FreeCoursesOnline Me Packt Troubleshooting Python Deep Learning FCO

1. 1. Solutions to Convolutional Neural Network Problems – Part One_/01.The Course Overview.mp437.64MB
2. 1. Solutions to Convolutional Neural Network Problems – Part One_/02.Concatenate Two CNNs Correctly.mp473.99MB
3. 1. Solutions to Convolutional Neural Network Problems – Part One_/03.Splitting Trained Model.mp413.19MB
4. 1. Solutions to Convolutional Neural Network Problems – Part One_/04.Resolving fit_generator Errors.mp47.63MB
5. 1. Solutions to Convolutional Neural Network Problems – Part One_/05.Model Object Has No Attribute load_model Keras.mp43.19MB
6. 1. Solutions to Convolutional Neural Network Problems – Part One_/06.High val_acc, But Low Accuracy in Practice.mp411.03MB
7. 1. Solutions to Convolutional Neural Network Problems – Part One_/07.Error in Adding a Dense Layer.mp44.83MB
8. 1. Solutions to Convolutional Neural Network Problems – Part One_/08.Model with Multiple Outputs Errors.mp43.86MB
9. 1. Solutions to Convolutional Neural Network Problems – Part One_/09.Model That Uses Dropout Is Still Overfitting.mp48.09MB
10. 2. Solutions to Convolutional Neural Network Problems – Part Two_/10.When the Value Error Input 0 Is Incompatible with Layer conv2d_1.mp46.42MB
11. 2. Solutions to Convolutional Neural Network Problems – Part Two_/11.Interpreting kernel_size Notation in CNNs.mp49.92MB
12. 2. Solutions to Convolutional Neural Network Problems – Part Two_/12.Choosing Last Layer’s Activation Function in CNN.mp47.53MB
13. 2. Solutions to Convolutional Neural Network Problems – Part Two_/13.Using Validation Accuracy.mp48.82MB
14. 2. Solutions to Convolutional Neural Network Problems – Part Two_/14.Error When Using CNN to Classify Text.mp48.3MB
15. 2. Solutions to Convolutional Neural Network Problems – Part Two_/15.Kernel Weight Initialization in CNN Model.mp45.36MB
16. 2. Solutions to Convolutional Neural Network Problems – Part Two_/16.Common Problems When Using Pre-Trained CNN Models.mp47.6MB
17. 2. Solutions to Convolutional Neural Network Problems – Part Two_/17.Shape Error When Training CIFAR-10 Dataset on CNN.mp48.68MB
18. 3. Solutions to Recurrent Neural Network Problems_/18.Building an RNN Model in Keras.mp48.12MB
19. 3. Solutions to Recurrent Neural Network Problems_/19.Wrong Input - ValueError – Error When Checking Input.mp413.17MB
20. 3. Solutions to Recurrent Neural Network Problems_/20.Correct Text Preparation for Machine Translation.mp410.45MB
21. 3. Solutions to Recurrent Neural Network Problems_/21.Handling Invalid Input Shape Error.mp47.89MB
22. 3. Solutions to Recurrent Neural Network Problems_/22.Mapping Series of Vectors to a Single Vector.mp47.01MB
23. 3. Solutions to Recurrent Neural Network Problems_/23.Resolving a Bad Output from RNN While Generating a Simple Sequence.mp46.26MB
24. 3. Solutions to Recurrent Neural Network Problems_/24.Preparing Data Correctly for Time Series Prediction.mp49.66MB
25. 3. Solutions to Recurrent Neural Network Problems_/25.How to Enable Stateful RNN.mp46.99MB
26. 4.Solutions to LSTM Recurrent Neural Networks Problems/26.Stacking Multiple LSTM in Keras TypeError - Call() Got an Unexpected Keyword Argument 'return_sequences'.mp49.61MB
27. 4.Solutions to LSTM Recurrent Neural Networks Problems/27.Working with Different Lengths of Input and Output Sequences.mp415.61MB
28. 4.Solutions to LSTM Recurrent Neural Networks Problems/28.How to Use Stacked LSTMs.mp46.13MB
29. 4.Solutions to LSTM Recurrent Neural Networks Problems/29.Using CNN-LSTM for Time Series Prediction.mp48.48MB
30. 4.Solutions to LSTM Recurrent Neural Networks Problems/30.Solving LSTM Underfitting on Time Series Problem.mp46.03MB
31. 4.Solutions to LSTM Recurrent Neural Networks Problems/31.Using LSTM for Multi-Value Prediction.mp45.29MB
32. 4.Solutions to LSTM Recurrent Neural Networks Problems/32.How To Do Text Classification with LSTM.mp411.39MB
33. 4.Solutions to LSTM Recurrent Neural Networks Problems/33.Data Preparation for Seq2Seq Learning.mp47.7MB
34. 5. Troubleshooting Models with scikit-learn_/34.LabelBinarizer Returns Vector When There Are Two Classes.mp47.95MB
35. 5. Troubleshooting Models with scikit-learn_/35.Handling Missing Values.mp412.64MB
36. 5. Troubleshooting Models with scikit-learn_/36.Evaluating Deep Learning Models Using Additional Metrics.mp48.7MB
37. 5. Troubleshooting Models with scikit-learn_/37.Fixing Warning Messages.mp410.3MB
38. 5. Troubleshooting Models with scikit-learn_/38.Generating Test Datasets.mp46.9MB
39. 5. Troubleshooting Models with scikit-learn_/39.Normalizing and Standardizing the Data.mp46.84MB
40. 5. Troubleshooting Models with scikit-learn_/40.Preparing Text for Use with Deep Learning Models.mp48.18MB
41. 6. Solving NumPy Problems_/41.Converting a 2D Matrix to a One-Hot Encoded Matrix.mp48.55MB
42. 6. Solving NumPy Problems_/42.Reshaping a 2D NumPy Array to 3D Array.mp44.84MB
43. 6. Solving NumPy Problems_/43.Fix load.npy Error in Python3.mp416.54MB
44. 6. Solving NumPy Problems_/44.Turn ND Matrix Into 1D Vector.mp430.07MB