deep learning mlp

remember not to run gradient checking, because in the run gradient checking with the BP for each layer of error calculation, which is time-consuming (but I feel that even if not calculated gradient Checking, do you want to use BP algorithm for inverse calculation? ）。 In the network training, do not set the initial value of the parameters to the same, because the learning of each layer of the parameters are ultimately the same, that is to learn the im

variables, and the parent process has also seen this modification. The vfork function may occur because the fork of the early system did not implement the write-time replication technology, resulting in a lot of useless work in each fork call (in most cases, it is called exec to execute a new program after fork) the efficiency is not high, so the vfork function is created. The current implementation basically uses the write-time replication technology, and when the vfork function is used improp

1. Why add pooling (pooling) to the convolutional networkIf you only use convolutional operations to reduce the size of the feature map, you will lose a lot of information. So think of a way to reduce the volume of stride, leaving most of the information, through pooling to reduce the size of feature map.Advantages of pooling:1. Pooled operation does not increase parameters2. Experimental results show that the model with pooling is more accurateDisadvantages of pooling:1. Because the stride of t

CSS deep understanding of learning notes-margin and css learning notes-margin 1. margin and container size Element size: ① visible size clientWidth (standard); ② occupying size 　　 Margin and visual size: ① applicable to normal block elements without width/height; ② applicable only to horizontal dimension Margin and occupy size: ① block/inline-block horizontal ele

Deep understanding of CSS learning notes border and css learning notes 1. border-width Border-width does not support percentages: semantics and scenarios are determined. In reality, the concepts of borders do not support percentages. Border-width supports keywords: thin, medium (default), and thick. The values are 1px, 3px, and 5px (except IE7 ). Why is the defau

above. Move right to erase the non-0-bit to the right of the decimal points of the result. These non-0 bits are actually positive, but because they are erased, the result subtracts the values of the non-0 bits represented by the original negative result, and the final result is rounded down rather than rounded to 0. Floating point number: Standard for representing floating-point numbers and their operations: IEEE Standard 754. Floating-point numbers are normalized, non-nor

Model optimization is important for both traditional machine learning and deep learning, especially in deep learning, and it is likely that more difficult challenges will need to be addressed during training. At present, the popular and widely used optimization algorithm has

Dueling Network architectures for deep reinforcement learningICML Best PaperAbsrtact: The contribution point of this paper is mainly in the DQN network structure, the features of convolutional neural network are divided into two paths, namely: the state value function and the State-dependent action Advantage function.. The main feature of this design is generalize learning across actions without imposing an

This is mainly from self-study to deep learning, simple record as follows:(1) deep Learning is more expressive than shallow network learning, and it expresses much more function set than shallow network in a compact and concise way. (2) The shortcomings of data acquisition,

Deep Learning Database Summary Thanks for the collection. Source: https://blog.csdn.net/chaipp0607/article/details/71403797 The preparation of the data is necessary to train the model, which is obviously time-consuming, so we can use the existing open source image Library to quickly prepare for the initial work in the introductory phase: ImageNet Imagenet is an image database organized according to the Wo

Objective In-depth learning Redis (3): Master-slave replication has mentioned that the role of Redis master-slave replication is data hot standby, load balancing, failure recovery, etc. but one problem with master-slave replication is that failback cannot be automated. This article will introduce the Sentinel, which is based on Redis master-slave replication, the main role is to solve the primary node failure recovery automation problems, and further

This brief introduction to the MSRA initialization method is also derived from He Keming paper delving deep into rectifiers:surpassing human-level performance on ImageNet Classification ". Motivation MSRA initialization Derivation proof Additional Information MotivationNetwork initialization is a very important thing. However, the traditional Gaussian distribution of fixed variance is initialized, whi

Image representations and New Domains inneural image captioningWe find that a State-of-theart neuralcaptioning algorithm are able to produce quality captions even when Providedwith SURPR isingly Poor image representationsDeep Boosting:joint Feature Selection andanalysis Dictionary Learning in HierarchyThis work investigates how the Traditionalimage classification pipelines can is extended into a deep archit

Deep Learning first battle: complete: ufldl tutorial sparse self-encoder-exercise: sparse autoencodercode: learned sparse parameter W1: References: Ufldl tutorial sparse self-Encoder Read autoencoders articles: [3] Hinton, G. E., osindero, S., teh, Y. (2006). A fast learning algorithm for deep belief nets [4] Hi

Without a GPU, deep learning is not possible. But when you do not optimize anything, how to make all the teraflops are fully utilized. With the recent spike in bitcoin prices, you can consider using these unused resources to make a profit. It's not hard, all you have to do is set up a wallet, choose what to dig, build a miner's software and run it. Google searches for "how to start digging on the GPU", and

Transferred from: http://baojie.org/blog/2013/01/27/deep-learning-tutorials/A few good deep learning tutorials, with basic videos and speeches. Two articles and a comic book are attached. There are some additions later.Jeff Dean @ StanfordHttp://i.stanford.edu/infoseminar/dean.pdfAn introductory introduction to what DL

ArXiv is a e-print service in the fields of physics, mathematics, Computer science, quantitative biology, quantitative fiNance and statistics. There ' ll is lots of papers in advance. Here's some recent papers which is important or interesting.1. Object Detectioneven faster than fast rcnn, more end-to-end for detection, more accurateYou have look once:unified, real-time Object DetectionFaster r-cnn:towards Real-time Object Detection with Regionproposal Network2. CNN Itselfmore faster over CNNFas

　　Sparse Coding: This section briefly introduces Sparse Coding, because Sparse Coding is also an important branch in deep learning and can also extract good features of a dataset. The content of this article is to refer to the Stanford deep learning Tutorial: Sparse Coding, Sparse Coding: autoencoder interpretation. Fo

Recently, deep learning is very popular, but it is found that there are very few forums dedicated to DL discussion on the Internet, so communication is particularly inconvenient. I have just started to contact DL and cainiao. I hope to communicate with many like-minded friends. I have established a high quality QQ Group for deep

1. Problems faced by deep learning:1) The model is getting bigger, difficult to deploy on the mobile side, and it is difficult to update the network.2) The training time is getting longer, limiting the production of researchers.3) Too much energy, hardware costs expensive.Workaround: Joint design algorithms and hardware.Computing hardware can be divided into two categories: general and private. Generic hard