Packt | Troubleshooting Python Deep Learning [FCO]
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Files
| 01.The Course Overview.mp4 | 37.6 MB | ||
| 02.Concatenate Two CNNs Correctly.mp4 | 74 MB | ||
| 03.Splitting Trained Model.mp4 | 13.2 MB | ||
| 04.Resolving fit_generator Errors.mp4 | 7.6 MB | ||
| 05.Model Object Has No Attribute load_model Keras.mp4 | 3.2 MB | ||
| 06.High val_acc, But Low Accuracy in Practice.mp4 | 11 MB | ||
| 07.Error in Adding a Dense Layer.mp4 | 4.8 MB | ||
| 08.Model with Multiple Outputs Errors.mp4 | 3.9 MB | ||
| 09.Model That Uses Dropout Is Still Overfitting.mp4 | 8.1 MB | ||
| 1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url | 307.2 B | ||
| 10.When the Value Error Input 0 Is Incompatible with Layer conv2d_1.mp4 | 6.4 MB | ||
| 11.Interpreting kernel_size Notation in CNNs.mp4 | 9.9 MB | ||
| 12.Choosing Last Layer’s Activation Function in CNN.mp4 | 7.5 MB | ||
| 13.Using Validation Accuracy.mp4 | 8.8 MB | ||
| 14.Error When Using CNN to Classify Text.mp4 | 8.3 MB | ||
| 15.Kernel Weight Initialization in CNN Model.mp4 | 5.4 MB | ||
| 16.Common Problems When Using Pre-Trained CNN Models.mp4 | 7.6 MB | ||
| 17.Shape Error When Training CIFAR-10 Dataset on CNN.mp4 | 8.7 MB | ||
| 18.Building an RNN Model in Keras.mp4 | 8.1 MB | ||
| 19.Wrong Input - ValueError – Error When Checking Input.mp4 | 13.2 MB | ||
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| 20.Correct Text Preparation for Machine Translation.mp4 | 10.4 MB | ||
| 21.Handling Invalid Input Shape Error.mp4 | 7.9 MB | ||
| 22.Mapping Series of Vectors to a Single Vector.mp4 | 7 MB | ||
| 23.Resolving a Bad Output from RNN While Generating a Simple Sequence.mp4 | 6.3 MB | ||
| 24.Preparing Data Correctly for Time Series Prediction.mp4 | 9.7 MB | ||
| 25.How to Enable Stateful RNN.mp4 | 7 MB | ||
| 26.Stacking Multiple LSTM in Keras TypeError - Call() Got an Unexpected Keyword Argument 'return_sequences'.mp4 | 9.6 MB | ||
| 27.Working with Different Lengths of Input and Output Sequences.mp4 | 15.6 MB | ||
| 28.How to Use Stacked LSTMs.mp4 | 6.1 MB | ||
| 29.Using CNN-LSTM for Time Series Prediction.mp4 | 8.5 MB | ||
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| 30.Solving LSTM Underfitting on Time Series Problem.mp4 | 6 MB | ||
| 31.Using LSTM for Multi-Value Prediction.mp4 | 5.3 MB | ||
| 32.How To Do Text Classification with LSTM.mp4 | 11.4 MB | ||
| 33.Data Preparation for Seq2Seq Learning.mp4 | 7.7 MB | ||
| 34.LabelBinarizer Returns Vector When There Are Two Classes.mp4 | 8 MB | ||
| 35.Handling Missing Values.mp4 | 12.6 MB | ||
| 36.Evaluating Deep Learning Models Using Additional Metrics.mp4 | 8.7 MB | ||
| 37.Fixing Warning Messages.mp4 | 10.3 MB | ||
| 38.Generating Test Datasets.mp4 | 6.9 MB | ||
| 39.Normalizing and Standardizing the Data.mp4 | 6.8 MB | ||
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| 40.Preparing Text for Use with Deep Learning Models.mp4 | 8.2 MB | ||
| 41.Converting a 2D Matrix to a One-Hot Encoded Matrix.mp4 | 8.5 MB | ||
| 42.Reshaping a 2D NumPy Array to 3D Array.mp4 | 4.8 MB | ||
| 43.Fix load.npy Error in Python3.mp4 | 16.5 MB | ||
| 44.Turn ND Matrix Into 1D Vector.mp4 | 30.1 MB | ||
| 5. (Discuss.FTUForum.com) FTU Discussion Forum.url | 307.2 B | ||
| How you can help Team-FTU.txt | 204.8 B | ||
| code_37489.zip | 2 KB | ||
| ▲ 51 total files | |||
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
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