Reinforcement Learning is one of the most in demand research topics whose popularity is only growing day by day. Reinforcement learning (RL) translates to learning by interacting from the surrounding environment. An RL expert learns from experience, rather than being explicitly taught, which is essentially trial and error learning. To understand RL, Analytics Insight compiles the Top 10 Reinforcement Learning Courses and Certifications in 2020. Read More
Tag Archives: Training
Machine Learning Courses
A thoughtful user on Github has put together a tremendous list of courses for AI and machine learning. He covers everything from introductory to advanced lectures. Read More
Deepfakes Are Becoming the Hot New Corporate Training Tool
This month, advertising giant WPP will send unusual corporate training videos to tens of thousands of employees worldwide. A presenter will speak in the recipient’s language and address them by name, while explaining some basic concepts in artificial intelligence. The videos themselves will be powerful demonstrations of what AI can do: The face, and the words it speaks, will be synthesized by software.
WPP doesn’t bill them as such, but its synthetic training videos might be called deepfakes, a loose term applied to images or videos generated using AI that look real. Read More
Top 10 Courses to Learn AI, Machine Learning and Deep Learning
Supervised, semi-supervised or unsupervised deep learning is part of a broader family of machine learning methods, that teach you the basics of neural networks. Learn from the Top 10 Deep Learning Courses curated exclusively by Analytics Insight and build your deep learning models with Python and NumPy. Read More
Sharing: Take “AI for Everyone” Course Or Not? — A Course By deeplearning.ai
Great overview by Sik-Ho Tsang. Totally agree!
Recently, I’ve just taken the course shown as above, an AI non-technical introductory course, “AI for Everyone”, taught by Prof. Andrew Ng, deeplearning.ai, through Coursera. I am not advertising it, or talk about the course content in details. Rather, I would like to tell my sharings about it. Here’s the link: https://www.coursera.org/learn/ai-for-everyone/. Read More
Royal Dutch Shell reskills workers in artificial intelligence as part of huge energy transition
- Royal Dutch Shell is collaborating with Udacity to digitally train its workers in artificial intelligence.
- This began long before the coronavirus pandemic and the company continues to use this training method.
- The digital workforce skilling platform may become the training method of choice for a growing number of companies who need to keep employees up to speed in the weeks and months ahead.
Hand labeling is the past. The future is #NoLabel AI
Data labeling is so hot right now… but could this rapidly emerging market face disruption from a small team at Stanford and the Snorkel open source project, which enables highly efficient programmatic labeling that is 10 to 1,000x as efficient as hand labeling? Read More
Not to ML when your problem…
Google DeepMind’s ‘Sideways’ takes a page from computer architecture
Increasingly, machine learning forms of artificial intelligence are contending with the limits of computing hardware, and it’s causing scientists to rethink how they design neural networks.
That was clear in last week’s research offering from Google, called Reformer, which aimed to stuff a natural language program into a single graphics processing chip instead of eight. Read More
Putting An End to End-to-End:Gradient-Isolated Learning of Representations
We propose a novel deep learning method for local self-supervised representation learning that does not require labels nor end-to-end backpropagation but exploits the natural order in data instead. Inspired by the observation that biological neural networks appear to learn without backpropagating a global error signal, we split a deep neural network into a stack of gradient-isolated modules. Each module is trained to maximally preserve the information of its inputs using the InfoNCE bound from Oord et al. [2018]. Despite this greedy training, we demonstrate that each module improves upon the output of its predecessor, and that the representations created by the top module yield highly competitive results on downstream classification tasks in the audio and visual domain. The proposal enables optimizing modules asynchronously, allowing large-scale distributed training of very deep neural networks on unlabelled datasets. Read More
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