FreeCoursesOnline Me Packtpub Com Building Machine Learning Systems with TensorFlow - FCO

1. Chapter 1 - Exploring and Transforming data/01. The Course Overview.mp418.55MB
2. Chapter 1 - Exploring and Transforming data/02. TensorFlow's Main Data Structure Tensors.mp427.13MB
3. Chapter 1 - Exploring and Transforming data/03. Handling the Computing Workflow TensorFlow's Data Flow Graph.mp416.2MB
4. Chapter 1 - Exploring and Transforming data/04. Basic Tensor Methods.mp436.91MB
5. Chapter 1 - Exploring and Transforming data/05. How TensorBoard Works.mp424.56MB
6. Chapter 1 - Exploring and Transforming data/06. Reading Information from Disk.mp421.8MB
7. Chapter 2 - Clustering/07. Learning from Data Unsupervised Learing.mp44.63MB
8. Chapter 2 - Clustering/08. Mechanics of k-Means.mp45.81MB
9. Chapter 2 - Clustering/09. k-Nearest Neighbor.mp418.88MB
10. Chapter 2 - Clustering/10. Project 1 k-Means Clustering on Synthetic Datasetsets.mp419.49MB
11. Chapter 2 - Clustering/11. Project 2 Nearest Neighbor on Synthetic Datasets.mp49.92MB
12. Chapter 3 - Linear Regression/12. Univariate Linear Modelling Function.mp48.77MB
13. Chapter 3 - Linear Regression/13. Optimizer Methods in TensorFlow The Train Module.mp45.53MB
14. Chapter 3 - Linear Regression/14. Univariate Linear Regression.mp425.25MB
15. Chapter 3 - Linear Regression/15. Multivariate Linear Regression.mp421.52MB
16. Chapter 4 - Logistic Regression/16. Logistic Function Predecessor The Logit Functions.mp46.9MB
17. Chapter 4 - Logistic Regression/17. The Logistic Function.mp49.58MB
18. Chapter 4 - Logistic Regression/18. Univariate Logistic Regression.mp431.62MB
19. Chapter 4 - Logistic Regression/19. Univariate Logistic Regression with keras.mp412.47MB
20. Chapter 5 - Simple FeedForward Neural Networks/20. Preliminary Concepts.mp413.36MB
21. Chapter 5 - Simple FeedForward Neural Networks/21. First Project Non-Linear Synthetic Function Regression.mp413.63MB
22. Chapter 5 - Simple FeedForward Neural Networks/22. Second Project Modeling Cars Fuel Efficiency with Non-Linear.mp415.6MB
23. Chapter 5 - Simple FeedForward Neural Networks/23. Third Project Learning to Classify Wines Multiclass Classification.mp412.61MB
24. Chapter 6 - Convolutional Neural Networks/24. Origin of Convolutional Neural Networks.mp45.4MB
25. Chapter 6 - Convolutional Neural Networks/25. Applying Convolution in TensorFlow.mp417.9MB
26. Chapter 6 - Convolutional Neural Networks/26. Subsampling Operation Pooling.mp410.85MB
27. Chapter 6 - Convolutional Neural Networks/27. Improving Efficiency Dropout Operation.mp46.17MB
28. Chapter 6 - Convolutional Neural Networks/28. Convolutional Type Layer Building Methods.mp42.9MB
29. Chapter 6 - Convolutional Neural Networks/29. MNIST Digit Classification.mp417.87MB
30. Chapter 6 - Convolutional Neural Networks/30. Image Classification with the CIFAR10 Dataset.mp412.92MB
31. Chapter 7 - Recurrent Neural Networks and LSTM/31. Recurrent Neural Networks.mp46.48MB
32. Chapter 7 - Recurrent Neural Networks and LSTM/32. A Fundamental Component Gate Operation and Its Steps.mp47.07MB
33. Chapter 7 - Recurrent Neural Networks and LSTM/33. TensorFlow LSTM Useful Classes and Methods.mp43.05MB
34. Chapter 7 - Recurrent Neural Networks and LSTM/34. Univariate Time Series Prediction with Energy Consumption Data.mp413.81MB
35. Chapter 7 - Recurrent Neural Networks and LSTM/35. Writing Music a la Bach.mp444.93MB
36. Chapter 8 - Deep Neural Networks/36. Deep Neural Network Definition and Architectures Through Time.mp44.87MB
37. Chapter 8 - Deep Neural Networks/37. Alexnet.mp49.77MB
38. Chapter 8 - Deep Neural Networks/38. Inception V3.mp41.85MB
39. Chapter 8 - Deep Neural Networks/39. Residual Networks (ResNet).mp43.26MB
40. Chapter 8 - Deep Neural Networks/40. Painting with Style VGG Style Transfer.mp415.43MB
41. Chapter 9 - Library Installation And Additional Tips/41. Windows Installation.mp412.74MB
42. Chapter 9 - Library Installation And Additional Tips/42. mac OS Installation.mp413.66MB