Introduction
GitHub repositories and Reddit discussions: both platforms have played a key role in my machine learning trip. They have helped me develop my knowledge and understanding of machine learning techniques and my business acumen.
Both GitHub and Reddit also keep me up to date on the latest developments in machine learning, A must have for anyone working in this field!!
And if you are a programmer, good, GitHub is like a temple for you. You can easily download the code and replicate it on your machine. This makes it even easier to learn new ideas and build a diverse skill set..
I'm delighted to pick the top GitHub repositories and this month's Reddit discussions. The Reddit threads I have featured are about both the technical side of machine learning as well as the one related to the race. This ability to combine the two is what separates machine learning experts from hobbyists..
Below are the monthly articles we've covered so far in this series:
So, Let's get down to work for March!
GitHub repositories
If I had to choose one of the reasons for my fascination with computer vision, would be GANs (Generative Adversarial Networks). They were invented by Ian Goodfellow just a few years ago and have grown into a whole body of research.. Recent AI art you've seen in the news? Everything works with GAN.
DeepMind came up with the BigGAN concept last year, but we have waited a while for a PyTorch implementation. This repository also includes previously trained models (128 × 128, 256 × 256 Y 512 × 512). You can install this in just one line of code:
pip install pytorch-pretrained-biggan
And if you are interested in reading the full BigGAN research article, visit here.
The ability to work with image data is becoming a defining trait for anyone interested in deep learning. The advent and rapid flourishing of computer vision algorithms has played an important role in this transformation.. You won't be surprised to learn that NVIDIA is one of the top leaders in this area..
Just take a look at their developments 2018:
And now, the folks at NVIDIA have created another amazing release: the ability to synthesize photorealistic images with an input semantic design. How good is it? The following comparison provides a good illustration:
SPADE has outperformed existing methods in the popular COCO dataset. The repository that we have linked above will house the PyTorch implementation and the models previously trained for this technique (be sure to bookmark it).
This video shows how well SPADE works in 40.000 images taken from Flickr:
This repository is based on the ‘Fast online object tracking and segmentation: a unifying approach‘ paper. Here is a sample result using this technique:
Impressive! The technique, called SiamMask, it is quite simple, versatile and extremely fast. Oh, Did I mention that object tracking is done in real time? That certainly got my attention. This repository also contains pre-trained models so you can get started.
The work will be presented at the prestigious CVPR conference 2019 (Computer Vision and Pattern Recognition) in June. The authors have demonstrated their approach in the following video:
Have you ever worked on a pose detection project? I've done it and let me tell you it's excellent. It is a testament to the progress we have made as a community in deep learning.. Who would have thought ago 10 years that we would be able to predict a person's next body movement?
This GitHub repository is a PyTorch implementation of ‘Self-supervised learning of the 3D human pose using multi-view geometry‘ paper. The authors have pioneered a new technique called EpipolarPose, a self-supervised learning method to estimate the pose of a human being in 3D.
EpipolarPose technique estimates 2D poses from multi-view images during training phase. Then use epipolar geometry to generate a 3D pose. This, at the same time, used to train the 3D pose estimator. This process is illustrated in the image above.
This article has also been accepted at the CVPR conference 2019. Shaping up to be an excellent lineup!!
This is a unique repository in many ways. It is an open source deep learning model to protect your privacy. The whole concept of DeepCamera is based on automated machine learning (AutoML). Therefore, you don't even need programming experience to train a new model.
DeepCamera works on Android devices. You can also integrate the code with surveillance cameras. There is A LOT you can do with DeepCamera code, what includes:
- Facial recognition
- Face detection
- Control from mobile application
- Object detection
- Motion detection
And many other things. Building your own AI-powered model has never been easier!!
Reddit discussions
I've divided this month's Reddit discussions into two categories:
- The technical side of machine learning
- Machine Learning Career-Related Discussions (roles and jobs)
Let's start with the technical aspect.
Data scientists are fascinated by research work. We want to read them, code them and maybe even write one from scratch. How cool would it be to present your own research paper at a top-notch ML conference??
I certainly belong to the category of “I want to write a research article”. This discussion, started by a veteran researcher, delves into the best practices we should follow when writing a research article. Here is a lot of information and experience, A must read for all of us!
Here is the GitHub repository with the best tips, tips and ideas in one place. Treat these tips as a set of guidelines and not as rules set in stone.
How do you put your trained machine learning models into production? How do you implement them? These are VERY common questions you will face in your data science interview (and work, of course). If you are not sure what this is, I suggest you read it NOW.
This discussion thread is about an open source library that converts your machine learning models to native code (C, Python, Java) no dependencies. Must scroll through the thread, as there are some common questions that the author has addressed in detail.
You can find the complete code in this GitHub repository. Below is the list of models that this library currently supports:
Let's shift focus now and see some discussions about the machine learning career. These are applicable to ALL machine learning professionals, both aspiring and established.
Will the emergence of automated machine learning be a disadvantage for the field itself?? That's a question most of us have been wondering about.. Most of the articles I come across predict all pessimism. Some even claim that data scientists will not be needed in 5 years!
Source: Themocracy
The author of this thread makes a wonderful argument against the general consensus. Data science is highly unlikely to disappear due to automation.
The discussion rightly argues that data science is not just about data modeling. That's just the 10% of the whole process. An important part of the data science life cycle is the human intuition behind the models. Data cleansing, data visualization and a touch of logic are what drives this whole process.
Here's a gem and a solid argument that caught my eye:
We develop all kinds of statistics software in the last century and, but nevertheless, has not replaced statisticians.
Do you want to land your first position in data science? Do you find it an overwhelming process? I've been there. It's one of the biggest hurdles to overcome on our respective data science journeys..
So I wanted to highlight this particular thread. It's a really eye-opening discussion, where data science professionals and beginners discuss how to enter this field. The author of the post offers some in-depth thoughts on the data science job search process along with tips for clearing each round of interviews..
A phrase that really stood out from this discussion:
Remember, the increase in interview requests and the increase in knowledge is not just a correlation, it is a causality. While applying, learn something new every day.
A DataPeaker, our goal is to help you land your first position in data science. Check out the amazing resources below to help you get started:
Domain knowledge: that key ingredient in the overall data scientist recipe. Often, aspiring data scientists overlook or misinterpret it. And that often results in rejections in interviews.. Then, How can you develop your business acumen to complement your existing skills in technical data science?
This Reddit discussion offers some helpful insights. The ability to translate your ideas and your results into commercial terms is VITAL. Most of the stakeholders you will face in your career will not understand technical jargon..
Here's my favorite pick from the discussion:
You need to know your business partners better. Find out what they do on a day-to-day basis, what are your processes, how they generate the data you are going to use. If you understand how X and Y see, you will be better able to help them when they come to you with problems.
At DataPeaker we strongly believe in building a structured thinking mindset. We have gathered our experience and knowledge on this topic in the comprehensive course below:
This course contains several case studies that will also help you get an idea of how companies work and think..
Final notes
I especially enjoyed last month's Reddit discussions. I urge you to learn more about how the production environment works in a machine learning project. Now considered almost mandatory for a data scientist, so you can't get away from him.
You should also participate in these Reddit discussions. Passive scrolling is good for acquiring knowledge, but adding your own perspective will help other applicants too. This is an intangible feeling, but you will appreciate and appreciate the more experience you get.
Which discussion did you find the most revealing? And which GitHub repository stood out for you? Let me know in the comment section below!!
