[Manning] Deep learning patterns practices (hevc) (2021) [EN]
- 收录时间:2022-06-03 11:10:47
- 文件大小:478MB
- 下载次数:1
- 最近下载:2022-06-03 11:10:47
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文件列表
- 02 - Ch1 Designing modern machine learning.m4v 8MB
- 84 - Ch14 Training schedulers.m4v 7MB
- 10 - Ch2 DNN binary classifier.m4v 7MB
- 81 - Ch14 Training and deployment pipeline.m4v 7MB
- 42 - Ch7 Alternative connectivity patterns.m4v 7MB
- 78 - Ch13 Data preprocessing.m4v 7MB
- 66 - Ch11 Transfer learning.m4v 7MB
- 88 - Ch14 TFX pipeline components for deployment.m4v 7MB
- 47 - Ch8 Mobile convolutional neural networks.m4v 7MB
- 57 - Ch9 Super-resolution.m4v 7MB
- 83 - Ch14 Model feeding with TFX.m4v 7MB
- 69 - Ch11 Distinct tasks.m4v 7MB
- 87 - Ch14 Serving predictions.m4v 7MB
- 72 - Ch12 Training as a CNN.m4v 7MB
- 09 - Ch2 Activation functions.m4v 7MB
- 82 - Ch14 Model feeding with tf.data.Dataset.m4v 7MB
- 64 - Ch10 Learning rate scheduler.m4v 7MB
- 49 - Ch8 MobileNet v2.m4v 7MB
- 40 - Ch6 ResNeXt - Wide residual neural networks.m4v 7MB
- 37 - Ch6 Inception v1 module.m4v 7MB
- 55 - Ch9 Autoencoders.m4v 7MB
- 70 - Ch12 Data distributions.m4v 6MB
- 11 - Ch2 Simple image classifier.m4v 6MB
- 71 - Ch12 Out of distribution.m4v 6MB
- 80 - Ch13 Data augmentation.m4v 6MB
- 76 - Ch13 TFRecord format.m4v 6MB
- 67 - Ch11 New classifier.m4v 6MB
- 36 - Ch6 Wide convolutional neural networks.m4v 6MB
- 50 - Ch8 SqueezeNet.m4v 6MB
- 34 - Ch5 Task component.m4v 6MB
- 33 - Ch5 Pre-stem.m4v 6MB
- 68 - Ch11 TF Hub prebuilt models.m4v 6MB
- 04 - Ch1 Next steps in computer learning - Part 1.m4v 6MB
- 07 - Ch2 Deep neural networks.m4v 6MB
- 22 - Ch4 Convergence.m4v 6MB
- 44 - Ch7 Xception - Extreme Inception.m4v 6MB
- 14 - Ch3 The ConvNet design for a CNN.m4v 6MB
- 75 - Ch13 HDF5 format.m4v 6MB
- 85 - Ch14 Model evaluations.m4v 6MB
- 38 - Ch6 Inception v2 - Factoring convolutions.m4v 6MB
- 16 - Ch3 Architecture.m4v 6MB
- 31 - Ch5 Stem component.m4v 6MB
- 58 - Ch9 Pretext tasks.m4v 6MB
- 48 - Ch8 Stem.m4v 5MB
- 77 - Ch13 Data feeding.m4v 5MB
- 05 - Ch1 Next steps in computer learning - Part 2.m4v 5MB
- 74 - Ch13 Compressed and raw-image formats.m4v 5MB
- 35 - Ch5 Beyond computer vision - NLP.m4v 5MB
- 25 - Ch4 Invariance.m4v 5MB
- 65 - Ch10 Regularization.m4v 5MB
- 21 - Ch4 Validation and overfitting.m4v 5MB
- 18 - Ch4 Training fundamentals.m4v 5MB
- 52 - Ch8 ShuffleNet v1.m4v 5MB
- 51 - Ch8 Classifier.m4v 5MB
- 63 - Ch10 Random search.m4v 5MB
- 56 - Ch9 Convolutional autoencoders.m4v 5MB
- 12 - Ch3 Convolutional and residual neural networks.m4v 5MB
- 62 - Ch10 Hyperparameter search fundamentals.m4v 5MB
- 41 - Ch6 Beyond computer vision - Structured data.m4v 5MB
- 23 - Ch4 Hyperparameters.m4v 5MB
- 30 - Ch5 Procedural design pattern.m4v 5MB
- 54 - Ch8 Deployment.m4v 5MB
- 60 - Ch10 Hyperparameter tuning.m4v 5MB
- 08 - Ch2 Sequential API method.m4v 5MB
- 03 - Ch1 The evolution in machine learning approaches.m4v 4MB
- 06 - Ch1 The benefits of design patterns.m4v 4MB
- 19 - Ch4 Dataset splitting.m4v 4MB
- 32 - Ch5 ResNet.m4v 4MB
- 61 - Ch10 Lottery hypothesis.m4v 4MB
- Manning.Deep.learning.patterns.practices.2021.pdf 4MB
- 27 - Ch4 Raw (disk) datasets.m4v 4MB
- 13 - Ch3 Feature detection.m4v 4MB
- 45 - Ch7 Exit flow of Xception.m4v 4MB
- 79 - Ch13 Preprocessing with TF Extended.m4v 4MB
- 15 - Ch3 VGG networks.m4v 4MB
- 46 - Ch7 SE-Net - Squeeze and excitation.m4v 4MB
- 17 - Ch3 Batch normalization.m4v 4MB
- 39 - Ch6 Normal convolution.m4v 4MB
- 89 - Ch14 Evolution in production pipeline design.m4v 4MB
- 53 - Ch8 Learner.m4v 4MB
- 28 - Ch4 Resizing.m4v 4MB
- 43 - Ch7 Dense block.m4v 3MB
- 24 - Ch4 Learning rate.m4v 3MB
- 86 - Ch14 TFX evaluation.m4v 3MB
- 73 - Ch13 Data pipeline.m4v 3MB
- 20 - Ch4 Data normalization.m4v 3MB
- 26 - Ch4 Scale invariance.m4v 3MB
- 59 - Part 3. Working with pipelines.m4v 1MB
- 29 - Part 2. Basic design pattern.m4v 1MB
- 01 - Part 1. Deep learning fundamentals.m4v 599KB
