Course Information
Course Overview
Develop your own deep learning framework from zero to one. Hands-on Machine Learning with Python.
Deep learning would be part of every developer's toolbox in near future. It wouldn't just be tool for experts.
In this course, we will develop our own deep learning framework in Python from zero to one whereas the mathematical backgrounds of neural networks and deep learning are mentioned concretely. Hands on programming approach would make concepts more understandable. So, you would not need to consume any high level deep learning framework anymore. Even though, python is used in the course, you can easily adapt the theory into any other programming language.
Course Content
- 7 section(s)
- 17 lecture(s)
- Section 1 Introduction
- Section 2 Single Layer Perceptrons
- Section 3 Constructing Neural Networks Model
- Section 4 Feedforward Neural Networks
- Section 5 Neural Networks Learning: Backpropagation
- Section 6 Tuning Neural Networks
- Section 7 Bonus
What You’ll Learn
- They can develop their own neural networks / deep learning framework
- Without any need to high level deep learning frameworks
- Tuning neural networks models
- Understand how neural networks work
- Learn how to apply neural networks in real world examples
- Even though, python is used in the course, you can easily adapt the logic into other programming languages
Skills covered in this course
Reviews
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FFikile Dube
I find it very difficult to understand the instructor. I also expected him to explain the code while he types it, which was not done. For a beginner, this made understanding the code extremely difficult.
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IIvan Radovic
Good for developing neural nets from purely programming principles and not much other prior knowledge. Would be nice to have a bit more resources to explain some of the theory and concepts a bit more thoroughly.
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MMesut kubatoglu
Strong math foundations make me happy. Similar courses skip these steps.
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AAbdullah GÜNDÜZ
Deep learning is not an out-of-the-box thing anymore. I've learnt how it works.