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Deep Learning for Vision Systems Video Edition

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Tutorials

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Files:

Get Bonus Downloads Here.url 180 bytes
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 772 bytes
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
050. Chapter 6. Open source datasets.en.srt 11.1 KB
050. Chapter 6. Open source datasets.mp4 23.9 MB
051. Chapter 6. Project 1 - A pretrained network as a feature extractor.en.srt 8.8 KB
051. Chapter 6. Project 1 - A pretrained network as a feature extractor.mp4 31.9 MB
052. Chapter 6. Project 2 - Fine-tuning.en.srt 9.1 KB
052. Chapter 6. Project 2 - Fine-tuning.mp4 24.0 MB
053. Chapter 6. Summary.en.srt 2.6 KB
053. Chapter 6. Summary.mp4 9.9 MB
054. Chapter 7. Object detection with R-CNN, SSD, and YOLO.en.srt 24.2 KB
054. Chapter 7. Object detection with R-CNN, SSD, and YOLO.mp4 76.4 MB
055. Chapter 7. Region-based convolutional neural networks (R-CNNs).en.srt 46.7 KB
055. Chapter 7. Region-based convolutional neural networks (R-CNNs).mp4 145.2 MB
056. Chapter 7. Single-shot detector (SSD).en.srt 23.8 KB
056. Chapter 7. Single-shot detector (SSD).mp4 71.4 MB
057. Chapter 7. You only look once (YOLO).en.srt 18.4 KB
057. Chapter 7. You only look once (YOLO).mp4 32.2 MB
058. Chapter 7. Project - Train an SSD network in a self-driving car application.en.srt 10.8 KB
058. Chapter 7. Project - Train an SSD network in a self-driving car application.mp4 31.2 MB
059. Chapter 7. Summary.en.srt 1.8 KB
059. Chapter 7. Summary.mp4 10.9 MB
060. Part 3. Generative models and visual embeddings.en.srt 1.2 KB
060. Part 3. Generative models and visual embeddings.mp4 2.6 MB
061. Chapter 8. Generative adversarial networks (GANs).en.srt 32.7 KB
061. Chapter 8. Generative adversarial networks (GANs).mp4 74.8 MB
062. Chapter 8. Evaluating GAN models.en.srt 9.4 KB
062. Chapter 8. Evaluating GAN models.mp4 18.9 MB
063. Chapter 8. Popular GAN applications.en.srt 7.8 KB
063. Chapter 8. Popular GAN applications.mp4 16.6 MB
064. Chapter 8. Project - Building your own GAN.en.srt 11.0 KB
064. Chapter 8. Project - Building your own GAN.mp4 44.0 MB
065. Chapter 8. Summary.en.srt 1.8 KB
065. Chapter 8. Summary.mp4 8.7 MB
066. Chapter 9. DeepDream and neural style transfer.en.srt 29.3 KB
066. Chapter 9. DeepDream and neural style transfer.mp4 66.6 MB
067. Chapter 9. DeepDream.en.srt 13.1 KB
067. Chapter 9. DeepDream.mp4 38.2 MB
068. Chapter 9. Neural style transfer.en.srt 18.6 KB
068. Chapter 9. Neural style transfer.mp4 51.7 MB
069. Chapter 9. Summary.en.srt 3.0 KB
069. Chapter 9. Summary.mp4 10.7 MB
070. Chapter 10. Visual embeddings.en.srt 14.7 KB
070. Chapter 10. Visual embeddings.mp4 41.8 MB
071. Chapter 10. Learning embedding.en.srt 2.4 KB
071. Chapter 10. Learning embedding.mp4 8.5 MB
072. Chapter 10. Loss functions.en.srt 18.5 KB
072. Chapter 10. Loss functions.mp4 37.2 MB
073. Chapter 10. Mining informative data.en.srt 25.1 KB
073. Chapter 10. Mining informative data.mp4 68.6 MB
074. Chapter 10. Project - Train an embedding network.en.srt 22.0 KB
074. Chapter 10. Project - Train an embedding network.mp4 68.9 MB
075. Chapter 10. Pushing the boundaries of current accuracy.en.srt 5.3 KB
075. Chapter 10. Pushing the boundaries of current accuracy.mp4 12.7 MB
076. Chapter 10. Summary.en.srt 666 bytes
076. Chapter 10. Summary.mp4 6.9 MB
Bonus Resources.txt 70 bytes

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