Troubleshooting Python deep learning / Jakub Konczyk.

Format:

Video

Author/Creator:

Konczyk, Jakub, author, speaker.

Contributor:

Packt Publishing, production company.

Series:

Academic Video Online

Language:

English

Subjects (All):

Debugging in computer science.

Python (Computer program language).

Machine learning.

Genre:

Instructional films.

Physical Description:

1 online resource (183 minutes)

Place of Publication:

Birmingham, England : PACKT Publishing, 2019.

Language Note:

In English.

System Details:

video file

Summary:

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 resolve 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.

Participant:

Presenter: Jakub Konczyk.

Notes:

Title from resource description page (viewed April 02, 2020).

OCLC:

1126148420