Complete Computer Vision Bootcamp With PyTorch and Tensorflow

[0dayddl]

27.54 GB | 1h 35min 27s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 -What is Object Detection.mp4 (148.66 MB)
10 -FASTER RCNN with Pytorch Implementation.mp4 (140.18 MB)
11 -Custom Object Detection with YOLOv11.mp4 (307.36 MB)
12 -Custom Object Detection with Detectron2.mp4 (233.39 MB)
2 -Object Detection Metrics.mp4 (208.06 MB)
3 -What are Bounding Boxes.mp4 (50.55 MB)
4 -Getting started with YOLO.mp4 (177.13 MB)
5 -Getting started with Detectron2.mp4 (137.15 MB)
6 -Object Detection Architectures.mp4 (88.98 MB)
7 -RCNN.mp4 (76.67 MB)
8 -FAST RCNN.mp4 (79 MB)
9 -FASTER RCNN.mp4 (129.65 MB)
1 -Introduction to Image Segmentation.mp4 (173.16 MB)
2 -002-Downsampling.zip (1.37 KB)
2 -Downsampling.mp4 (83.22 MB)
3 -003-Transposed convolution.zip (1.27 KB)
3 -UpsamplingTransposed Convolution.mp4 (89.45 MB)
4 -004-Segmentation Loss Functions.zip (1.15 KB)
4 -Segmentation Loss Functions.mp4 (88.49 MB)
5 -005-Fully Convolutional Networks (FCNs).zip (2.71 KB)
5 -Fully Convolutional Networks (FCNs).mp4 (325.02 MB)
6 -UNet.mp4 (113.42 MB)
1 -Anaconda Installation.mp4 (156.24 MB)
1 -Complete-Python-Bootcamp-main.zip (554.16 KB)
10 -Preactical Exmaples Of List.mp4 (82.23 MB)
11 -Sets In Python.mp4 (393.58 MB)
12 -Dictionaries In Python.mp4 (298.75 MB)
13 -Tuples In Python.mp4 (168.68 MB)
14 -Getting Started With Functions.mp4 (177.16 MB)
15 -More Coding Examples With Functions.mp4 (224.19 MB)
16 -Python Lambda Functions.mp4 (68.2 MB)
17 -Map Functions In Python.mp4 (82.41 MB)
18 -Filter Function In Python.mp4 (70.42 MB)
19 -Import Modules And Package In Python.mp4 (135.44 MB)
2 -Complete-Python-Bootcamp-main.zip (554.16 KB)
2 -Getting Started With VS Code.mp4 (141.75 MB)
20 -Standard Library Overview.mp4 (144.52 MB)
21 -File Operation In Python.mp4 (135.86 MB)
22 -Working With File Paths.mp4 (73.78 MB)
23 -Exception Handling.mp4 (204.28 MB)
24 -Classes And Objects In Python.mp4 (179.21 MB)
25 -Inheritance In OOPS.mp4 (161.6 MB)
26 -Polymorphism In OOPS.mp4 (157.71 MB)
27 -Encapsulation In OOPS.mp4 (187.02 MB)
28 -Abstraction In OOPS.mp4 (72.19 MB)
29 -Magic Methods In Python.mp4 (68.53 MB)
3 -Python Basics- Syntax and Semantics.mp4 (221.07 MB)
30 -Operator Overloading In Python.mp4 (77.44 MB)
31 -Custom Exception Handling.mp4 (136.14 MB)
32 -Iterators In Python.mp4 (47.51 MB)
33 -Generators In Python.mp4 (86.94 MB)
34 -Function Copy,Closures And Decorators.mp4 (176.72 MB)
35 -Numpy In Python.mp4 (520.42 MB)
36 -Pandas-DataFrame And Series.mp4 (532.58 MB)
37 -Data Manipulation With Pandas And Numpy.mp4 (447.01 MB)
38 -Reading Data From Various Data Source Using Pandas.mp4 (272.27 MB)
39 -Logging Practical Implementation In Python.mp4 (254.16 MB)
4 -Variables In Python.mp4 (267.74 MB)
40 -Logging With Multiple Loggers.mp4 (88.4 MB)
41 -Logging With a Real World Examples.mp4 (137.62 MB)
5 -Basic Datatypes In Python.mp4 (126.62 MB)
6 -Operators In Python.mp4 (167.63 MB)
7 -Conditional Statements(if,elif,else).mp4 (307.06 MB)
8 -Loops In Python.mp4 (376.89 MB)
9 -List and List Comprehension In Python.mp4 (287 MB)
1 -Introduction.mp4 (146.38 MB)
2 -Why Deep Learning is Becoming Popular.mp4 (228.39 MB)
1 -Perceptron Intuition.mp4 (326.9 MB)
