課程簡(jiǎn)介
幻燈片算法講解,結(jié)合代碼分析
目標(biāo)收益
結(jié)合實(shí)際應(yīng)用舉例和和業(yè)界趨勢(shì)分析
既有 TensorFlow 的案例，也有高層類庫(kù) Keras 的實(shí)踐
培訓(xùn)對(duì)象
對(duì)深度學(xué)習(xí)算法原理和應(yīng)用感興趣的技術(shù)人員，具有一定編程(Python)和數(shù)學(xué)基礎(chǔ)(線性代數(shù)、微積分、概率論)的技術(shù)人員。
課程大綱
| 1. TensorFlow 入門 |
- Overview - Graphs and Sessions 圖和會(huì)話 1) Tensor 2)Data Flow Graphs 3)Graph and sub-Graph - Distributed Computation 分布式計(jì)算 |
| 2. TensorFlow Ops 操作符 |
- Basic operations - Tensor types - Constants and variables - Feeding inputs - TensorBoard |
| 3. Basic Model 基本模型 |
- Linear regression in TensorFlow - Optimizers - Logistic regression - Loss functions |
| 4. Model Structure 模型結(jié)構(gòu) |
- Overall structure of a model in TensorFlow - word2vec - Name scope - Embedding visualization |
| 5. Experiments Management 實(shí)驗(yàn)管理 |
- tf.train.Saver - tf.summary - Randomization - Data Readers |
| 6. Application 實(shí)戰(zhàn) |
- AutoEncoder - MLP - CNN（AlexNet，VGGNet，Inception Net，ResNet） - RNN（LSTM，Bi-RNN） - Deep Reinforcement Learning（Policy Network、Value Network） |
|
1. TensorFlow 入門 - Overview - Graphs and Sessions 圖和會(huì)話 1) Tensor 2)Data Flow Graphs 3)Graph and sub-Graph - Distributed Computation 分布式計(jì)算 |
|
2. TensorFlow Ops 操作符 - Basic operations - Tensor types - Constants and variables - Feeding inputs - TensorBoard |
|
3. Basic Model 基本模型 - Linear regression in TensorFlow - Optimizers - Logistic regression - Loss functions |
|
4. Model Structure 模型結(jié)構(gòu) - Overall structure of a model in TensorFlow - word2vec - Name scope - Embedding visualization |
|
5. Experiments Management 實(shí)驗(yàn)管理 - tf.train.Saver - tf.summary - Randomization - Data Readers |
|
6. Application 實(shí)戰(zhàn) - AutoEncoder - MLP - CNN（AlexNet，VGGNet，Inception Net，ResNet） - RNN（LSTM，Bi-RNN） - Deep Reinforcement Learning（Policy Network、Value Network） |
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