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文档列表

Chapter 7 - Recurrent Neural Networks and LSTM/35. Writing Music a la Bach.mp4 47.1 MB
Chapter 1 - Exploring and Transforming data/04. Basic Tensor Methods.mp4 38.7 MB
Chapter 4 - Logistic Regression/18. Univariate Logistic Regression.mp4 33.2 MB
Chapter 1 - Exploring and Transforming data/02. TensorFlow's Main Data Structure Tensors.mp4 28.5 MB
Chapter 3 - Linear Regression/14. Univariate Linear Regression.mp4 26.5 MB
Chapter 1 - Exploring and Transforming data/05. How TensorBoard Works.mp4 25.8 MB
Chapter 1 - Exploring and Transforming data/06. Reading Information from Disk.mp4 22.9 MB
Chapter 3 - Linear Regression/15. Multivariate Linear Regression.mp4 22.6 MB
Chapter 2 - Clustering/10. Project 1 k-Means Clustering on Synthetic Datasetsets.mp4 20.4 MB
Chapter 2 - Clustering/09. k-Nearest Neighbor.mp4 19.8 MB
Chapter 1 - Exploring and Transforming data/01. The Course Overview.mp4 19.5 MB
Chapter 6 - Convolutional Neural Networks/25. Applying Convolution in TensorFlow.mp4 18.8 MB
Chapter 6 - Convolutional Neural Networks/29. MNIST Digit Classification.mp4 18.7 MB
Chapter 1 - Exploring and Transforming data/03. Handling the Computing Workflow TensorFlow's Data Flow Graph.mp4 17.0 MB
Chapter 5 - Simple FeedForward Neural Networks/22. Second Project Modeling Cars Fuel Efficiency with Non-Linear.mp4 16.4 MB
Chapter 8 - Deep Neural Networks/40. Painting with Style VGG Style Transfer.mp4 16.2 MB
Chapter 7 - Recurrent Neural Networks and LSTM/34. Univariate Time Series Prediction with Energy Consumption Data.mp4 14.5 MB
Chapter 9 - Library Installation And Additional Tips/42. mac OS Installation.mp4 14.3 MB
Chapter 5 - Simple FeedForward Neural Networks/21. First Project Non-Linear Synthetic Function Regression.mp4 14.3 MB
Chapter 5 - Simple FeedForward Neural Networks/20. Preliminary Concepts.mp4 14.0 MB
Chapter 6 - Convolutional Neural Networks/30. Image Classification with the CIFAR10 Dataset.mp4 13.5 MB
Chapter 9 - Library Installation And Additional Tips/41. Windows Installation.mp4 13.4 MB
Chapter 5 - Simple FeedForward Neural Networks/23. Third Project Learning to Classify Wines Multiclass Classification.mp4 13.2 MB
Chapter 4 - Logistic Regression/19. Univariate Logistic Regression with keras.mp4 13.1 MB
Chapter 6 - Convolutional Neural Networks/26. Subsampling Operation Pooling.mp4 11.4 MB
Chapter 2 - Clustering/11. Project 2 Nearest Neighbor on Synthetic Datasets.mp4 10.4 MB
Chapter 8 - Deep Neural Networks/37. Alexnet.mp4 10.2 MB
Chapter 4 - Logistic Regression/17. The Logistic Function.mp4 10.0 MB
Chapter 3 - Linear Regression/12. Univariate Linear Modelling Function.mp4 9.2 MB
Chapter 7 - Recurrent Neural Networks and LSTM/32. A Fundamental Component Gate Operation and Its Steps.mp4 7.4 MB
Chapter 4 - Logistic Regression/16. Logistic Function Predecessor The Logit Functions.mp4 7.2 MB
Chapter 7 - Recurrent Neural Networks and LSTM/31. Recurrent Neural Networks.mp4 6.8 MB
Chapter 6 - Convolutional Neural Networks/27. Improving Efficiency Dropout Operation.mp4 6.5 MB
Chapter 2 - Clustering/08. Mechanics of k-Means.mp4 6.1 MB
Chapter 3 - Linear Regression/13. Optimizer Methods in TensorFlow The Train Module.mp4 5.8 MB
Chapter 6 - Convolutional Neural Networks/24. Origin of Convolutional Neural Networks.mp4 5.7 MB
Chapter 8 - Deep Neural Networks/36. Deep Neural Network Definition and Architectures Through Time.mp4 5.1 MB
Chapter 2 - Clustering/07. Learning from Data Unsupervised Learing.mp4 4.9 MB
Chapter 8 - Deep Neural Networks/39. Residual Networks (ResNet).mp4 3.4 MB
Chapter 7 - Recurrent Neural Networks and LSTM/33. TensorFlow LSTM Useful Classes and Methods.mp4 3.2 MB
Chapter 6 - Convolutional Neural Networks/28. Convolutional Type Layer Building Methods.mp4 3.0 MB
Chapter 8 - Deep Neural Networks/38. Inception V3.mp4 1.9 MB
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