Packt | Troubleshooting Python Deep Learning [FCO] GloDLS
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Torrent File Contents
Packt | Troubleshooting Python Deep Learning [FCO] GloDLS
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1. Solutions to Convolutional Neural Network Problems – Part One_/01.The Course Overview.mp4
1. Solutions to Convolutional Neural Network Problems – Part One_/02.Concatenate Two CNNs Correctly.mp4
1. Solutions to Convolutional Neural Network Problems – Part One_/03.Splitting Trained Model.mp4
1. Solutions to Convolutional Neural Network Problems – Part One_/04.Resolving fit_generator Errors.mp4
1. Solutions to Convolutional Neural Network Problems – Part One_/05.Model Object Has No Attribute load_model Keras.mp4
1. Solutions to Convolutional Neural Network Problems – Part One_/06.High val_acc, But Low Accuracy in Practice.mp4
1. Solutions to Convolutional Neural Network Problems – Part One_/07.Error in Adding a Dense Layer.mp4
1. Solutions to Convolutional Neural Network Problems – Part One_/08.Model with Multiple Outputs Errors.mp4
1. Solutions to Convolutional Neural Network Problems – Part One_/09.Model That Uses Dropout Is Still Overfitting.mp4
2. Solutions to Convolutional Neural Network Problems – Part Two_/10.When the Value Error Input 0 Is Incompatible with Layer conv2d_1.mp4
2. Solutions to Convolutional Neural Network Problems – Part Two_/11.Interpreting kernel_size Notation in CNNs.mp4
2. Solutions to Convolutional Neural Network Problems – Part Two_/12.Choosing Last Layer’s Activation Function in CNN.mp4
2. Solutions to Convolutional Neural Network Problems – Part Two_/13.Using Validation Accuracy.mp4
2. Solutions to Convolutional Neural Network Problems – Part Two_/14.Error When Using CNN to Classify Text.mp4
2. Solutions to Convolutional Neural Network Problems – Part Two_/15.Kernel Weight Initialization in CNN Model.mp4
2. Solutions to Convolutional Neural Network Problems – Part Two_/16.Common Problems When Using Pre-Trained CNN Models.mp4
2. Solutions to Convolutional Neural Network Problems – Part Two_/17.Shape Error When Training CIFAR-10 Dataset on CNN.mp4
3. Solutions to Recurrent Neural Network Problems_/18.Building an RNN Model in Keras.mp4
3. Solutions to Recurrent Neural Network Problems_/19.Wrong Input - ValueError – Error When Checking Input.mp4
3. Solutions to Recurrent Neural Network Problems_/20.Correct Text Preparation for Machine Translation.mp4
3. Solutions to Recurrent Neural Network Problems_/21.Handling Invalid Input Shape Error.mp4
3. Solutions to Recurrent Neural Network Problems_/22.Mapping Series of Vectors to a Single Vector.mp4
3. Solutions to Recurrent Neural Network Problems_/23.Resolving a Bad Output from RNN While Generating a Simple Sequence.mp4
3. Solutions to Recurrent Neural Network Problems_/24.Preparing Data Correctly for Time Series Prediction.mp4
3. Solutions to Recurrent Neural Network Problems_/25.How to Enable Stateful RNN.mp4
4.Solutions to LSTM Recurrent Neural Networks Problems/26.Stacking Multiple LSTM in Keras TypeError - Call() Got an Unexpected Keyword Argument 'return_sequences'.mp4
4.Solutions to LSTM Recurrent Neural Networks Problems/27.Working with Different Lengths of Input and Output Sequences.mp4
4.Solutions to LSTM Recurrent Neural Networks Problems/28.How to Use Stacked LSTMs.mp4
4.Solutions to LSTM Recurrent Neural Networks Problems/29.Using CNN-LSTM for Time Series Prediction.mp4
4.Solutions to LSTM Recurrent Neural Networks Problems/30.Solving LSTM Underfitting on Time Series Problem.mp4
4.Solutions to LSTM Recurrent Neural Networks Problems/31.Using LSTM for Multi-Value Prediction.mp4
4.Solutions to LSTM Recurrent Neural Networks Problems/32.How To Do Text Classification with LSTM.mp4
4.Solutions to LSTM Recurrent Neural Networks Problems/33.Data Preparation for Seq2Seq Learning.mp4
