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            Natural Language Processing with TensorFlow培訓

             
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            上課地點:【上海】:同濟大學(滬西)/新城金郡商務樓(11號線白銀路站) 【深圳分部】:電影大廈(地鐵一號線大劇院站)/深圳大學成教院 【北京分部】:北京中山學院/福鑫大樓 【南京分部】:金港大廈(和燕路) 【武漢分部】:佳源大廈(高新二路) 【成都分部】:領館區1號(中和大道) 【沈陽分部】:沈陽理工大學/六宅臻品 【鄭州分部】:鄭州大學/錦華大廈 【石家莊分部】:河北科技大學/瑞景大廈 【廣州分部】:廣糧大廈 【西安分部】:協同大廈
            最近開課時間(周末班/連續班/晚班):2019年1月26日
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                    3、培訓合格學員可享受免費推薦就業機會。

            課程大綱
             

            Getting Started

            Setup and Installation
            TensorFlow Basics

            Creation, Initializing, Saving, and Restoring TensorFlow variables
            Feeding, Reading and Preloading TensorFlow Data
            How to use TensorFlow infrastructure to train models at scale
            Visualizing and Evaluating models with TensorBoard
            TensorFlow Mechanics 101

            Prepare the Data
            Download
            Inputs and Placeholders
            Build the Graph
            Inference
            Loss
            Training
            Train the Model
            The Graph
            The Session
            Train Loop
            Evaluate the Model
            Build the Eval Graph
            Eval Output
            Advanced Usage

            Threading and Queues
            Distributed TensorFlow
            Writing Documentation and Sharing your Model
            Customizing Data Readers
            Using GPUs
            Manipulating TensorFlow Model Files
            TensorFlow Serving

            Introduction
            Basic Serving Tutorial
            Advanced Serving Tutorial
            Serving Inception Model Tutorial
            Getting Started with SyntaxNet

            Parsing from Standard Input
            Annotating a Corpus
            Configuring the Python Scripts
            Building an NLP Pipeline with SyntaxNet

            Obtaining Data
            Part-of-Speech Tagging
            Training the SyntaxNet POS Tagger
            Preprocessing with the Tagger
            Dependency Parsing: Transition-Based Parsing
            Training a Parser Step 1: Local Pretraining
            Training a Parser Step 2: Global Training
            Vector Representations of Words

            Motivation: Why Learn word embeddings?
            Scaling up with Noise-Contrastive Training
            The Skip-gram Model
            Building the Graph
            Training the Model
            Visualizing the Learned Embeddings
            Evaluating Embeddings: Analogical Reasoning
            Optimizing the Implementation

             
              備案號:備案號:滬ICP備08026168號-1 .(2024年07月24日)...............
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