I have a pretty awesome backlog of blog posts from Udacity Self-Driving Car students, partly because they're doing awesome things and partly because I fell behind on reviewing them for a bit. Here are five that look pretty neat. This is a great blog post if you're looking to get started with point cloud files. The most popular laptop among Silicon Valley software developers is the Macbook Pro. The current version of the Macbook Pro, however, does not include an NVIDIA GPU, which restricts its ability to use CUDA and cuDNN, NVIDIA's tools for accelerating deep learning.

In today's competitive business environment, power plant owners are constantly striving to reduce their operation and maintenance costs, thus increasing their profits. To enable plant owners to operate their plants more efficiently, it is important to develop advanced digital solutions (software and tools) that can provide decision support for the plant operation optimization. For example, Digital Power Plant, a part of the GE's vision for the digitization of industrial assets, is one of such technologies recently developed in GE. Digital Power Plant involves building a collection of digital models (both physics-based and datadrive), or "Digital Twins" as we call it at GE, which are used to model the present state of every asset in a power plant. This transformational technology enables utilities to monitor and manage every aspect of the power generation ecosystem to generate electricity as cleanly, efficiently, and securely.

Unsupervised single-channel overlapped speech recognition is one of the hardest problems in automatic speech recognition (ASR). Permutation invariant training (PIT) is a state of the art model-based approach, which applies a single neural network to solve this single-input, multiple-output modeling problem. We propose to advance the current state of the art by imposing a modular structure on the neural network, applying a progressive pretraining regimen, and improving the objective function with transfer learning and a discriminative training criterion. The modular structure splits the problem into three sub-tasks: frame-wise interpreting, utterance-level speaker tracing, and speech recognition. The pretraining regimen uses these modules to solve progressively harder tasks. Transfer learning leverages parallel clean speech to improve the training targets for the network. Our discriminative training formulation is a modification of standard formulations, that also penalizes competing outputs of the system. Experiments are conducted on the artificial overlapped Switchboard and hub5e-swb dataset. The proposed framework achieves over 30% relative improvement of WER over both a strong jointly trained system, PIT for ASR, and a separately optimized system, PIT for speech separation with clean speech ASR model. The improvement comes from better model generalization, training efficiency and the sequence level linguistic knowledge integration.

Ontario is increasing support for students in the science, technology, engineering and mathematics (STEM) disciplines, including artificial intelligence, to continue to build a highly skilled workforce and support job creation and economic growth. Leading businesses from around the world choose Ontario because of its talented workforce, strong public education system and commitment to universal health care. These same qualities help to support an ecosystem that enables locally owned companies to succeed and grow. To bolster provincial competitiveness, the government plans to increase the number of postsecondary students graduating in the STEM disciplines by 25 per cent over the next five years. This initiative will boost the number of STEM graduates from 40,000 to 50,000 per year and position Ontario as the number one producer of postsecondary STEM graduates per capita in North America.

One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. The course will teach you those fundamental concepts by implementing practical exercises which are based on live examples.

One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. A DIFFERENT & MORE EFFECTIVE APPROACH TO LEARNING DATA SCIENCE: In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. WE'LL BUILD SUPERVISED MACHINE LEARNING ALGORITHMS TOGETHER: I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. LEARN BOTH THE THEORY & APPLICATION OF MACHINE LEARNING: The course will teach you those fundamental concepts by implementing practical exercises which are based on live examples.

The history of Artificial Intelligence isn't a long one, around 60-70 years, but the advances in recent years has been huge. The Modern Artificial Intelligence Infographic shows how technology coupled with studies of the human brain have aided in making AI a reality, and a reality we can use everyday. Machines are already intelligent, but we fail to recognise it. When a machine demonstrates intelligence we counter it by saying'it's not real intelligence'. Therefore Al becomes whatever has not been accomplished so far by a machine.

Google's AutoML system recently produced a series of machine-learning codes with higher rates of efficiency than those made by the researchers themselves. In this latest blow to human superiority the robot student has become the self-replicating master. AutoML was developed as a solution to the lack of top-notch talent in AI programming. There aren't enough cutting edge developers to keep up with demand, so the team came up with a machine learning software that can create self-learning code. The system runs thousands of simulations to determine which areas of the code can be improved, makes the changes, and continues the process ad infinitum, or until its goal is reached.

Back in May, Google revealed its AutoML project; artificial intelligence (AI) designed to help them create other AIs. Now, Google has announced that AutoML has beaten the human AI engineers at their own game by building machine-learning software that's more efficient and powerful than the best human-designed systems. An AutoML system recently broke a record for categorising images by their content, scoring 82 percent. While that's a relatively simple task, AutoML also beat the human-built system at a more complex task integral to autonomous robots and augmented reality: marking the location of multiple objects in an image. For that task, AutoML scored 43 percent versus the human-built system's 39 percent.

"With approachable text, examples, exercises, guidelines for teachers, a MATLAB toolbox and an accompanying web site, Bayesian Reasoning and Machine Learning by David Barber provides everything needed for your machine learning course. Jaakko Hollmén, Aalto University "Barber has done a commendable job in presenting important concepts in probabilistic modeling and probabilistic aspects of machine learning. The chapters on graphical models form one of the clearest and most concise presentations I have seen. The book has wide coverage of probabilistic machine learning, including discrete graphical models, Markov decision processes, latent variable models, Gaussian process, stochastic and deterministic inference, among others. The material is excellent for advanced undergraduate or introductory graduate course in graphical models, or probabilistic machine learning.