5. Troubleshooting Models with scikit-learn_/34.LabelBinarizer Returns Vector When There Are Two Classes.mp4
5. Troubleshooting Models with scikit-learn_/35.Handling Missing Values.mp4
5. Troubleshooting Models with scikit-learn_/36.Evaluating Deep Learning Models Using Additional Metrics.mp4
5. Troubleshooting Models with scikit-learn_/37.Fixing Warning Messages.mp4
5. Troubleshooting Models with scikit-learn_/38.Generating Test Datasets.mp4
5. Troubleshooting Models with scikit-learn_/39.Normalizing and Standardizing the Data.mp4
5. Troubleshooting Models with scikit-learn_/40.Preparing Text for Use with Deep Learning Models.mp4
6. Solving NumPy Problems_/41.Converting a 2D Matrix to a One-Hot Encoded Matrix.mp4
6. Solving NumPy Problems_/42.Reshaping a 2D NumPy Array to 3D Array.mp4
6. Solving NumPy Problems_/43.Fix load.npy Error in Python3.mp4
6. Solving NumPy Problems_/44.Turn ND Matrix Into 1D Vector.mp4
Exercise Files/code_37489.zip
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0. Websites you may like/How you can help Team-FTU.txt
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1. Solutions to Convolutional Neural Network Problems – Part One_/01.The Course Overview.mp4
- 37.64 MB
1. Solutions to Convolutional Neural Network Problems – Part One_/02.Concatenate Two CNNs Correctly.mp4
- 73.99 MB
1. Solutions to Convolutional Neural Network Problems – Part One_/03.Splitting Trained Model.mp4
- 13.19 MB
1. Solutions to Convolutional Neural Network Problems – Part One_/04.Resolving fit_generator Errors.mp4
- 7.63 MB
1. Solutions to Convolutional Neural Network Problems – Part One_/05.Model Object Has No Attribute load_model Keras.mp4
- 3.19 MB
1. Solutions to Convolutional Neural Network Problems – Part One_/06.High val_acc, But Low Accuracy in Practice.mp4
- 11.03 MB
1. Solutions to Convolutional Neural Network Problems – Part One_/07.Error in Adding a Dense Layer.mp4
- 4.83 MB
1. Solutions to Convolutional Neural Network Problems – Part One_/08.Model with Multiple Outputs Errors.mp4
- 3.86 MB
1. Solutions to Convolutional Neural Network Problems – Part One_/09.Model That Uses Dropout Is Still Overfitting.mp4
- 8.09 MB
2. Solutions to Convolutional Neural Network Problems – Part Two_/10.When the Value Error Input 0 Is Incompatible with Layer conv2d_1.mp4
- 6.42 MB
2. Solutions to Convolutional Neural Network Problems – Part Two_/11.Interpreting kernel_size Notation in CNNs.mp4
- 9.92 MB
2. Solutions to Convolutional Neural Network Problems – Part Two_/12.Choosing Last Layer’s Activation Function in CNN.mp4
- 7.53 MB
2. Solutions to Convolutional Neural Network Problems – Part Two_/13.Using Validation Accuracy.mp4
- 8.82 MB
2. Solutions to Convolutional Neural Network Problems – Part Two_/14.Error When Using CNN to Classify Text.mp4
- 8.3 MB
2. Solutions to Convolutional Neural Network Problems – Part Two_/15.Kernel Weight Initialization in CNN Model.mp4
- 5.36 MB
2. Solutions to Convolutional Neural Network Problems – Part Two_/16.Common Problems When Using Pre-Trained CNN Models.mp4
- 7.6 MB
2. Solutions to Convolutional Neural Network Problems – Part Two_/17.Shape Error When Training CIFAR-10 Dataset on CNN.mp4
- 8.68 MB
3. Solutions to Recurrent Neural Network Problems_/18.Building an RNN Model in Keras.mp4
- 8.12 MB
3. Solutions to Recurrent Neural Network Problems_/19.Wrong Input - ValueError – Error When Checking Input.mp4
- 13.17 MB
3. Solutions to Recurrent Neural Network Problems_/20.Correct Text Preparation for Machine Translation.mp4
- 10.45 MB
3. Solutions to Recurrent Neural Network Problems_/21.Handling Invalid Input Shape Error.mp4
- 7.89 MB
3. Solutions to Recurrent Neural Network Problems_/22.Mapping Series of Vectors to a Single Vector.mp4
- 7.01 MB
3. Solutions to Recurrent Neural Network Problems_/23.Resolving a Bad Output from RNN While Generating a Simple Sequence.mp4
- 6.26 MB
