custom Unet Training.mp4 415.2 MB
  • 02. Python Prerequisites/12. Sets In Python.mp4 412.7 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/3. ANN intuition and Working.mov.mp4 405.2 MB
  • 02. Python Prerequisites/9. Loops In Python.mp4 395.2 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/4. Back Propogation and Weight Updation.mp4 377.4 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/17. Loss Function Classification Problem.mp4 375.4 MB
  • 06. PyTorch/12. Create Linear Regression model with Pytorch components.mp4 370.6 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/1. Perceptron Intuition.mp4 342.8 MB
  • 11. Image Segmentation/5. Fully Convolutional Networks (FCNs).mp4 340.8 MB
  • 10. Basics of Object Detection/11. Custom Object Detection with YOLOv11.mp4 322.3 MB
  • 02. Python Prerequisites/8. Conditional Statements(if,elif,else).mp4 322.0 MB
  • 06. PyTorch/22. Implementing gradio app inference backend.mp4 321.8 MB
  • 02. Python Prerequisites/13. Dictionaries In Python.mp4 313.3 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/16. Regression Cost Function.mp4 299.6 MB
  • 11. Image Segmentation/8. Mask-RCNN.mp4 288.9 MB
  • 05. computer vision (Open CV With Python)/11. Affine.mp4 288.9 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/32. Convolution Operatuin In CNN.mp4 288.5 MB
  • 05. computer vision (Open CV With Python)/6. image Resizing, Scaling and interpolation.mp4 288.3 MB
  • 02. Python Prerequisites/39. Reading Data From Various Data Source Using Pandas.mp4 285.5 MB
  • 11. Image Segmentation/9. Training Yolov11 Instance Segmentation.mp4 284.1 MB
  • 02. Python Prerequisites/5. Variables In Python.mp4 280.7 MB
  • 06. PyTorch/14. Understanding components of custom data loader in pytorch.mp4 279.8 MB
  • 02. Python Prerequisites/40. Logging Practical Implementation In Python.mp4 266.5 MB
  • 06. PyTorch/15. Defining custom Image Dataset loader and usage.mp4 260.5 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/8. Sigmoid Activation Function part -2.mp4 246.2 MB
  • 10. Basics of Object Detection/12. Custom Object Detection with Detectron2.mp4 244.7 MB
  • 03. Introduction To Deep Learning/2. Why Deep Learning is Becoming Popular.mp4 239.5 MB
  • 02. Python Prerequisites/16. More Coding Examples With Functions.mp4 235.1 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/5. Chain Rule Of Derivatives.mp4 234.5 MB
  • 02. Python Prerequisites/4. Python Basics- Syntax and Semantics.mp4 231.8 MB
  • 06. PyTorch/7. Tensor Manuplation.mp4 225.0 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/28. Dropout Layers.mp4 223.9 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/22. SGD with Momentum.mp4 221.7 MB
  • 10. Basics of Object Detection/2. Object Detection Metrics.mp4 218.2 MB
  • 06. PyTorch/11. Understanding Pytorch neural network components.mp4 216.9 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/19. Gradient Descent Optimizers.mp4 216.8 MB
  • 02. Python Prerequisites/24. Exception Handling.mp4 214.2 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/27. Weight Initialisation Techniques.mp4 211.3 MB
  • 05. computer vision (Open CV With Python)/4. Exploring Color Space.mp4 208.9 MB
  • 07. Deep Dive Visualizing CNNs/1. Image Understanding with CNNs vs ANNs.mp4 208.4 MB
