Deep Learning for Vision Systems, Video Edition

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size2.6 GB
  • Uploaded Byfreecoursewb
  • Downloads7
  • Last checkedJul. 31st '25
  • Date uploadedJul. 31st '25
  • Seeders 1
  • Leechers3

Infohash : C3C76C01041D1771F09B8A2766015E244EE945A3

Deep Learning for Vision Systems, Video Edition

https://WebToolTip.com

Published: 11/2020
Duration: 12h 14m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 44.1kHz, 2ch | Size: 2.57 GB
Genre: eLearning | Language: English

In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision!

About the Technology
How much has computer vision advanced? One ride in a Tesla is the only answer you’ll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway.

Files:

[ WebToolTip.com ] Deep Learning for Vision Systems, Video Edition
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here !
    • 001. Part 1. Deep learning foundation.en.srt (1.6 KB)
    • 001. Part 1. Deep learning foundation.mp4 (7.2 MB)
    • 002. Chapter 1. Welcome to computer vision.en.srt (24.5 KB)
    • 002. Chapter 1. Welcome to computer vision.mp4 (42.8 MB)
    • 003. Chapter 1. Applications of computer vision.en.srt (15.0 KB)
    • 003. Chapter 1. Applications of computer vision.mp4 (44.5 MB)
    • 004. Chapter 1. Computer vision pipeline - The big picture.en.srt (7.7 KB)
    • 004. Chapter 1. Computer vision pipeline - The big picture.mp4 (23.0 MB)
    • 005. Chapter 1. Image input.en.srt (11.3 KB)
    • 005. Chapter 1. Image input.mp4 (26.9 MB)
    • 006. Chapter 1. Image preprocessing.en.srt (10.2 KB)
    • 006. Chapter 1. Image preprocessing.mp4 (20.0 MB)
    • 007. Chapter 1. Feature extraction.en.srt (15.5 KB)
    • 007. Chapter 1. Feature extraction.mp4 (44.8 MB)
    • 008. Chapter 1. Classifier learning algorithm.en.srt (3.0 KB)
    • 008. Chapter 1. Classifier learning algorithm.mp4 (5.5 MB)
    • 009. Chapter 1. Summary.en.srt (1.2 KB)
    • 009. Chapter 1. Summary.mp4 (3.0 MB)
    • 010. Chapter 2. Deep learning and neural networks.en.srt (25.1 KB)
    • 010. Chapter 2. Deep learning and neural networks.mp4 (58.3 MB)
    • 011. Chapter 2. Multilayer perceptrons.en.srt (18.2 KB)
    • 011. Chapter 2. Multilayer perceptrons.mp4 (62.5 MB)
    • 012. Chapter 2. Activation functions.en.srt (21.9 KB)
    • 012. Chapter 2. Activation functions.mp4 (53.7 MB)
    • 013. Chapter 2. The feedforward process.en.srt (13.0 KB)
    • 013. Chapter 2. The feedforward process.mp4 (41.0 MB)
    • 014. Chapter 2. Error functions.en.srt (15.4 KB)
    • 014. Chapter 2. Error functions.mp4 (27.5 MB)
    • 015. Chapter 2. Optimization algorithms.en.srt (28.7 KB)
    • 015. Chapter 2. Optimization algorithms.mp4 (85.0 MB)
    • 016. Chapter 2. Backpropagation.en.srt (8.5 KB)
    • 016. Chapter 2. Backpropagation.mp4 (18.8 MB)
    • 017. Chapter 2. Summary.en.srt (1.0 KB)
    • 017. Chapter 2. Summary.mp4 (2.8 MB)
    • 018. Chapter 3. Convolutional neural networks.en.srt (22.3 KB)
    • 018. Chapter 3. Convolutional neural networks.mp4 (76.6 MB)
    • 019. Chapter 3. CNN architecture.en.srt (10.3 KB)
    • 019. Chapter 3. CNN architecture.mp4 (34.4 MB)
    • 020. Chapter 3. Basic components of a CNN.en.srt (36.3 KB)
    • 020. Chapter 3. Basic components of a CNN.mp4 (96.7 MB)
    • 021. Chapter 3. Image classification using CNNs.en.srt (10.4 KB)
    • 021. Chapter 3. Image classification using CNNs.mp4 (22.5 MB)
    • 022. Chapter 3. Adding dropout layers to avoid overfitting.en.srt (10.9 KB)
    • 022. Chapter 3. Adding dropout layers to avoid overfitting.mp4 (20.2 MB)
