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predicting

  • We address the problem of predicting a word from previous words in a sample of text. In particular,

    We address the problem of predicting a word from previous words in a sample of text. In particular, we discuss n-gram models based on classes of words. We also discuss several statistical algorithms for assigning words to classes based on the frequency of their co-occurrence with other words. We find that we are able to extract classes that have the flavor of either syntactically based groupings or semantically based groupings, depending on the nature of the underlying statistics.

    標簽： predicting particular previous address

    上傳時間： 2016-12-26

    上傳用戶：xfbs821

  • The Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG are finalising a new standard for the cod

    The Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG are finalising a new standard for the coding (compression) of natural video images. The new standard [1,2] will be known as H.264 and also MPEG-4 Part 10, “Advanced Video Coding”. This document describes the methods of predicting intra-coded macroblocks in an H.264 CODEC.

    標簽： finalising standard Joint ITU-T

    上傳時間： 2013-12-28

    上傳用戶：guanliya

  • base implementaion of the protein (starting from the amino acid sequence) feature extractor used in

    base implementaion of the protein (starting from the amino acid sequence) feature extractor used in "L. Nanni and A. Lumini, An ensemble of Support Vector Machines for predicting virulent proteins, Expert Systems With Applications, vol.36, no.4, pp.7458-7462, May 2009. "

    標簽： implementaion extractor the starting

    上傳時間： 2017-08-02

    上傳用戶：wqxstar

  • Nonlinear_Distortion_in_Wireless_Systems

    Modeling and simulation of nonlinear systems provide communication system designers with a tool to predict and verify overall system performance under nonlinearity and complex communication signals. Traditionally, RF system designers use deterministic signals (discrete tones), which can be implemented in circuit simulators, to predict the performance of their nonlinear circuits/systems. However, RF system designers are usually faced with the problem of predicting system performance when the input to the system is real-world communication signals which have a random nature.

    標簽： Nonlinear_Distortion_in_Wireless_ Systems

    上傳時間： 2020-05-31

    上傳用戶：shancjb

  • 《Python深度學習》2018中文版+源代碼

    這是我在做大學教授期間推薦給我學生的一本書，非常好，適合入門學習。《python深度學習》由Keras之父、現(xiàn)任Google人工智能研究員的弗朗索瓦?肖萊（Franc?ois Chollet）執(zhí)筆，詳盡介紹了用Python和Keras進行深度學習的探索實踐，包括計算機視覺、自然語言處理、產(chǎn)生式模型等應用。書中包含30多個代碼示例，步驟講解詳細透徹。作者在github公布了代碼，代碼幾乎囊括了本書所有知識點。在學習完本書后，讀者將具備搭建自己的深度學習環(huán)境、建立圖像識別模型、生成圖像和文字等能力。但是有一個小小的遺憾：代碼的解釋和注釋是全英文的，即使英文水平較好的朋友看起來也很吃力。本人認為，這本書和代碼是初學者入門深度學習及Keras最好的工具。作者在github公布了代碼，本人參照書本，對全部代碼做了中文解釋和注釋，并下載了代碼所需要的一些數(shù)據(jù)集（尤其是“貓狗大戰(zhàn)”數(shù)據(jù)集），并對其中一些圖像進行了本地化，代碼全部測試通過。（請按照文件順序運行，代碼前后有部分關(guān)聯(lián)）。以下代碼包含了全書約80%左右的知識點，代碼目錄：2.1: A first look at a neural network（ 初識神經(jīng)網(wǎng)絡）3.5: Classifying movie reviews（電影評論分類：二分類問題）3.6: Classifying newswires（新聞分類：多分類問題 ）3.7: predicting house prices（預測房價：回歸問題）4.4: Underfitting and overfitting（ 過擬合與欠擬合）5.1: Introduction to convnets（卷積神經(jīng)網(wǎng)絡簡介）5.2: Using convnets with small datasets（在小型數(shù)據(jù)集上從頭開始訓練一個卷積網(wǎng)絡）5.3: Using a pre-trained convnet（使用預訓練的卷積神經(jīng)網(wǎng)絡）5.4: Visualizing what convnets learn（卷積神經(jīng)網(wǎng)絡的可視化）

    標簽： python 深度學習

    上傳時間： 2022-01-30

    上傳用戶：

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