  • 05. computer vision (Open CV With Python)/18. Contours.mp4 206.3 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/13. Softmax for Multiclass Classification.mp4 203.9 MB
  • 02. Python Prerequisites/28. Encapsulation In OOPS.mp4 196.1 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/10. Relu Activation Function.mp4 193.4 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/35. Max, Min and Average Pooling.mp4 191.2 MB
  • 02. Python Prerequisites/25. Classes And Objects In Python.mp4 187.9 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/21. Mini Batch With SGD.mp4 187.2 MB
  • 02. Python Prerequisites/15. Getting Started With Functions.mp4 185.8 MB
  • 10. Basics of Object Detection/4. Getting started with YOLO.mp4 185.7 MB
  • 02. Python Prerequisites/35. Function Copy,Closures And Decorators.mp4 185.3 MB
  • 05. computer vision (Open CV With Python)/12. Image FIlters.mp4 185.2 MB
  • 05. computer vision (Open CV With Python)/14. Edge Detection Using Sobel, Canny & Laplacian.mp4 182.1 MB
  • 11. Image Segmentation/1. Introduction to Image Segmentation.mp4 181.6 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/26. Exploding Gradient Problem.mp4 177.9 MB
  • 02. Python Prerequisites/14. Tuples In Python.mp4 176.9 MB
  • 02. Python Prerequisites/7. Operators In Python.mp4 175.8 MB
  • 06. PyTorch/13. Multi Class classification with pytorch using custom neural networks.mp4 173.2 MB
  • 02. Python Prerequisites/26. Inheritance In OOPS.mp4 169.5 MB
  • 02. Python Prerequisites/27. Polymorphism In OOPS.mp4 165.4 MB
  • 02. Python Prerequisites/2. Anaconda Installation.mp4 163.8 MB
  • 05. computer vision (Open CV With Python)/3. Working with the video Files.mp4 163.0 MB
  • 05. computer vision (Open CV With Python)/16. Histogram Equalization.mp4 162.5 MB
  • 05. computer vision (Open CV With Python)/5. Color Thresholding.mp4 158.9 MB
  • 10. Basics of Object Detection/1. What is Object Detection.mp4 155.9 MB
  • 05. computer vision (Open CV With Python)/7. Flip, Rotate and Crop Images.mp4 154.0 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/29. CNN Introduction.mp4 153.6 MB
  • 03. Introduction To Deep Learning/1. Introduction.mp4 153.5 MB
  • 02. Python Prerequisites/21. Standard Library Overview.mp4 151.5 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/23. Adagard.mp4 151.2 MB
  • 08. Image Classification/4. LeNet with Pytorch.mp4 148.7 MB
  • 02. Python Prerequisites/3. Getting Started With VS Code.mp4 148.6 MB
  • 06. PyTorch/10. Stack Operation.mp4 147.3 MB
  • 10. Basics of Object Detection/10. FASTER RCNN with Pytorch Implementation.mp4 147.0 MB
  • 08. Image Classification/17. ResNet Architecture.mp4 146.9 MB
  • 02. Python Prerequisites/42. Logging With a Real World Examples.mp4 144.3 MB
  • 10. Basics of Object Detection/5. Getting started with Detectron2.mp4 143.8 MB
  • 07. Deep Dive Visualizing CNNs/2. CNN Explainer.mp4 143.4 MB
  • 02. Python Prerequisites/32. Custom Exception Handling.mp4 142.8 MB
  • 02. Python Prerequisites/22. File Operation In Python.mp4 142.5 MB
  • 02. Python Prerequisites/20. Import Modules And Package In Python.mp4 142.0 MB
  • 08. Image Classification/6. AlexNet with Keras.mp4 141.9 MB
