Created by Andrei Neagoie, Daniel Bourke Students also bought Machine Learning A-Z: Hands-On Python & R In Data Science Data Science A-Z: Real-Life Data Science Exercises Included Machine Learning, Data Science and Deep Learning with Python Statistics for Data Science and Business Analysis Data Science 2020: Complete Data Science & Machine Learning Preview this Udemy Course GET COUPON CODE Description This is a brand new Machine Learning and Data Science course just launched January 2020 and updated this month with the latest trends and skills! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 270,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries).

"It is not a dream, it is a simple feat of scientific engineering, only expensive -- blind, faint-hearted, doubting world!" Discovering a new medicine is a billion-dollar research endeavour. At least, it can draw in the money as the results are kind of self-explanatory; life-saving. But, in case of AI, which is usually riddled by speculations and scepticism, it is an uphill task for the researchers to sell their idea or to churn profits to keep fueling their AI labs. For example, OpenAI, which started as a non-profit research lab, changed its stance when it partnered with Microsoft. A year later, they have announced that they are making all their exotic deep learning innovations available to the public through an API that comes with a price tag.

A research team from the Chinese Academy of Sciences and the City University of Hong Kong have unveiled DeepFaceDrawing, an AI-powered framework that turns sketches into photorealistic portraits. This deep learning system uses modules to generate the images, or in other words, it identifies the most notable facial features individually, like the eyes, nose, mouth, face shape, etc., before these vectors are merged to create realistic images. There are other deep image-to-image translation techniques that may generate face images from freehand sketches faster, but they require professional sketches or even edge maps as input. DeepFaceDrawing can implicitly model the shape space of recognizable face images and then proceeds to synthesize a face image in this space to approximate an input sketch. Our method essentially uses input sketches as soft constraints and is thus able to produce high-quality face images even from rough and/or incomplete sketches," said researcher Shu-Yu Chen.

This article is for paid subscribers only. We routinely send the best resources and easter eggs to our subscribers. Quantum computing is another buzzword just like Machine Learning and Deep Learning was and continue to be.

In this lecture series, research scientists from leading AI research lab, DeepMind, deliver 12 lectures on an exciting selection of topics in Deep Learning, ranging from the fundamentals of training neural networks via advanced ideas around memory, attention, and generative modelling to the important topic of responsible innovation.

Sign in to report inappropriate content. This lecture is about the detailed architecture of RNN.

The human brain is an incredibly efficient source of intelligence. Earlier this month, OpenAI announced it had built the biggest AI model in history. This astonishingly large model, known as GPT-3, is an impressive technical achievement. Yet it highlights a troubling and harmful trend in the field of artificial intelligence--one that has not gotten enough mainstream attention. Modern AI models consume a massive amount of energy, and these energy requirements are growing at a breathtaking rate.

Light-weight: Edge devices have limited resources in terms of storage and computation capacity. Deep learning models are resource-intensive, so the models we deploy on edge devices should be light-weight with smaller binary sizes. As the inferences are made on the Edge device, a round trip from the device to the server will be eliminated, making inferences faster. Pre-trained: Models can be trained on-prem or cloud for different deep learning tasks like image classification, object detection, speech recognition, etc. and can be easily deployed to make inferences at the Edge. Light-weight: Edge devices have limited resources in terms of storage and computation capacity.

There exist significant gaps in research about how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article, we propose a comprehensive approach to design a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are most effective in accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F-Score that ranges from 95.2% to 97.8% with 0.03 to 0.05 coefficient of variation. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

Compositional generalization is a basic but essential intellective capability of human beings, which allows us to recombine known parts readily. However, existing neural network based models have been proven to be extremely deficient in such a capability. Inspired by work in cognition which argues compositionality can be captured by variable slots with symbolic functions, we present a refreshing view that connects a memory-augmented neural model with analytical expressions, to achieve compositional generalization. Our model consists of two cooperative neural modules Composer and Solver, fitting well with the cognitive argument while still being trained in an end-to-end manner via a hierarchical reinforcement learning algorithm. Experiments on a well-known benchmark SCAN demonstrate that our model seizes a great ability of compositional generalization, solving all challenges addressed by previous works with 100% accuracies.