https://simplestory.github.io/en/categories/2019/Recent content in 2019 on SimplestoryHugoen-usSimplestoryThu, 03 Oct 2019 00:00:00 +0000https://simplestory.github.io/en/posts/20191003_34df20bab5e85dc75bfc94ef569cced9/Thu, 03 Oct 2019 00:00:00 +0000https://simplestory.github.io/en/posts/20191003_34df20bab5e85dc75bfc94ef569cced9/Single Shot MultiBox Detectorhttps://simplestory.github.io/en/posts/20190922_5e8040095d43e97ccedd09771f9eab38/Sun, 22 Sep 2019 00:00:00 +0000https://simplestory.github.io/en/posts/20190922_5e8040095d43e97ccedd09771f9eab38/An object detection algorithmhttps://simplestory.github.io/en/posts/20190921_817fe11366b1149fdc44a5a4c92685c4/Sat, 21 Sep 2019 00:00:00 +0000https://simplestory.github.io/en/posts/20190921_817fe11366b1149fdc44a5a4c92685c4/A classic object detection algorithmhttps://simplestory.github.io/en/posts/20190919_13243638926ee79bc7810c80a460408c/Thu, 19 Sep 2019 00:00:00 +0000https://simplestory.github.io/en/posts/20190919_13243638926ee79bc7810c80a460408c/A classic object detection algorithmhttps://simplestory.github.io/en/posts/20190910_cefb8aa8290a0b4cfddf3f88bf5a2a3c/Tue, 10 Sep 2019 00:00:00 +0000https://simplestory.github.io/en/posts/20190910_cefb8aa8290a0b4cfddf3f88bf5a2a3c/<blockquote> <p>First of all, happy Teachers&rsquo; Day to all teachers, and thank you for your spirit of giving roses to others with a lingering fragrance on your hands. Then, after almost a month, I have rolled back to update the blog again; before that I was busy with joining the company, hoping to fit into the company&rsquo;s development work as soon as possible, so I was &ldquo;busy&rdquo; for a month. Next let us continue with the introduction of classic image recognition network models. In previous posts we already introduced the AlexNet and VGG networks, both of which pushed the depth of neural networks of that time to the extreme, and subsequent neural network models basically performed no better. This is because as the network gets deeper, during error backpropagation the weights of the earlier network layers basically do not get updated and stay around some fixed value. In 2015, Kaiming He proposed a shortcut connection that propagates the error to the earlier network layers, greatly improving network performance. Below is a brief analysis of the ResNet network.</p> </blockquote>https://simplestory.github.io/en/posts/20190811_58bd041ca5bf7b009cdc8d07392dc061/Sun, 11 Aug 2019 00:00:00 +0000https://simplestory.github.io/en/posts/20190811_58bd041ca5bf7b009cdc8d07392dc061/<blockquote> <p>Neural networks already have several normalization algorithms. They all look very similar and basically share a unified form. This post refers to the Zhihu column SIGAI&rsquo;s article on normalization algorithms; the link is at the bottom of the post.</p> </blockquote> <h2 id="the-basic-problem">The basic problem</h2> <p>When using a neural network to learn from a dataset, because the sources of the data may differ considerably, the values of different attributes may also span very different ranges; all of this causes errors in how the data is treated and leads to poor learning by the network. To solve this problem we normalize the data, that is, we regularize data whose value ranges vary widely into the same scale, which is beneficial for the network&rsquo;s learning. Up to now normalization has many variants, the common ones being Batch Normalization (BN), Layer Normalization (LN), Instance Normalization (IN) and Group Normalization (GN). In fact their processing is similar (subtract the mean, divide by the standard deviation, then apply a linear mapping); the difference lies in the dimension along which the statistics are computed. The concrete formula is as follows:</p>https://simplestory.github.io/en/posts/20190730_1036fd1af9bc59d1a811b4e2ab23d60c/Tue, 30 Jul 2019 00:00:00 +0000https://simplestory.github.io/en/posts/20190730_1036fd1af9bc59d1a811b4e2ab23d60c/A classic convolutional neural networkhttps://simplestory.github.io/en/posts/20190722_cdebdc3cc4ecd47a04cff48d03a688d3/Mon, 22 Jul 2019 00:00:00 +0000https://simplestory.github.io/en/posts/20190722_cdebdc3cc4ecd47a04cff48d03a688d3/A classic convolutional neural networkhttps://simplestory.github.io/en/posts/20190324_161af8745042398050ad585874c3eec3/Sun, 24 Mar 2019 00:00:00 +0000https://simplestory.github.io/en/posts/20190324_161af8745042398050ad585874c3eec3/A Highly Efficient Gradient Boosting Decision Treehttps://simplestory.github.io/en/posts/20190317_8fc39e31f30337e40756914df1f8b607/Sun, 17 Mar 2019 00:00:00 +0000https://simplestory.github.io/en/posts/20190317_8fc39e31f30337e40756914df1f8b607/A Scalabe Tree Boosting Systeam