  • 06. PyTorch/19. Tools to create interactive demos.mp4 141.7 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/36. Flattening and Fully Connected Layers.mp4 141.3 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/34. Operation Of CNN Vs ANN.mp4 139.6 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/30. Human Brain V CNN.mp4 137.3 MB
  • 10. Basics of Object Detection/9. FASTER RCNN.mp4 135.9 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/20. SGD.mp4 135.3 MB
  • 02. Python Prerequisites/6. Basic Datatypes In Python.mp4 132.8 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/31. All you need to know about Images.mp4 127.9 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/2. Adv and Diadvantaes of Perceptron.mp4 127.5 MB
  • 07. Deep Dive Visualizing CNNs/5. Building Your Own Filters.mp4 122.3 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/7. Sigmoid Activation Function.mp4 122.0 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/15. Loss Function Vs Cost Function.mp4 121.8 MB
  • 07. Deep Dive Visualizing CNNs/4. CNN Filters.mp4 120.7 MB
  • 11. Image Segmentation/6. UNet.mp4 118.9 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/25. Adam Optimiser.mp4 118.9 MB
  • 09. Data Augmentation/2. Data Augmentation with Albumentations.mp4 118.4 MB
  • 05. computer vision (Open CV With Python)/17. CLAHE.mp4 116.7 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/24. RMSPROP.mp4 115.0 MB
  • 06. PyTorch/6. Tensor data types.mp4 113.6 MB
  • 08. Image Classification/20. Resnet Transfer Learning.mp4 113.1 MB
  • 06. PyTorch/1. Introduction PyTorch.mp4 112.1 MB
  • 06. PyTorch/3. indexing Tensors.mp4 110.8 MB
  • 08. Image Classification/7. AlexNet with Pytorch.mp4 110.5 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/33. Padding In CNN.mp4 110.0 MB
  • 08. Image Classification/12. VGG Transfer Learning.mp4 109.0 MB
  • 08. Image Classification/13. Inception Architecture.mp4 107.9 MB
  • 08. Image Classification/16. Inception Transfer Learning.mp4 107.7 MB
  • 11. Image Segmentation/10. Testing Yolov11 Instance Segmentation.mp4 107.2 MB
  • 05. computer vision (Open CV With Python)/15. Calculating and Plotting Histogram.mp4 107.1 MB
  • 06. PyTorch/4. Using Random Numbers to create noise image.mp4 104.8 MB
  • 06. PyTorch/9. View and Reshape Operation.mp4 102.8 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/9. Tanh Activation Function.mp4 102.7 MB
  • 07. Deep Dive Visualizing CNNs/7. CNN Parameter Calculations.mp4 102.4 MB
  • 05. computer vision (Open CV With Python)/13. Applying Blur filters Average, Gaussian, Median.mp4 101.9 MB
  • 08. Image Classification/8. VGG Architecture.mp4 101.4 MB
  • 06. PyTorch/2. Introduction to Tensors.mp4 101.1 MB
  • 07. Deep Dive Visualizing CNNs/3. Visualization with Tensorspace.mp4 97.0 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/14. Which Activation Function To Apply When.mp4 96.2 MB
  • 05. computer vision (Open CV With Python)/9. Drawing lines and shapes using opencv.mp4 94.6 MB
  • 11. Image Segmentation/3. UpsamplingTransposed Convolution.mp4 93.8 MB
  • 10. Basics of Object Detection/6. Object Detection Architectures.mp4 93.3 MB
  • 07. Deep Dive Visualizing CNNs/8. Receptive Fields.mp4 93.2 MB
  • 11. Image Segmentation/4. Segmentation Loss Functions.mp4 92.8 MB
