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Troubleshooting Python Deep Learning [Video]

More Information
  • 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

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.

  • 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
Course Length 3 hours 2 minutes
ISBN 9781788998192
Date Of Publication 29 Apr 2019


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. Kuba is an author of multiple bestselling video courses on Machine Learning and Deep Learning including Real-World Deep Learning Python Projects and AI in Finance.