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001. Part 1. Deep learning foundation.en.srt
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001. Part 1. Deep learning foundation.mp4
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002. Chapter 1. Welcome to computer vision.en.srt
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002. Chapter 1. Welcome to computer vision.mp4
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003. Chapter 1. Applications of computer vision.en.srt
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003. Chapter 1. Applications of computer vision.mp4
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004. Chapter 1. Computer vision pipeline - The big picture.en.srt
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004. Chapter 1. Computer vision pipeline - The big picture.mp4
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005. Chapter 1. Image input.en.srt
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005. Chapter 1. Image input.mp4
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006. Chapter 1. Image preprocessing.en.srt
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006. Chapter 1. Image preprocessing.mp4
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20 MB
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007. Chapter 1. Feature extraction.en.srt
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007. Chapter 1. Feature extraction.mp4
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44.8 MB
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008. Chapter 1. Classifier learning algorithm.en.srt
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008. Chapter 1. Classifier learning algorithm.mp4
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009. Chapter 1. Summary.en.srt
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009. Chapter 1. Summary.mp4
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3 MB
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010. Chapter 2. Deep learning and neural networks.en.srt
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010. Chapter 2. Deep learning and neural networks.mp4
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58.3 MB
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011. Chapter 2. Multilayer perceptrons.en.srt
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011. Chapter 2. Multilayer perceptrons.mp4
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62.5 MB
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012. Chapter 2. Activation functions.en.srt
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012. Chapter 2. Activation functions.mp4
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53.7 MB
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013. Chapter 2. The feedforward process.en.srt
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13 KB
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013. Chapter 2. The feedforward process.mp4
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41 MB
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014. Chapter 2. Error functions.en.srt
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014. Chapter 2. Error functions.mp4
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27.5 MB
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015. Chapter 2. Optimization algorithms.en.srt
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015. Chapter 2. Optimization algorithms.mp4
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85 MB
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016. Chapter 2. Backpropagation.en.srt
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016. Chapter 2. Backpropagation.mp4
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18.8 MB
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017. Chapter 2. Summary.en.srt
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017. Chapter 2. Summary.mp4
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018. Chapter 3. Convolutional neural networks.en.srt
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018. Chapter 3. Convolutional neural networks.mp4
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76.6 MB
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019. Chapter 3. CNN architecture.en.srt
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019. Chapter 3. CNN architecture.mp4
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34.4 MB
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020. Chapter 3. Basic components of a CNN.en.srt
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020. Chapter 3. Basic components of a CNN.mp4
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96.7 MB
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021. Chapter 3. Image classification using CNNs.en.srt
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021. Chapter 3. Image classification using CNNs.mp4
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22.5 MB
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022. Chapter 3. Adding dropout layers to avoid overfitting.en.srt
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022. Chapter 3. Adding dropout layers to avoid overfitting.mp4
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20.2 MB
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023. Chapter 3. Convolution over color images (3D images).en.srt
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023. Chapter 3. Convolution over color images (3D images).mp4
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18.3 MB
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024. Chapter 3. Project - Image classification for color images.en.srt
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024. Chapter 3. Project - Image classification for color images.mp4
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62.5 MB
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025. Chapter 3. Summary.en.srt
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025. Chapter 3. Summary.mp4
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026. Chapter 4. Structuring DL projects and hyperparameter tuning.en.srt
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026. Chapter 4. Structuring DL projects and hyperparameter tuning.mp4
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027. Chapter 4. Designing a baseline model.en.srt
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027. Chapter 4. Designing a baseline model.mp4
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028. Chapter 4. Getting your data ready for training.en.srt
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028. Chapter 4. Getting your data ready for training.mp4
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029. Chapter 4. Evaluating the model and interpreting its performance.en.srt
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029. Chapter 4. Evaluating the model and interpreting its performance.mp4
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030. Chapter 4. Improving the network and tuning hyperparameters.en.srt
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030. Chapter 4. Improving the network and tuning hyperparameters.mp4
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031. Chapter 4. Learning and optimization.en.srt
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031. Chapter 4. Learning and optimization.mp4
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032. Chapter 4. Optimization algorithms.en.srt
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032. Chapter 4. Optimization algorithms.mp4
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033. Chapter 4. Regularization techniques to avoid overfitting.en.srt
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033. Chapter 4. Regularization techniques to avoid overfitting.mp4
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034. Chapter 4. Batch normalization.en.srt
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034. Chapter 4. Batch normalization.mp4
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035. Chapter 4. Project - Achieve high accuracy on image classification.en.srt
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035. Chapter 4. Project - Achieve high accuracy on image classification.mp4
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30.6 MB
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036. Chapter 4. Summary.en.srt
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036. Chapter 4. Summary.mp4
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037. Part 2. Image classification and detection.en.srt
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037. Part 2. Image classification and detection.mp4
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038. Chapter 5. Advanced CNN architectures.en.srt
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038. Chapter 5. Advanced CNN architectures.mp4