  • 02. Python Prerequisites/41. Logging With Multiple Loggers.mp4 92.7 MB
  • 02. Python Prerequisites/34. Generators In Python.mp4 91.2 MB
  • 05. computer vision (Open CV With Python)/2. Reading and Writing Images.mp4 89.3 MB
  • 08. Image Classification/3. LeNet with Keras.mp4 89.1 MB
  • 06. PyTorch/17. Understanding Components of an Application.mp4 89.1 MB
  • 08. Image Classification/20. Fruits dataset.zip 89.0 MB
  • 01. Introduction/1. Welcome to the Course.mp4 87.4 MB
  • 11. Image Segmentation/2. Downsampling.mp4 87.3 MB
  • 02. Python Prerequisites/18. Map Functions In Python.mp4 86.4 MB
  • 02. Python Prerequisites/11. Preactical Exmaples Of List.mp4 86.2 MB
  • 07. Deep Dive Visualizing CNNs/6. Feature Map Size Calculation.mp4 85.3 MB
  • 09. Data Augmentation/1. What is Data Augmentation.mp4 85.2 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/11. Leaky Relu and Parametric Relu.mp4 83.1 MB
  • 06. PyTorch/8. Matrix Aggregation.mp4 83.1 MB
  • 10. Basics of Object Detection/8. FAST RCNN.mp4 82.8 MB
  • 08. Image Classification/1. What is Image Classification.mp4 82.4 MB
  • 02. Python Prerequisites/31. Operator Overloading In Python.mp4 81.2 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/37. CNN Example with RGB.mp4 80.7 MB
  • 10. Basics of Object Detection/7. RCNN.mp4 80.4 MB
  • 02. Python Prerequisites/23. Working With File Paths.mp4 77.4 MB
  • 06. PyTorch/24. Deploying gradio app on hugging face space.mp4 76.9 MB
  • 02. Python Prerequisites/29. Abstraction In OOPS.mp4 75.7 MB
  • 06. PyTorch/20. Hosting platform.mp4 74.0 MB
  • 02. Python Prerequisites/19. Filter Function In Python.mp4 73.8 MB
  • 02. Python Prerequisites/10. List and List Comprehension In Python.mp4 73.2 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/12. ELU Activation Function.mp4 72.4 MB
  • 02. Python Prerequisites/30. Magic Methods In Python.mp4 71.9 MB
  • 02. Python Prerequisites/17. Python Lambda Functions.mp4 71.5 MB
  • 05. computer vision (Open CV With Python)/10. Adding Text to Image.mp4 70.8 MB
  • 08. Image Classification/5. AlexNet Architecture.mp4 70.6 MB
  • 08. Image Classification/10. VGG Pretrained Keras.mp4 68.8 MB
  • 08. Image Classification/2. LeNet Architecture.mp4 68.7 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/18. Which Loss Function To Use When.mp4 65.1 MB
  • 09. Data Augmentation/3. Data Augmentation with Imgaug.mp4 61.4 MB
  • 06. PyTorch/23. Setting hugging face space.mp4 61.3 MB
  • 06. PyTorch/21. Setting up gradio app in local space.mp4 57.2 MB
  • 10. Basics of Object Detection/3. What are Bounding Boxes.mp4 53.0 MB
  • 02. Python Prerequisites/33. Iterators In Python.mp4 49.8 MB
  • 05. computer vision (Open CV With Python)/1. Introduction to OpenCV.mp4 49.2 MB
  • 08. Image Classification/14. Inception Pretrained Keras.mp4 48.0 MB
  • 08. Image Classification/11. VGG Pretrained Pytorch.mp4 44.5 MB
  • 06. PyTorch/5. Tensors of Zero's and One's.mp4 37.1 MB
  • 08. Image Classification/15. Inception Pretrained Pytorch.mp4 37.0 MB
  • 08. Image Classification/9. Transfer Learning vs Pretrained.mp4 35.6 MB
  • 10. Basics of Object Detection/5. Getting_Started_with_Detectron2_Object_Detection.ipynb 35.0 MB