10 -Relu Activation Function.mp4 (184.45 MB)
11 -Leaky Relu and Parametric Relu.mp4 (79.27 MB)
12 -ELU Activation Function.mp4 (69.01 MB)
13 -Softmax for Multiclass Classification.mp4 (194.42 MB)
14 -Which Activation Function To Apply When.mp4 (91.71 MB)
15 -Loss Function Vs Cost Function.mp4 (116.14 MB)
16 -Regression Cost Function.mp4 (285.7 MB)
17 -Loss Function Classification Problem.mp4 (358.04 MB)
18 -Which Loss Function To Use When.mp4 (62.07 MB)
19 -Gradient Descent Optimizers.mp4 (206.74 MB)
2 -Adv and Diadvantaes of Perceptron.mp4 (121.56 MB)
20 -SGD.mp4 (129.07 MB)
21 -Mini Batch With SGD.mp4 (178.56 MB)
22 -SGD with Momentum.mp4 (211.45 MB)
23 -Adagard.mp4 (144.22 MB)
24 -RMSPROP.mp4 (109.66 MB)
25 -Adam Optimiser.mp4 (113.42 MB)
26 -Exploding Gradient Problem.mp4 (169.63 MB)
27 -Weight Initialisation Techniques.mp4 (201.52 MB)
28 -Dropout Layers.mp4 (213.57 MB)
29 -CNN Introduction.mp4 (146.49 MB)
3 -ANN intuition and Working mov.mp4 (386.45 MB)
30 -Human Brain V CNN.mp4 (130.98 MB)
31 -All you need to know about Images.mp4 (121.94 MB)
32 -Convolution Operatuin In CNN.mp4 (275.14 MB)
33 -Padding In CNN.mp4 (104.92 MB)
34 -Operation Of CNN Vs ANN.mp4 (133.09 MB)
35 -Max, Min and Average Pooling.mp4 (182.37 MB)
36 -Flattening and Fully Connected Layers.mp4 (134.79 MB)
37 -CNN Example with RGB.mp4 (76.95 MB)
4 -Back Propogation and Weight Updation.mp4 (359.89 MB)
5 -Chain Rule Of Derivatives.mp4 (223.67 MB)
6 -Vanishing Gradient Problem and Sigmoid.mp4 (399.21 MB)
7 -Sigmoid Activation Function.mp4 (116.32 MB)
8 -Sigmoid Activation Function part -2.mp4 (234.8 MB)
9 -Tanh Activation Function.mp4 (97.95 MB)
1 -002.zip (471.57 KB)
1 -Reading and Writing Images.mp4 (85.18 MB)
10 -010.zip (48.46 KB)
10 -Adding Text to Image.mp4 (67.48 MB)
11 -011.zip (5.1 MB)
11 -Affine.mp4 (275.47 MB)
12 -012.zip (570.41 KB)
12 -Image FIlters.mp4 (176.61 MB)
13 -013.mp4 (98.12 MB)
13 -013.zip (585.09 KB)
13 -Applying Blur filters Average, Gaussian, Median.mp4 (97.19 MB)
14 -014.mp4 (189.97 MB)
14 -014.zip (1.04 MB)
14 -Edge Detection Using Sobel, Canny & Laplacian.mp4 (173.71 MB)
15 -015.zip (304.71 KB)
15 -Calculating and Plotting Histogram.mp4 (102.16 MB)
16 -016.zip (1005.32 KB)
16 -Histogram Equalization.mp4 (155 MB)
17 -017.zip (1.69 MB)
17 -CLAHE.mp4 (111.33 MB)
18 -018.zip (817.75 KB)
18 -Contours.mp4 (196.71 MB)
19 -019.zip (2.95 MB)
19 -Image Segmentation Using openCV.mp4 (658.3 MB)
2 -003.zip (1004.58 KB)
2 -Working with the video Files.mp4 (155.43 MB)
20 -020.zip (1.55 KB)
20 -Haar Cascade for face detection.mp4 (419.88 MB)
3 -Introduction openCv.mp4 (46.92 MB)
4 -004.zip (1.83 MB)
4 -Exploring Color Space.mp4 (199.25 MB)
5 -005.zip (1.02 MB)
5 -Color Thresholding.mp4 (151.53 MB)
6 -006.zip (1.73 MB)
6 -image Resizing, Scaling and interpolation.mp4 (274.93 MB)
7 -007.zip (884.79 KB)
7 -Flip, Rotate and Crop Images.mp4 (146.83 MB)
8 -Understanding Coordinate system in openCV.mp4 (21.85 MB)
9 -009.zip (408.9 KB)
9 -Drawing lines and shapes using opencv.mp4 (90.21 MB)
1 -Introduction PyTorch.mp4 (106.88 MB)
10 -010-Stack-Operation.zip (846.84 KB)
10 -Stack Operation.mp4 (140.45 MB)
11 -011-Understanding Pytorch neural network components.zip (2.6 KB)
11 -Understanding Pytorch neural network components.mp4 (206.85 MB)
