Image Classification using CNN
Neural Networks are the programmable patterns that helps to solve complex problems and bring the best achievable output. Deep Learning as we all know is a step ahead of Machine Learning, and it helps to train the Neural Networks for getting the solution of questions unanswered and or improving the solution!
In this article, we will be implementing a Deep Learning Model using CIFAR-10 dataset. Read More
Daily Archives: October 27, 2020
Microsoft’s new Lobe app lets anyone train AI models
Unsupervised NLP : Methods and Intuitions behind working with unstructured texts
tldr; this is a primer in the domain of unsupervised techniques in NLP and their applications. It begins with the intuition behind word vectors, their use and advancements. This evolves to the centerstage discussion about the language models in detail — introduction, active use in industry and possible applications for different use-cases. Read More
Google, Cambridge, DeepMind & Alan Turing Institute’s ‘Performer’ Transformer Slashes Compute Costs
It’s no coincidence that Transformer neural network architecture is gaining popularity across so many machine learning research fields. Best known for natural language processing (NLP) tasks, Transformers not only enabled OpenAI’s 175 billion parameter language model GPT-3 to deliver SOTA performance, the power- and potential-packed architecture also helped DeepMind’s AlphaStar bot defeat professional StarCraft players. Researchers have now introduced a way to make Transformers more compute-efficient, scalable and accessible. Read More
Attention Is All You Need
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer,based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task,our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. Read More
Rick's Cafe AI 