Problem: time series forecasting Challenge: forecasting for non-stationary signals and multiple future steps prediction ?? how to deal with non-stationary datasets?? Introduction one-step prediction problem VS multi-step prediction; multi-step foreca…
Training (deep) Neural Networks Part: 1 Nowadays training deep learning models have become extremely easy with high-quality libraries such as Torch and Theano. These libraries are really helpful for rapidly prototyping deep learning models even witho…
http://handong1587.github.io/deep_learning/2015/10/09/training-dnn.html //转载于 Training Deep Neural Networks Published: 09 Oct 2015 Category: deep_learning Tutorials Popular Training Approaches of DNNs — A Quick Overview https://medium.com/@asjad/p…
Problem: multi-horizon probabilistic forecasting tasks; Propose an end-to-end framework for multi-horizon time series forecasting, with temporal attention mechanisms to capture latent patterns. Introduction: forecasting ----- understanding demands. t…
w强化算法和数学,来迎接机器学习.神经网络. http://cs.stanford.edu/people/karpathy/convnetjs/ ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no comp…
Problem: high-dimensional time series forecasting ?? what is "high-dimensional" time series forecasting? one dimension for each individual time-series. n个time series为n维. A need for exploiting global pattern and coupling them with local calibrati…
目录 概 主要内容 Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[C]. international conference on artificial intelligence and statistics, 2010: 249-256. @article{glorot2010understanding, title={Understanding the…
目录 摘要 1.引言 2.BinaryConnect 2.1 +1 or -1 2.2确定性与随机性二值化 2.3 Propagations vs updates 2.4 Clipping 2.5 A few more tricks 2.6 Test-Time Inference 3 Benchmark results 3.1 Permutation-invariant MNIST 3.2 CIFAR-10 3.3 SVHN 4 Related works 5. Conclusion and f…