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Improving Language Understanding with Unsupervised Learning

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Our system works in two stages; first we train a transformer model on a very large amount of data in an unsupervised manner -- using language modeling as a training signal -- then we fine-tune this model on much smaller supervised datasets to help it solve specific tasks. We developed this approach following our sentiment neuron work, in which we noted that unsupervised learning techniques can yield surprisingly discriminative features when trained on enough data. Here, we wanted to further explore this idea: can we develop one model, train it in an unsupervised way on a large amount of data, and then fine-tune the model to achieve good performance on many different tasks? Our results indicate that this approach works surprisingly well; the same core model can be fine-tuned for very different tasks with minimal adaptation. This work builds on the approach introduced in Semi-supervised Sequence Learning, which showed how to improve document classification performance by using unsupervised pre-training of an LSTM followed by supervised fine-tuning.


Machine Learning, incl. Deep Learning, with R

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Upper Confidence Bound) You will know how to evaluate your model, what underfitting and overfitting is, why resampling techniques are important, and how you can split your dataset into parts (train/validation/test). We will understand the theory behind deep neural networks. We will understand and implement convolutional neural networks - the most powerful technique for image recognition. Description Did you ever wonder how machines "learn" - in this course you will find out. We will cover all fields of Machine Learning: Regression and Classification techniques, Clustering, Association Rules, Reinforcement Learning, and, possibly most importantly, Deep Learning for Regression, Classification, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks, ... For each field, different algorithms are shown in detail: their core concepts are presented in 101 sessions.


Google Open-Sources ALBERT Natural Language Model

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Google AI has open-sourced A Lite Bert (ALBERT), a deep-learning natural language processing (NLP) model, which uses 89% fewer parameters than the state-of-the-art BERT model, with little loss of accuracy. The model can also be scaled-up to achieve new state-of-the-art performance on NLP benchmarks. The research team described the model in a paper to be presented at the International Conference on Learning Representations. ALBERT uses two optimizations to reduce model size: a factorization of the embedding layer and parameter-sharing across the hidden layers of the network. Combining these two approaches results in a baseline model with only 12M parameters, compared to BERT's 108M, while achieving an average of 80.1% accuracy on several NLP benchmarks compared with BERT's 82.3% average.


8 Leading Language Models For NLP In 2020

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The introduction of transfer learning and pretrained language models in natural language processing (NLP) pushed forward the limits of language understanding and generation. Transfer learning and applying transformers to different downstream NLP tasks have become the main trend of the latest research advances. At the same time, there is a controversy in the NLP community regarding the research value of the huge pretrained language models occupying the leaderboards. While lots of AI experts agree with Anna Rogers's statement that getting state-of-the-art results just by using more data and computing power is not research news, other NLP opinion leaders point out some positive moments in the current trend, like, for example, the possibility of seeing the fundamental limitations of the current paradigm. Anyway, the latest improvements in NLP language models seem to be driven not only by the massive boosts in computing capacity but also by the discovery of ingenious ways to lighten models while maintaining high performance.


A Beginner's Guide to Face Recognition with OpenCV in Python - Sefik Ilkin Serengil

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OpenCV becomes a de facto standard for image processing studies. The library offers some legacy techniques for face recognition as well. Local binary patterns histograms (LBPH), EigenFace and FisherFace methods are covered in the package. It is a fact that these conventional face recognition algorithms ARE NOT state-of-the-art techniques anymore. Nowadays, CNN based deep learning approaches overperform than these old-fashioned methods.