Generative Ai

cnn-benchmarks Benchmarks for popular convolutional neural network models on CPU and different GPUs, with and without cuDNN. https://github.com/jcjohnson/cnn-benchmarks

Курс по Tensorflow от BigDataUniversity (free) https://bigdatauniversity.com/courses/deep-learning-tensorflow/ ML0120EN COURSE LEVEL: Advanced TIME TO COMPLETE: 10 Hours #tensorflow

Port of Single Shot MultiBox Detector to Keras (SSD) https://arxiv.org/pdf/1512.02325v3.pdf https://github.com/rykov8/ssd_keras #keras #ssd

Команда Google DeepMind представила новый ИИ, способный самостоятельно учиться выполнять задачи https://tproger.ru/news/deepmind-new-algorithm/

LipNet: Automated lipreading - Чтение по губам. 93% распознавание сетью (люди 52%) Lipreading is the task of decoding text from the movement of a speaker’s mouth. Traditional approaches separated the problem into two stages: designing or learning visual features, and prediction. http://www.oxml.co.uk/publications/2016-Assael_Shillingford_LipNet.pdf https://www.youtube.com/watch?v=fa5QGremQf8

Когда мы сможем загрузить мозг в компьютер Специалист по машинному обучению, создатель одной из сильнейших шахматных программ Сергей Марков прочитал в Москве лекцию о перспективах перемещения сознания на другой носитель. О прогрессе в области нейроинтерфейсов, первых киборгах и перспективах нейронных сетей — в материале «Футуриста». http://futurist.ru/articles/558

Запись и презентация с вводного вебинара по DeepLearning от Александра Гончар для студентов СФ БашГУ. https://www.youtube.com/watch?v=8pbQ9Pve8bo https://www.linkedin.com/in/alex-honchar-4423b962

Multifaceted Feature Visualization. Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks. We can better understand deep neural networks by identifying which features each of their neurons have learned to detect. Here, we introduce an algorithm that explicitly uncovers the multiple facets of each neuron by producing a synthetic visualization of each of the types of images that activate a neuron. We also introduce regularization methods that produce state-of-the-art results in terms of the interpretability of images obtained by activation maximization. https://github.com/Evolving-AI-Lab/mfv https://arxiv.org/pdf/1602.03616v2.pdf http://www.gitxiv.com/posts/Kqy2rHju5EsqpC32N/multifaceted-feature-visualization