  • 06. PyTorch/22. 022. Implementing gradio app inference backend(gradio-app-1-chkpt-22).zip 32.8 MB
  • 06. PyTorch/16. 016-CNN-Training-Using-a-Custom-Dataset.zip 32.8 MB
  • 06. PyTorch/18. What is Deployment.mp4 31.6 MB
  • 08. Image Classification/19. Resnet Pretrained Pytorch.mp4 27.3 MB
  • 08. Image Classification/18. Resnet Pretrained Keras.mp4 23.0 MB
  • 05. computer vision (Open CV With Python)/8. Understanding Coordinate system in openCV.mp4 22.9 MB
  • 05. computer vision (Open CV With Python)/3. 003. Working_with_video_files.zip 9.2 MB
  • 07. Deep Dive Visualizing CNNs/1. Understanding of images with Visualization.pdf 8.8 MB
  • 05. computer vision (Open CV With Python)/2. 002. Reading_and_writing_images.zip 6.9 MB
  • 05. computer vision (Open CV With Python)/11. 011. Affine and Perspective Transformation.zip 5.4 MB
  • 10. Basics of Object Detection/12. Custom_Dataset_Training_with_Detectron2.ipynb 5.2 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/29. 30-38 CNN.pdf 5.2 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/6. 8-15 Activation functions.pdf 4.9 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/19. 20-26 Optimizers.pdf 4.4 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/3. 5-8 Deep LEarning.pdf 4.4 MB
  • 07. Deep Dive Visualizing CNNs/5. Building Your Custom Filters.ipynb 4.3 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/15. 16-19 Loss Functions.pdf 3.6 MB
  • 03. Introduction To Deep Learning/1. 1-4 Deep learnng.pdf 3.3 MB
  • 05. computer vision (Open CV With Python)/20. 020. Haar Cascade for face detection (1).zip 3.2 MB
  • 05. computer vision (Open CV With Python)/19. 019. Image Segmentation Using openCV (1).zip 3.1 MB
  • 05. computer vision (Open CV With Python)/7. 007. Flip, Rotate and Crop Images.zip 2.4 MB
  • 11. Image Segmentation/8. 008-Mask-RCNN.pdf 2.3 MB
  • 05. computer vision (Open CV With Python)/6. 006. Image Resizing, Scaling and interpolation.zip 2.2 MB
  • 09. Data Augmentation/2. Data_Augmenation_with_Albumentations.ipynb 2.1 MB
  • 11. Image Segmentation/1. 001-Introduction to image segmentation.pdf 2.1 MB
  • 10. Basics of Object Detection/1. What is Object Detection.pdf 2.0 MB
  • 05. computer vision (Open CV With Python)/11. 011. Affine and Perspective Transformation.pdf 2.0 MB
  • 05. computer vision (Open CV With Python)/16. 016. Histogram Equalization.zip 2.0 MB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/27. 27-8 Weight initialization Techniques.pdf 2.0 MB
  • 05. computer vision (Open CV With Python)/4. 004. Exploring_Color_Space (1).zip 1.9 MB
  • 08. Image Classification/8. VGG CNN Architecture .pdf 1.9 MB
  • 10. Basics of Object Detection/9. Faster RCNN.pdf 1.9 MB
  • 05. computer vision (Open CV With Python)/6. 006. Image Resizing, Scaling and interpolation (1).zip 1.8 MB
  • 05. computer vision (Open CV With Python)/17. 017. CLAHE.zip 1.8 MB
  • 05. computer vision (Open CV With Python)/14. 014. Edge Detection Using Sobel, Canny & Laplacian_pdf.zip 1.7 MB
  • 11. Image Segmentation/5. 005-Fully Convolutional Networks (FCNs).pdf 1.7 MB
  • 05. computer vision (Open CV With Python)/19. 019. Image Segmentation Using openCV.zip 1.6 MB
  • 08. Image Classification/17. Resnet Architecture .pdf 1.5 MB
  • 10. Basics of Object Detection/7. RCNN.pdf 1.5 MB