3. Solutions to Recurrent Neural Network Problems_/24.Preparing Data Correctly for Time Series Prediction.mp4
- 9.66 MB
3. Solutions to Recurrent Neural Network Problems_/25.How to Enable Stateful RNN.mp4
- 6.99 MB
4.Solutions to LSTM Recurrent Neural Networks Problems/26.Stacking Multiple LSTM in Keras TypeError - Call() Got an Unexpected Keyword Argument 'return_sequences'.mp4
- 9.61 MB
4.Solutions to LSTM Recurrent Neural Networks Problems/27.Working with Different Lengths of Input and Output Sequences.mp4
- 15.61 MB
4.Solutions to LSTM Recurrent Neural Networks Problems/28.How to Use Stacked LSTMs.mp4
- 6.13 MB
4.Solutions to LSTM Recurrent Neural Networks Problems/29.Using CNN-LSTM for Time Series Prediction.mp4
- 8.48 MB
4.Solutions to LSTM Recurrent Neural Networks Problems/30.Solving LSTM Underfitting on Time Series Problem.mp4
- 6.03 MB
4.Solutions to LSTM Recurrent Neural Networks Problems/31.Using LSTM for Multi-Value Prediction.mp4
- 5.29 MB
4.Solutions to LSTM Recurrent Neural Networks Problems/32.How To Do Text Classification with LSTM.mp4
- 11.39 MB
4.Solutions to LSTM Recurrent Neural Networks Problems/33.Data Preparation for Seq2Seq Learning.mp4
- 7.7 MB
5. Troubleshooting Models with scikit-learn_/34.LabelBinarizer Returns Vector When There Are Two Classes.mp4
- 7.95 MB
5. Troubleshooting Models with scikit-learn_/35.Handling Missing Values.mp4
- 12.64 MB
5. Troubleshooting Models with scikit-learn_/36.Evaluating Deep Learning Models Using Additional Metrics.mp4
- 8.7 MB
5. Troubleshooting Models with scikit-learn_/37.Fixing Warning Messages.mp4
- 10.3 MB
5. Troubleshooting Models with scikit-learn_/38.Generating Test Datasets.mp4
- 6.9 MB
5. Troubleshooting Models with scikit-learn_/39.Normalizing and Standardizing the Data.mp4
- 6.84 MB
5. Troubleshooting Models with scikit-learn_/40.Preparing Text for Use with Deep Learning Models.mp4
- 8.18 MB
6. Solving NumPy Problems_/41.Converting a 2D Matrix to a One-Hot Encoded Matrix.mp4
- 8.55 MB
6. Solving NumPy Problems_/42.Reshaping a 2D NumPy Array to 3D Array.mp4
- 4.84 MB
6. Solving NumPy Problems_/43.Fix load.npy Error in Python3.mp4
- 16.54 MB
6. Solving NumPy Problems_/44.Turn ND Matrix Into 1D Vector.mp4
- 30.07 MB
Exercise Files/code_37489.zip
- 1.98 KB
Torrent Description
Description:
By: Jakub Konczyk
Released: 29 Apr 2019 (New Release!)
Torrent Contains: 51 Files, 8 Folders
Course Source: https://www.packtpub.com/big-data-and-business-intelligence/troubleshooting-python-deep-learning-video
Practical solutions to your problems while building Deep Learning models using CNN, LSTM, Scikit-Learn, and NumPy
Video Details
ISBN 9781788998192
Course Length 3 hours 2 minutes
Table of Contents
• Solutions to Convolutional Neural Network Problems – Part One
• Solutions to Convolutional Neural Network Problems – Part Two
• Solutions to Recurrent Neural Network Problems
• Solutions to LSTM Recurrent Neural Networks Problems
• Troubleshooting Models with scikit-learn
• Solving NumPy Problems
Learn
• Go through curated issues that many developers face when building their deep learning models
• Discover the most efficient techniques to overcome classification problems in CNN
• Resolve issues that are related to the CNN architecture, accuracy, input, and output
• Work with LSTM, which is a part of RNN, and deal with the most efficient part of text problems
• Discover how to solve the most popular problems from architecture to input and output
• Implement the most usable libraries: Scikit Learn and Numpy, to resolve the major problems arising from your Deep Learning models
About
Building Deep Learning models with Python is a strenuous task and there are chances of getting stuck on specific tasks. When that happens, you usually end up searching for solutions and need to manually look for ways to come out of these problems. This wastes both time and effort and may also lead to reduced performance of your Deep Learning system.