12 -012-Create Linear Regression model with Pytorch components.zip (11.28 KB)
12 -Create Linear Regression model with Pytorch components.mp4 (353.42 MB)
13 -013-Multi-Class-classification-with-pytorch-using-custom-neural-networks.zip (5.62 KB)
13 -Multi Class classification with pytorch using custom neural networks.mp4 (165.18 MB)
14 -014-Understanding-components-of-custom-data-loader-in-pytorch.zip (27.52 KB)
14 -Understanding components of custom data loader in pytorch.mp4 (266.82 MB)
15 -015-Defining-custom-Image-Dataset-loader-and-usage.zip (155.7 KB)
15 -Defining custom Image Dataset loader and usage.mp4 (248.41 MB)
16 -016-CNN-Training-Using-a-Custom-Dataset.zip (31.25 MB)
16 -CNN Training Using a Custom Dataset.mp4 (535.32 MB)
17 -Understanding Components of an Application.mp4 (85 MB)
18 -What is Deployment.mp4 (30.17 MB)
19 -Tools to create interactive demos.mp4 (135.17 MB)
2 -002-Introduction to tensors.zip (2.14 KB)
2 -Introduction to Tensors.mp4 (96.44 MB)
20 -Hosting platform.mp4 (70.55 MB)
21 -021.zip (778 B)
21 -Setting up gradio app in local space.mp4 (54.52 MB)
22 -022.zip (31.31 MB)
22 -Implementing gradio app inference backend.mp4 (306.87 MB)
23 -Setting hugging face space.mp4 (58.47 MB)
24 -Deploying gradio app on hugging face space.mp4 (73.3 MB)
3 -003-Indexing-Tensors.zip (2.22 KB)
3 -indexing Tensors.mp4 (105.63 MB)
4 -004-Using Random Numbers to create noise image.zip (759.76 KB)
4 -Using Random Numbers to create noise image.mp4 (99.93 MB)
5 -005-Tensors of Zero s and One s.zip (6.22 KB)
5 -Tensors of Zero's and One's.mp4 (35.37 MB)
6 -006-Tensor DataTypes.zip (1.96 KB)
6 -Tensor data types.mp4 (108.31 MB)
7 -007-Tensor Manipulation.zip (3.33 KB)
7 -Tensor Manuplation.mp4 (214.62 MB)
8 -008-Matrix Aggregation.zip (1.88 KB)
8 -Matrix Aggregation.mp4 (79.23 MB)
9 -009-View-and-reshape.zip (943.3 KB)
9 -View and Reshape Operation.mp4 (98 MB)
1 -Image Understanding with CNNs vs ANNs.mp4 (198.74 MB)
2 -CNN Explainer.mp4 (136.76 MB)
3 -Visualization with Tensorspace.mp4 (92.55 MB)
4 -CNN Filters.mp4 (115.07 MB)
5 -Building Your Own Filters.mp4 (116.62 MB)
6 -Feature Map Size Calculation.mp4 (81.38 MB)
7 -CNN Parameter Calculations.mp4 (97.69 MB)
8 -Receptive Fields.mp4 (88.93 MB)
1 -What is Image Classification.mp4 (78.59 MB)
10 -VGG Pretrained Keras.mp4 (65.57 MB)
11 -VGG Pretrained Pytorch.mp4 (42.41 MB)
12 -VGG Transfer Learning.mp4 (103.94 MB)
13 -Inception Architecture.mp4 (102.86 MB)
14 -Inception Pretrained Keras.mp4 (45.81 MB)
15 -Inception Pretrained Pytorch.mp4 (35.3 MB)
16 -Inception Transfer Learning.mp4 (102.75 MB)
17 -ResNet Architecture.mp4 (140.06 MB)
18 -Resnet Pretrained Keras.mp4 (21.89 MB)
19 -Resnet Pretrained Pytorch.mp4 (26.04 MB)
2 -LeNet Architecture.mp4 (65.49 MB)
20 -Resnet Transfer Learning.mp4 (107.86 MB)
3 -LeNet with Keras.mp4 (85.02 MB)
4 -LeNet with Pytorch.mp4 (141.81 MB)
5 -AlexNet Architecture.mp4 (67.36 MB)
6 -AlexNet with Keras.mp4 (135.33 MB)
7 -AlexNet with Pytorch.mp4 (105.38 MB)
8 -VGG Architecture.mp4 (96.69 MB)
9 -Transfer Learning vs Pretrained.mp4 (33.99 MB)
1 -What is Data Augmentation.mp4 (81.3 MB)
2 -Data Augmentation with Albumentations.mp4 (112.94 MB)
3 -Data Augmentation with Imgaug.mp4 (58.59 MB)

