English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 02m | 371 MB
Practical solutions to your problems while building Deep Learning models using CNN, LSTM, Scikit-Learn, and NumPy
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.
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.
What You Will 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
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