GAN Specialization; Nvidia GTC Fall 2020; Vision Transformer; OpenCV 4.5.0
Vision Geek AI Newsletter #6
|Arun Ponnusamy||Nov 29, 2020||1|
deeplearning.ai has launched a new specialization on Coursera containing 3 courses on Generative Adversarial Networks (GAN). Since it’s inception in 2014 by Ian Goodfellow, GANs have created a whole new subfield in AI, giving machines the ability to imagine and learn to create new content (text, image, video, music etc). This is already proving to be a game changer (good or bad ?) with things like OpenAI GPT-3 and DeepFakes. Apart from this book (uses TensorFlow/Keras), this set of courses are going to be really helpful for people interested in getting a better understanding of GANs and get hands-on with PyTorch.
Nvidia GTC Fall 2020
Nvidia has announced a bunch of new products and partnerships in the GPU Technology Conference (GTC) happened virtually in October. Ranging from a new 2GB Jetson Nano ($59) to data center specific products like DPU, new architectures like Bluefield in partnership with ARM and VMWare. Nvidia is definitely spearheading the AI hardware movement in full throttle. They have completely repositioned themselves from just a gaming company to premier AI computing company.
Transformers for Image Recognition
CNNs have been the go-to algorithm/method for computer vision tasks in almost the last decade since AlexNet won the ImageNet competition in 2012. In recent times, Transformer networks have been slowly gaining momentum since the success of models like GPT-3. Though transformer networks have been mainly used in NLP, it is slowly getting into computer vision as well.
Facebook AI Research team successfully applied transformers to object detection recently, OpenAI team also released Image-GPT. And now a team from Google has used transformers for image classification. The paper is currently under review. Can transformers replace CNN ? well, we have to wait and see. But CNN is definitely starting to face competition. (paper | github)
Deep Learning with PyTorch Book
Just in case you missed it, few months back PyTorch team has made the full version of the book “Deep Learning with PyTorch” freely available for everyone. Apart from the official PyTorch tutorials, this book will be a great place to start for anyone new to PyTorch. You can download the book here.
OpenCV 4.5.0 has been released. Some of the improvements include
Better SIFT in the main repository
Real-time Single Object Tracking using Deep Learning
(opencv_contrib): OpenCV bindings for Julia Programming Language
(dnn module) 3-5x faster inference on ARM, Improved ONNX support, fixes and optimizations in DNN CUDA backend.
and the license has been switched to Apache 2 from BSD 3-clause license. It doesn’t affect how we use OpenCV. It’s still free to use in all forms (including commercial use), Apache 2 license provides more legal protections. Read more here for the rationale behind this decision.
AI Pay Grades
Though there is a high demand for good AI talents in the industry, it may be unclear what the actual job offers look like around the world. Often times, ML Engineers like us are not really sure whether they are underpaid and (ironically) don’t have enough data to get an overall idea and better negotiate the compensation. aipaygrad.es is a good initiative from the community to collect and show statistics from real job offers made.
Keep in mind, currently there is only a small amount of data available and mostly from US. So it’s not yet fully representative of the industry. Nevertheless keep an eye on this site for more data over a period and if possible spread the word and contribute.
Landing an ML job
Even though many studies say that there is a shortage of ML Engineers, getting into an actual ML role might not be that easy. In this session hosted by deeplearning.ai , technical recruiters from top companies like Pinterest and Scale AI explain practical approaches on how to land an ML job and build a career in the AI industry. Informative session for ML Engineers.
AI fun :)
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