    • 023. Chapter 3. Convolution over color images (3D images).en.srt (9.3 KB)
    • 023. Chapter 3. Convolution over color images (3D images).mp4 (18.3 MB)
    • 024. Chapter 3. Project - Image classification for color images.en.srt (19.2 KB)
    • 024. Chapter 3. Project - Image classification for color images.mp4 (62.5 MB)
    • 025. Chapter 3. Summary.en.srt (2.2 KB)
    • 025. Chapter 3. Summary.mp4 (10.3 MB)
    • 026. Chapter 4. Structuring DL projects and hyperparameter tuning.en.srt (17.4 KB)
    • 026. Chapter 4. Structuring DL projects and hyperparameter tuning.mp4 (29.4 MB)
    • 027. Chapter 4. Designing a baseline model.en.srt (4.2 KB)
    • 027. Chapter 4. Designing a baseline model.mp4 (9.4 MB)
    • 028. Chapter 4. Getting your data ready for training.en.srt (14.9 KB)
    • 028. Chapter 4. Getting your data ready for training.mp4 (43.5 MB)
    • 029. Chapter 4. Evaluating the model and interpreting its performance.en.srt (14.9 KB)
    • 029. Chapter 4. Evaluating the model and interpreting its performance.mp4 (27.9 MB)
    • 030. Chapter 4. Improving the network and tuning hyperparameters.en.srt (14.7 KB)
    • 030. Chapter 4. Improving the network and tuning hyperparameters.mp4 (29.3 MB)
    • 031. Chapter 4. Learning and optimization.en.srt (18.7 KB)
    • 031. Chapter 4. Learning and optimization.mp4 (44.7 MB)
    • 032. Chapter 4. Optimization algorithms.en.srt (11.9 KB)
    • 032. Chapter 4. Optimization algorithms.mp4 (21.6 MB)
    • 033. Chapter 4. Regularization techniques to avoid overfitting.en.srt (9.5 KB)
    • 033. Chapter 4. Regularization techniques to avoid overfitting.mp4 (23.5 MB)
    • 034. Chapter 4. Batch normalization.en.srt (10.4 KB)
    • 034. Chapter 4. Batch normalization.mp4 (29.7 MB)
    • 035. Chapter 4. Project - Achieve high accuracy on image classification.en.srt (11.9 KB)
    • 035. Chapter 4. Project - Achieve high accuracy on image classification.mp4 (30.6 MB)
    • 036. Chapter 4. Summary.en.srt (0.8 KB)
    • 036. Chapter 4. Summary.mp4 (1.8 MB)
    • 037. Part 2. Image classification and detection.en.srt (1.2 KB)
    • 037. Part 2. Image classification and detection.mp4 (2.5 MB)
    • 038. Chapter 5. Advanced CNN architectures.en.srt (13.8 KB)
    • 038. Chapter 5. Advanced CNN architectures.mp4 (26.2 MB)
    • 039. Chapter 5. LeNet-5.en.srt (7.2 KB)
    • 039. Chapter 5. LeNet-5.mp4 (17.5 MB)
    • 040. Chapter 5. AlexNet.en.srt (30.4 KB)
    • 040. Chapter 5. AlexNet.mp4 (49.0 MB)
    • 041. Chapter 5. VGGNet.en.srt (7.9 KB)
    • 041. Chapter 5. VGGNet.mp4 (27.1 MB)
    • 042. Chapter 5. Inception and GoogLeNet.en.srt (24.5 KB)
    • 042. Chapter 5. Inception and GoogLeNet.mp4 (65.2 MB)
    • 043. Chapter 5. ResNet.en.srt (24.5 KB)
    • 043. Chapter 5. ResNet.mp4 (59.4 MB)
    • 044. Chapter 5. Summary.en.srt (1.7 KB)
    • 044. Chapter 5. Summary.mp4 (9.0 MB)
    • 045. Chapter 6. Transfer learning.en.srt (10.5 KB)
    • 045. Chapter 6. Transfer learning.mp4 (32.3 MB)
    • 046. Chapter 6. What is transfer learning.en.srt (12.6 KB)
    • 046. Chapter 6. What is transfer learning.mp4 (29.4 MB)
    • 047. Chapter 6. How transfer learning works.en.srt (13.0 KB)
    • 047. Chapter 6. How transfer learning works.mp4 (24.5 MB)
    • 048. Chapter 6. Transfer learning approaches.en.srt (15.1 KB)
    • 048. Chapter 6. Transfer learning approaches.mp4 (28.1 MB)
    • 049. Chapter 6. Choosing the appropriate level of transfer learning.en.srt (9.3 KB)
    • 049. Chapter 6. Choosing the appropriate level of transfer learning.mp4 (26.7 MB)

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