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Machine Learning is clearly a field that has seen crazy advancements in the past couple of years. This trend and advancements have created a lot of Job opportunities in the industry. The need for Machine Learning Engineers are high in demand and this surge is due to evolving technology and generation of huge amounts of data aka Big Data. So, in this article, I’ll be discussing the most amazing Machine Learning Projects one should definitely know and work with
Detectron is Facebook AI Research’s software system that implements state-of-the-art object detection algorithms. It is written in Python and powered by the Caffe2 deep learning framework.
The goal of Detectron is to provide a high-quality, high-performance codebase for object detection research. It is designed to be flexible in order to support rapid implementation and evaluation of novel research. It contains more than 50 Pre-trained models.
Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. DensePose-RCNN is implemented in the Detectron framework.
It is a library for developing and training ML models and deploying in the browser. It’s become a very popular release since it’s release earlier this year and continues to amaze with its flexibility. With this you can:
Develop ML in the Browser: Use flexible and intuitive APIs to build models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.
Run Existing models: Use TensorFlow.js model converters to run pre-existing TensorFlow models right in the browser.
Retrain Existing models: Retrain pre-existing ML models using sensor data connected to the browser, or other client-side data.
Machine Learning is also doing major advancements in audio processing and it’s not just generating music or classification. WaveGlow is a Flow-based Generative Network for Speech Synthesis by NVIDIA. The researchers have also listed down the steps you can follow if you want to train your own model from scratch.
Imagine you have a half image of a scene and you wanted the full scenery, well that’s what image outpainting can do that for you. This project is a Keras implementation of Stanford’s Image Outpainting paper. The model was trained with 3500 scrapped beach data with argumentation totaling up to 10500 images for 25 epochs.
This is an amazing paper with a detailed step by step explanation. A must try example for all the Machine Learning Enthusiasts. Personally, this is my favorite Machine Learning project.
Well, talking about images, this one is a masterpiece. What this algorithm does is, takes an image as input and then if you add an external element to the image, it blends that element into the surroundings as if it is a part of it.
Can you tell the difference? No, right? Well, this shows us how far we have come in terms of Machine Learning.
Now, have a close look at the images here, you see a stick figure doing spin-kick, backflip, and cartwheel. That my friend is reinforcement learning in action. DeepMimic is an example-Guided Deep Reinforcement Learning of Physics-Based Character Skills.
Magenta is a research project exploring the role of machine learning in the process of creating art and music. Primarily this involves developing new deep learning and reinforcement learning algorithms for generating songs, images, drawings, and other materials.
It is also an exploration in building smart tools and interfaces that allow artists and musicians to extend (not replace!) their processes using these models. Go spread your wings, create your unique content for Instagram or Soundcloud and become an influencer.
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