  • 05. computer vision (Open CV With Python)/12. 012. Image FIlters (1).zip 1.5 MB
  • 08. Image Classification/13. Googlenet CNN Architecture.pdf 1.5 MB
  • 08. Image Classification/2. LeNet-5 CNN Architecture .pdf 1.4 MB
  • 10. Basics of Object Detection/8. Fast RCNN.pdf 1.3 MB
  • 06. PyTorch/20. 020. Hosting platform.pdf 1.3 MB
  • 10. Basics of Object Detection/10. Faster_RCNN_with_Pytorch.ipynb 1.2 MB
  • 10. Basics of Object Detection/3. Bounding Boxes.pdf 1.2 MB
  • 08. Image Classification/1. What is Image Classification.pdf 1.2 MB
  • 11. Image Segmentation/4. 004-Segmentation Loss Functions.pdf 1.1 MB
  • 05. computer vision (Open CV With Python)/14. 014. Edge Detection Using Sobel, Canny & Laplacian.zip 1.1 MB
  • 10. Basics of Object Detection/11. Custom_Dataset_Training_with_YOLOv11.ipynb 1.1 MB
  • 05. computer vision (Open CV With Python)/5. 005. Color Thresholding (1).zip 1.1 MB
  • 11. Image Segmentation/6. 006-Unet.pdf 1.0 MB
  • 05. computer vision (Open CV With Python)/16. 016. Histogram Equalization (1).zip 1.0 MB
  • 05. computer vision (Open CV With Python)/3. 003. Working_with_video_files (1).zip 1.0 MB
  • 06. PyTorch/17. 017. Understanding Components of an Application.pdf 997.2 kB
  • 10. Basics of Object Detection/6. Object Detection Architectures .pdf 978.0 kB
  • 06. PyTorch/9. 009-View-and-reshape.zip 965.9 kB
  • 06. PyTorch/9. 009-View-and-reshape.pdf 965.7 kB
  • 05. computer vision (Open CV With Python)/17. 017. CLAHE.pdf 962.8 kB
  • 11. Image Segmentation/9. 009-Training Yolov11 Instance Segmentation.pdf 961.6 kB
  • 08. Image Classification/5. AlexNet CNN Architecture.pdf 928.0 kB
  • 06. PyTorch/15. 015. Defining custom Image Dataset loader and usage.pdf 919.4 kB
  • 06. PyTorch/16. 016. CNN Training Using a Custom Dataset.pdf 918.9 kB
  • 07. Deep Dive Visualizing CNNs/7. CNN Parameters Calculation.pdf 914.9 kB
  • 11. Image Segmentation/3. 003-Transposed convolution.pdf 912.9 kB
  • 06. PyTorch/19. 019. Tools to create interactive demos.pdf 908.5 kB
  • 05. computer vision (Open CV With Python)/7. 007. Flip, Rotate and Crop Images (1).zip 906.0 kB
  • 09. Data Augmentation/3. Data_Augmentation_with_IMGAUG.ipynb 895.9 kB
  • 05. computer vision (Open CV With Python)/5. 005. Color Thresholding.zip 887.2 kB
  • 06. PyTorch/11. 011-Understanding-Pytorch-neural-network-components.pdf 883.0 kB
  • 07. Deep Dive Visualizing CNNs/8. Receptive Fields in CNN.pdf 869.1 kB
  • 06. PyTorch/10. 010-Stack-Operation.zip 867.2 kB
  • 06. PyTorch/10. 010-Stack-Operation.pdf 867.0 kB
  • 06. PyTorch/18. 018. What is Deployment.pdf 863.9 kB
  • 11. Image Segmentation/2. 002-Downsampling.pdf 845.6 kB
  • 05. computer vision (Open CV With Python)/18. 018. Contours (1).zip 837.4 kB
  • 05. computer vision (Open CV With Python)/4. 004. Exploring_Color_Space.zip 833.1 kB
  • 08. Image Classification/20. Resnet Transfer Learning Pytorch.ipynb 805.9 kB
  • 05. computer vision (Open CV With Python)/8. 008. Understanding Coordinate system in openCV.pdf 798.7 kB
  • 08. Image Classification/16. InceptionV3_Transfer_Learning_Keras_CIFAR10.ipynb 778.9 kB
  • 04. Deep Learning-ANN, Optimizers, Loss Functions, Activation Functions,CNN Theory/28. 29-Dropout Layer.pdf 778.4 kB
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