The landmark achievements of AlphaGo Zero have created great research interest into self-play in reinforcement learning. In self-play, Monte Carlo Tree Search is used to train a deep neural network, that is then used in tree searches. Training itself is governed by many hyperparameters.There has been surprisingly little research on design choices for hyper-parameter values and loss-functions, presumably because of the prohibitive computational cost to explore the parameter space. In this paper, we investigate 12 hyper-parameters in an AlphaZero-like self-play algorithm and evaluate how these parameters contribute to training. We use small games, to achieve meaningful exploration with moderate computational effort. The experimental results show that training is highly sensitive to hyper-parameter choices. Through multi-objective analysis we identify 4 important hyper-parameters to further assess. To start, we find surprising results where too much training can sometimes lead to lower performance. Our main result is that the number of self-play iterations subsumes MCTS-search simulations, game-episodes, and training epochs. The intuition is that these three increase together as self-play iterations increase, and that increasing them individually is sub-optimal. A consequence of our experiments is a direct recommendation for setting hyper-parameter values in self-play: the overarching outer-loop of self-play iterations should be maximized, in favor of the three inner-loop hyper-parameters, which should be set at lower values. A secondary result of our experiments concerns the choice of optimization goals, for which we also provide recommendations.

Google's mighty BERT and its siblings' transformer architecture models -- RoBERTa, XLNet, XLM, DistilBERT, ALBERT, CamemBERT, XLM-RoBERTa, FlauBERT have dominated the NLP research and most widely used in various downstream tasks like -- Text classification, NER, POS tagging, Machine Comprehension, Text Summarization, etc. In this article, we will focus on building a summarizer web app with BERT. The app will take URL of any kind -- News articles, blog posts, etc. and will return the summary of its content.

If not the most deadly, the novel coronavirus (COVID-19) is one of the most contagious diseases to have hit our green planet in the past decades. In little over three months since the virus was first spotted in mainland China, it has spread to more than 90 countries, infected more than 185,000 people, and taken more than 3,500 lives. As governments and health organizations scramble to contain the spread of coronavirus, they need all the help they can get, including from artificial intelligence. Though current AI technologies are far from replicating human intelligence, they are proving to be very helpful in tracking the outbreak, diagnosing patients, disinfecting areas, and speeding up the process of finding a cure for COVID-19. Data science and machine learning might be two of the most effective weapons we have in the fight against the coronavirus outbreak. Just before the turn of the year, BlueDot, an artificial intelligence platform that tracks infectious diseases around the world, flagged a cluster of "unusual pneumonia" cases happening around a market in Wuhan, China.

Use of our Services for any illegal or unauthorized purpose, or engage in, encourage or promote any activity that violates our Terms of Service and Privacy Policy are not permitted. Move Fast and Invent Things. Don't do anything I would do, and definitely don't do anything I wouldn't do. There is no fate but what we make for ourselves. I must not fear, fear is a mind killer.

A field that is bringing alot of commotion and noise is Artificial Intelligence. But something that really fascinates me is a subset of that field known as Artificial General Intelligence (AGI) or the holy grail of Artificial Intelligence. Many of today's machine learning or deep learning algorithms would be classified as Artificial Narrow Intelligence (ANI). I believe many of these algorithms are rapidly proliferating at the back end of most technologies we currently use from ride-sharing apps to social media and to other applications. And I believe that will continue to happen at an exponential pace until many specific tasks can be done better by algorithms than by humans.

Chico Caramago, a postdoctoral researcher in data science at the Oxford Internet Institute came to data science from a background in biology. "Biology is big, messy and complex," he told Built In, "so I was drawn toward tools that could help me make some sense out of that." Usually, humans make sense of the natural world's complexity with our own natural tools: our brains and our senses. Caramago was especially drawn to unsupervised machine learning and natural language processing, which helps humans with everything from detecting signs of metastasizing cancer to understanding foreign languages with Google Translate. At this point, in fact, data science has gotten so sophisticated that it doesn't just enhance our natural abilities -- it mimics them. Take deep learning, for example.

It's no secret that deep learning lets data science practitioners reach new levels of accuracy with predictive models. However, one of the drawbacks of deep learning is it typically requires huge data sets (not to mention big clusters). But with a little skill, practitioners with smaller data sets can still partake of deep learning riches. Deep learning has exploded in popularity, with good reason: Deep learning approaches, such as convolutional neural networks for computer (used primarily for image data) and recurrent neural networks (used primarily for language and textual data) can deliver higher accuracy and precision compared to "classical" machine learning approaches, like regression algorithms, gradient-boosted trees, and support vector machines. But that higher accuracy comes at a cost.

Vicarious, a secretive 10-year-old startup backed by Mark Zuckerberg, Elon Musk, and Jeff Bezos, reveals its progress and an initial customer. Google's TensorFlow is ready for quantum, but is AI ready for quantum?

Least mean squares (LMS) is a particular case of the backpropagation (BP) algorithm applied to single-layer neural networks with the mean squared error (MSE) loss. One drawback of the LMS is that the instantaneous weight update is proportional to the square of the norm of the input vector. Normalized least mean squares (NLMS) algorithm amends this drawback by dividing the weight changes by the square of the norm of the input vector. The affine projection algorithm (APA) improved the NLMS algorithm to weight update over a batch of recently seen samples. However, the application of NLMS and APA had been limited to single-layer networks and adaptive filters. In this paper, we consider a virtual target for each neuron of a multi-layer neural network and show that the BP algorithm is equivalent to training the weights of each layer using these virtual targets and the LMS algorithm. We also introduce a consequentialism interpretation of the NLMS and the APA algorithms that justifies their use in multi-layer neural networks. Given any optimization algorithm based on the BP over mini-batches, we propose a novel consequentialism method for updating the weights.Consequently, our proposed weight update can be applied both to plain stochastic gradient descent (SGD) and to momentum methods like RMSProp, Adam, and NAG. These ideas helped us to update the weights more carefully in such a way that minimization of the loss for one sample of the mini-batch does not interfere with other samples in that mini-batch. Our experiments show the usefulness of the proposed method in optimizing deep neural network architectures.

Deep neural networks are a very powerful tool for many computer vision tasks, including image restoration, exhibiting state-of-the-art results. However, the performance of deep learning methods tends to drop once the observation model used in training mismatches the one in test time. In addition, most deep learning methods require vast amounts of training data, which are not accessible in many applications. To mitigate these disadvantages, we propose to combine two image restoration approaches: (i) Deep Image Prior (DIP), which trains a convolutional neural network (CNN) from scratch in test time using the given degraded image. It does not require any training data and builds on the implicit prior imposed by the CNN architecture; and (ii) a backprojection (BP) fidelity term, which is an alternative to the standard least squares loss that is usually used in previous DIP works. We demonstrate the performance of the proposed method, termed BP-DIP, on the deblurring task and show its advantages over the plain DIP, with both higher PSNR values and better inference run-time.