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[0dayddl]

Complete Computer Vision Bootcamp With PyTorch & Tensorflow
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 35.58 GB | Duration: 54h 9m​

Learn Computer Vision with CNN, TensorFlow, and PyTorch - Master Object Detection from Basics to Advanced

What you'll learn
Master CNN concepts from basics to advanced with TensorFlow & PyTorch.
Learn object detection models like YOLO and Faster R-CNN.
Implement real-world computer vision projects step-by-step.
Gain hands-on experience with data preprocessing and augmentation.
Build custom CNN models for various computer vision tasks.
Master transfer learning with pre-trained models like ResNet and VGG
Gain practical skills with TensorFlow and PyTorch libraries

Requirements
Basic understanding of Python programming.
Familiarity with fundamental machine learning concepts.
Knowledge of basic linear algebra and calculus.
Understanding of image data and its structure.
Enthusiasm to learn computer vision with hands-on projects.

Description
In this comprehensive course, you will master the fundamentals and advanced concepts of computer vision, focusing on Convolutional Neural Networks (CNN) and object detection models using TensorFlow and PyTorch. This course is designed to equip you with the skills required to build robust computer vision applications from scratch.What You Will LearnThroughout this course, you will gain expertise in:Introduction to Computer VisionUnderstanding image data and its structure.Exploring pixel values, channels, and color spaces.Learning about OpenCV for image manipulation and preprocessing.Deep Learning Fundamentals for Computer VisionIntroduction to Neural Networks and Deep Learning concepts.Understanding backpropagation and gradient descent.Key concepts like activation functions, loss functions, and optimization techniques.Convolutional Neural Networks (CNN)Introduction to CNN architecture and its components.Understanding convolution layers, pooling layers, and fully connected layers.Implementing CNN models using TensorFlow and PyTorch.Data Augmentation and PreprocessingTechniques for improving model performance through data augmentation.Using libraries like imgaug, Albumentations, and TensorFlow Data Pipeline.Transfer Learning for Computer VisionUtilizing pre-trained models such as ResNet, VGG, and EfficientNet.Fine-tuning and optimizing transfer learning models.Object Detection ModelsExploring object detection algorithms like:YOLO (You Only Look Once)Faster R-CNNImplementing these models with TensorFlow and PyTorch.Image Segmentation TechniquesUnderstanding semantic and instance segmentation.Implementing U-Net and Mask R-CNN models.Real-World Projects and ApplicationsBuilding practical computer vision projects such as:Face detection and recognition system.Real-time object detection with webcam integration.Image classification pipelines with deployment.Who Should Enroll?This course is ideal for:Beginners looking to start their computer vision journey.Data scientists and ML engineers wanting to expand their skill set.AI practitioners aiming to master object detection models.Researchers exploring computer vision techniques for academic projects.Professionals seeking practical experience in deploying CV models.PrerequisitesBefore enrolling, ensure you have:Basic knowledge of Python programming.Familiarity with fundamental machine learning concepts.Basic understanding of linear algebra and calculus.Hands-on Learning with Real ProjectsThis course emphasizes practical learning through hands-on projects. Each module includes coding exercises, project implementations, and real-world examples to ensure you gain valuable skills.By the end of this course, you will confidently build, train, and deploy computer vision models using TensorFlow and PyTorch. Whether you are a beginner or an experienced practitioner, this course will empower you with the expertise needed to excel in the field of computer vision.Enroll now and take your computer vision skills to the next level!

Who this course is for:
Beginners eager to learn computer vision from scratch., Data scientists looking to expand their skill set with CNN and object detection., AI and ML engineers aiming to build computer vision models., Researchers and students exploring deep learning for visual tasks., Professionals interested in deploying real-world CV applications

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