After carefully analyzing the most popular errors or problems that arise while working on Deep Learning models, we have identified the most usable models used for classification in this course and provided practical yet unique solutions to each problem that are easy to understand and implement.
You can either follow the entire course or directly jump into the section that covers a specific problem you’re facing. Some of the common yet important issues we cover include errors while building and training Deep Learning with neural networks, especially without a specific framework.
By the end of the course, you will be well-versed to tackle and troubleshoot any errors with your Deep learning models.
The code bundle for this video course is available at - https://github.com/PacktPublishing/Troubleshooting-Node.js
Style and Approach
This video tutorial provides practical insights on how to solve issues in your Deep Learning models. You’ll identify and address specific problems faced while working with Deep Learning and tackle them straight away with Python.
Features:
• Discover the limitless use of building any application using Deep Learning and ensure its issues aren’t a roadblock for your projects
• Problems are addressed with practical yet unique solutions that are easy to understand and implement
• Identify and address specific problems that developers face while working with Deep Learning and show them to tackle it straight away with Python
Authors
Jakub Konczyk
Jakub Konczyk has enjoyed and done programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share it with others. He first discovered Machine Learning when he was trying to predict the real estate prices in one of the early stage start-ups he was involved in. He failed miserably. Then he discovered a much more practical way to learn Machine Learning that he would like to share with you in this course. It boils down to “Keep it simple!” mantra. Learn more at https://kubakonczyk.com
By: Jakub Konczyk
Released: 29 Apr 2019 (New Release!)
Torrent Contains: 51 Files, 8 Folders
Course Source: https://www.packtpub.com/big-data-and-business-intelligence/troubleshooting-python-deep-learning-video
Practical solutions to your problems while building Deep Learning models using CNN, LSTM, Scikit-Learn, and NumPy
Video Details
ISBN 9781788998192
Course Length 3 hours 2 minutes
Table of Contents
• Solutions to Convolutional Neural Network Problems – Part One
• Solutions to Convolutional Neural Network Problems – Part Two
• Solutions to Recurrent Neural Network Problems
• Solutions to LSTM Recurrent Neural Networks Problems
• Troubleshooting Models with scikit-learn
• Solving NumPy Problems
Learn
• Go through curated issues that many developers face when building their deep learning models
• Discover the most efficient techniques to overcome classification problems in CNN
• Resolve issues that are related to the CNN architecture, accuracy, input, and output
• Work with LSTM, which is a part of RNN, and deal with the most efficient part of text problems
• Discover how to solve the most popular problems from architecture to input and output
• Implement the most usable libraries: Scikit Learn and Numpy, to resolve the major problems arising from your Deep Learning models
About
Building Deep Learning models with Python is a strenuous task and there are chances of getting stuck on specific tasks. When that happens, you usually end up searching for solutions and need to manually look for ways to come out of these problems. This wastes both time and effort and may also lead to reduced performance of your Deep Learning system.
After carefully analyzing the most popular errors or problems that arise while working on Deep Learning models, we have identified the most usable models used for classification in this course and provided practical yet unique solutions to each problem that are easy to understand and implement.
You can either follow the entire course or directly jump into the section that covers a specific problem you’re facing. Some of the common yet important issues we cover include errors while building and training Deep Learning with neural networks, especially without a specific framework.
By the end of the course, you will be well-versed to tackle and troubleshoot any errors with your Deep learning models.
The code bundle for this video course is available at - https://github.com/PacktPublishing/Troubleshooting-Node.js
Style and Approach
This video tutorial provides practical insights on how to solve issues in your Deep Learning models. You’ll identify and address specific problems faced while working with Deep Learning and tackle them straight away with Python.
Features:
• Discover the limitless use of building any application using Deep Learning and ensure its issues aren’t a roadblock for your projects
• Problems are addressed with practical yet unique solutions that are easy to understand and implement
• Identify and address specific problems that developers face while working with Deep Learning and show them to tackle it straight away with Python
Authors
Jakub Konczyk
Jakub Konczyk has enjoyed and done programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share it with others. He first discovered Machine Learning when he was trying to predict the real estate prices in one of the early stage start-ups he was involved in. He failed miserably. Then he discovered a much more practical way to learn Machine Learning that he would like to share with you in this course. It boils down to “Keep it simple!” mantra. Learn more at https://kubakonczyk.com