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039. Chapter 5. LeNet-5.en.srt
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039. Chapter 5. LeNet-5.mp4
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040. Chapter 5. AlexNet.en.srt
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040. Chapter 5. AlexNet.mp4
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49 MB
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041. Chapter 5. VGGNet.en.srt
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041. Chapter 5. VGGNet.mp4
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042. Chapter 5. Inception and GoogLeNet.en.srt
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042. Chapter 5. Inception and GoogLeNet.mp4
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043. Chapter 5. ResNet.en.srt
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043. Chapter 5. ResNet.mp4
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59.4 MB
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044. Chapter 5. Summary.en.srt
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044. Chapter 5. Summary.mp4
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9 MB
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045. Chapter 6. Transfer learning.en.srt
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045. Chapter 6. Transfer learning.mp4
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046. Chapter 6. What is transfer learning.en.srt
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046. Chapter 6. What is transfer learning.mp4
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047. Chapter 6. How transfer learning works.en.srt
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13 KB
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047. Chapter 6. How transfer learning works.mp4
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048. Chapter 6. Transfer learning approaches.en.srt
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048. Chapter 6. Transfer learning approaches.mp4
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049. Chapter 6. Choosing the appropriate level of transfer learning.en.srt
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049. Chapter 6. Choosing the appropriate level of transfer learning.mp4
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050. Chapter 6. Open source datasets.en.srt
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050. Chapter 6. Open source datasets.mp4
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23.9 MB
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051. Chapter 6. Project 1 - A pretrained network as a feature extractor.en.srt
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051. Chapter 6. Project 1 - A pretrained network as a feature extractor.mp4
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31.9 MB
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052. Chapter 6. Project 2 - Fine-tuning.en.srt
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052. Chapter 6. Project 2 - Fine-tuning.mp4
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24 MB
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053. Chapter 6. Summary.en.srt
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053. Chapter 6. Summary.mp4
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054. Chapter 7. Object detection with R-CNN, SSD, and YOLO.en.srt
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054. Chapter 7. Object detection with R-CNN, SSD, and YOLO.mp4
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76.4 MB
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055. Chapter 7. Region-based convolutional neural networks (R-CNNs).en.srt
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055. Chapter 7. Region-based convolutional neural networks (R-CNNs).mp4
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056. Chapter 7. Single-shot detector (SSD).en.srt
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056. Chapter 7. Single-shot detector (SSD).mp4
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057. Chapter 7. You only look once (YOLO).en.srt
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057. Chapter 7. You only look once (YOLO).mp4
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32.2 MB
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058. Chapter 7. Project - Train an SSD network in a self-driving car application.en.srt
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058. Chapter 7. Project - Train an SSD network in a self-driving car application.mp4
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31.2 MB
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059. Chapter 7. Summary.en.srt
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059. Chapter 7. Summary.mp4
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060. Part 3. Generative models and visual embeddings.en.srt
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060. Part 3. Generative models and visual embeddings.mp4
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061. Chapter 8. Generative adversarial networks (GANs).en.srt
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061. Chapter 8. Generative adversarial networks (GANs).mp4
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062. Chapter 8. Evaluating GAN models.en.srt
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062. Chapter 8. Evaluating GAN models.mp4
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063. Chapter 8. Popular GAN applications.en.srt
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063. Chapter 8. Popular GAN applications.mp4
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064. Chapter 8. Project - Building your own GAN.en.srt
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064. Chapter 8. Project - Building your own GAN.mp4
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065. Chapter 8. Summary.en.srt
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065. Chapter 8. Summary.mp4
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066. Chapter 9. DeepDream and neural style transfer.en.srt
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066. Chapter 9. DeepDream and neural style transfer.mp4
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067. Chapter 9. DeepDream.en.srt
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067. Chapter 9. DeepDream.mp4
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068. Chapter 9. Neural style transfer.en.srt
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068. Chapter 9. Neural style transfer.mp4
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069. Chapter 9. Summary.en.srt
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069. Chapter 9. Summary.mp4
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070. Chapter 10. Visual embeddings.en.srt
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070. Chapter 10. Visual embeddings.mp4
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41.8 MB
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071. Chapter 10. Learning embedding.en.srt
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071. Chapter 10. Learning embedding.mp4
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072. Chapter 10. Loss functions.en.srt
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072. Chapter 10. Loss functions.mp4
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073. Chapter 10. Mining informative data.en.srt
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073. Chapter 10. Mining informative data.mp4
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68.6 MB
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074. Chapter 10. Project - Train an embedding network.en.srt
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22 KB
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074. Chapter 10. Project - Train an embedding network.mp4
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68.9 MB
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075. Chapter 10. Pushing the boundaries of current accuracy.en.srt
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075. Chapter 10. Pushing the boundaries of current accuracy.mp4
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12.7 MB
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076. Chapter 10. Summary.en.srt
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716.8 B
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076. Chapter 10. Summary.mp4
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6.9 MB
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Bonus Resources.txt
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TXT
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102.4 B
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Get Bonus Downloads Here.url
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URL
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204.8 B
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