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Apply your Deep Learning abilities and create your personal end-to-end Image Search engine!
- What are Image-to-Image Search engines
- How to construct your AI primarily based Image-to-Image Search engine
- How to create easy internet primarily based interface in your Deep studying fashions utilizing the Python framework Flask
- Coding a Convolutional Neural Network (CNN) from scratch in Tensorflow 1.10.0
- Using the Python framework Flask to serve a Deep Learning mannequin in manufacturing
- How to create an End-to-End pipeline for any Deep Learning mannequin utilizing Tensorflow
- How to create a Flask utility from scratch
Artificial intelligence is without doubt one of the quickest rising fields of laptop science at present and the demand for wonderful AI Engineers is rising day in and time out. This course will provide help to keep aggressive within the AI job market by educating you the way to create a Deep Learning End-to-End product by yourself.
Most programs deal with the fundamentals of Deep Learning and train you in regards to the very fundamentals of various fashions. In this course, nevertheless, you’ll discover ways to write an entire End-to-End pipeline, from information preprocessing throughout selecting the best hyper-parameters, to displaying your customers leads to a browser.
The case that we’ll sort out on this course is an engine for Image to Image Search.
Why do you have to take this course?
This course isn’t targeted on educating you Neural Networks (ANNs, CNNs, RNNs…), however educating you the way to apply them in actual world circumstances.
If you haven’t labored on a product that makes use of Deep Learning earlier than, that is the right course for you! Throughout the course we’ll work collectively on the Image to Image Search engine, ranging from floor zero – picture preprocessing, making a mannequin, coaching it, then testing. After that we’ll create a easy internet utility and use it to serve our mannequin in manufacturing.
Another cool factor about this course is that we’ll use a number of programming languages to create the entire utility across the mannequin itself. This will make you not solely a greater AI Engineer but in addition get you on the trail in direction of turning into a Full stack AI Engineer.
After taking this course you’ll assure your self to be one step nearer to touchdown your dream job as an AI/ML Engineer by having your personal AI product/venture in your portfolio.
Libraries/Tools used within the course:
The complete Deep studying back-end of our pipeline might be constructed utilizing Tensorflow 1.10.0. For some picture preprocessing process we’ll use some fundamental performance from OpenCV, an important Python library for picture processing duties!
For the app’s back-end (mannequin dealing with, picture importing, web page navigation, and many others.) we’ll use the Flask python framework.
Who is that this course for?
As you may see the course is supposed to show you the way to create your personal Deep Learning product from scratch.
If you might be simply beginning out with Deep Learning, this course is perhaps too onerous for you. But when you like challenges, I do suggest following it. Although I can’t be explaining the meat of Neural Networks (ANNs, CNNs), I’ll clarify most ideas in nice element, so even in case you are a complete newbie you must have the ability to observe with the assistance of your friends or my assist via the feedback part.
If you will have Deep Learning expertise and need to transfer it to the subsequent degree one can find this course very helpful! You can take into account it as a degree up in your abilities by placing your already nice abilities to new use. At the tip of the course you’ll not solely have realized the way to create a working End-to-End pipeline, but in addition maintain proof of your abilities for potential employers!
The conclusion is that this – that is very uncommon alternative, not solely to be taught Deep Learning ideas, but in addition the way to apply that data and create your personal internet utility (as an entire product) from scratch.