Transfer Learning is the process of taking a network pre-trained on a dataset and utilizing it to recognize the image, object detection, image segmentation, semantic segmentation, and many more. We can utilize the robust, discriminative filters learned by the state-of-the-art network on challenging datasets such as Imagenet/COCO and then apply these networks for some other tasks.
In today’s article, we are going to talk about five of the open-source Transfer Learning projects to enhance your skills in the field of data science.
_Note: _This article is to just give a glimpse of some of the not-so-famous but really good open-source projects which you can use in your projects. To read more about each of them I recommend following the link given along the project.
_Having a good theoretical knowledge is amazing but implementing them in code in a real-time machine learning project is a completely different thing. You might get different and unexpected results based on different problems and datasets. So as a Bonus,I am also adding the links to the various courses which has helped me a lot in my journey to learn Data science and ML.I am personally a fan of _DataCamp_, I started from it and I am still learning through _DataCamp_ and keep doing new courses. They seriously have some exciting courses. Do check them out._
- Data-scientist-with-python
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- Machine-learning-scientist-with-r
- Machine-learning-scientist-with-python
- Machine-learning-for-everyone
- Data-science-for-everyone
- Data-engineer-with-python
- Data-analyst-with-python
_P.S: I am still using _DataCamp_ and keep doing courses in my free time. I actually insist the readers to try out any of the above courses as per their interest, to get started and build a good foundation in Machine learning and Data Science. The best thing about these courses by _DataCamp_ is that they explain it in a very elegant and different manner with a balanced focus on practical and well as conceptual knowledge and at the end, there is always a Case study. This is what I love the most about them. These courses are truly worth your time and money. These courses would surely help you also understand and implement transfer learning, machine learning in a better way and also implement it in Python or R. I am damn sure you will love it and I am claiming this from my personal opinion and experience._
_Also, I have noticed that _DataCamp_ is giving unlimited access to all the courses for free for one week starting from 1st of September through 8th September 2020, 12 PM EST. So this would literally be the best time to grab some yearly subscriptions(which I have) which basically has unlimited access to all the courses and other things on _DataCamp_ and make fruitful use of your time sitting at home during this Pandemic. So go for it folks and Happy learning_
Coming back to the topic -
1.Densedepth
Densedepth is a high-quality Monocular Depth Estimation project built on top of Transfer Learning and NYU Depth V2 and KITTI dataset, implemented by Ibraheem Alhashimand Peter Wonka.
Densedepth aims to apply state of the art approach to 3D images and produce high-quality dense maps from the images with the best-generalized performance using Transfer Learning and standard Neural network architecture.
Transfer learning has proved to be efficient between different tasks many of which are related to 3D reconstruction and using transfer learning, allows for a more modular architecture as we get to use the power of the pre-trained networks.
#python #data-science #machine-learning #open-source #transfer-learning
