"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).]
Welcome to the Lenet tutorial using TensorFlow. From being a long time user of Theano, migrating to TensorFlow is not that easy. This documentation website that comes along with this repository might help users migrating from theano to tensorflow, just as I did while implementing this repository. Once the code is running, setup tensorboard to observe results and outputs.
The eight technologies added to the Hype Cycle this year include 5G, Artificial General Intelligence, Deep Learning, Deep Reinforcement Learning, Digital Twin, Edge Computing, Serverless PaaS and Cognitive Computing. Ten technologies not included in the hype cycle for 2017 include 802.11ax, The three most dominant trends include Artifical Intelligence (AI) Everywhere, Transparently Immersive Experiences, and Digital Platforms. Gartner believes that key platform-enabling technologies are 5G, Digital Twin, Edge Computing, Blockchain, IoT Platforms, Neuromorphic Hardware, Quantum Computing, Serverless PaaS and Software-Defined Security. Key takeaways from this year's Hype Cycle include the following:
So, to do this, I extracted the TOP 100 articles posted on medium, related to AI/ML/DL and created a list with all of them, so that I can read them in the coming weeks. For the top 100 articles, the average reading time is 9.1 minutes. For the top 100 articles, the average post contains 10 images. For the top 100 articles, the average post contains 9.4 links.
Machine learning is currently one of the most talked about concepts in the world of technology and computers. A highly promising topic, machine learning is also quite controversial among people who are not aware of its nature and benefits. This book will help you with this mission, as you will find all the required and relevant data regarding machine learning gathered in one single text. An absolute must for beginners and the curious, this book answers all the questions and queries that you might have about machine learning.
Thus the theme of Marko's useful offering, which gets into the practicalities of "automated algorithm selection," where the proper algorithm for a narrower use case is machine-determined. He also dishes on Disney's anti-Netflix manouvers (Disney to launch own streaming service, pulling movies from Netflix). Sidenote: how many streaming services will consumers have to shell out for to get the content they want? As Riley points out, audits can serve as a sales tactic in the absence of a natural upselling event (Or, I'd add, if such an upsell is met with disinterest).
Neural nets intuition • Input: • Features (coordinates in feature space) • Output: • A predicted class at every coordinate in the feature space white black black y x 24. Neural nets intuition • The combination of several features make classes distinguishable • Training on more examples doesn't always work • Neural networks can handle thousands of features white black black 0 1 y x 0 & 1 27. Insights • High dimensional • Can handle and find correlations between lots of features • They do probabilistic predictions • They require a training set and a test set for validation 37. from tensorflow.contrib.learn Conclusion • You can use machine learning using a high level api • It can make complex decisions on lots of features • Using high level api's you can do experiments without knowing all the mathematical details • When you have your first results and want to improve • Learn more • Ask for expert help 39. Dijkstra probably hates me - Linus Torvalds, in kernel/sched.c Conclusion • You can use machine learning using a high level api • It can make complex decisions on lots of features • Using high level api's you can do experiments without knowing all the mathematical details • When you have your first results and want to improve • Learn more • Ask for expert help
I will then outline practices that are relevant for the most common tasks, in particular classification, sequence labelling, natural language generation, and neural machine translation. For training deep neural networks, some tricks are essential to avoid the vanishing gradient problem. Let us augment the layer output \(h\) and layer input \(x\) with indices \(l\) indicating the current layer. They have also found to be useful for Multi-Task Learning of different NLP tasks (Ruder et al., 2017) , while a residual variant that uses summation has been shown to consistently outperform residual connections for neural machine translation (Britz et al., 2017) .
In its simplest form, Relational Reasoning is learning to understand relations between different objects(ideas). It can accept encoded objects and learn relations from them, but more importantly, they can be plugged into Convolutional Neural networks, and LSTMs. The authors have presented a way to combine relational networks, convolutional networks, and LSTMs to construct an end to end neural network that can learn relations between objects. Figure 2.0 An end to end relational reasoning neural network.
In ice hockey, Toronto-based startup ICEBERG is using AI and computer vision to give teams a nuanced understanding of the data behind player positioning and activity. AI systems have beat the world's top eSports players at complicated multi-player games like Super Smash Bros. and the world's top poker pros in no-limit Texas Hold'em. Canary Speech, a Utah-based startup, is building voice tests that use deep learning to pick up the subtle voice tremors, slower speech, and gaps between words that may reveal concussions. GPUs (graphics processing units) are commonly used to digest the enormous amounts of data involved in AI deep learning algorithms, including all of the above applications.