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TensorFlow for Image Recognition培训

 
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       坚持小班授课,为保证培训效果,增加互动环节,每期人数限3到5人。
   上课时间和地点
上课地点:【上海】:同济大学(沪西)/新城金郡商务楼(11号线白银路站) 【深圳分部】:电影大厦(地铁一号线大剧院站)/深圳大学成教院 【北京分部】:北京中山学院/福鑫大楼 【南京分部】:金港大厦(和燕路) 【武汉分部】:佳源大厦(高新二路) 【成都分部】:领馆区1号(中和大道) 【沈阳分部】:沈阳理工大学/六宅臻品 【郑州分部】:郑州大学/锦华大厦 【石家庄分部】:河北科技大学/瑞景大厦 【广州分部】:广粮大厦 【西安分部】:协同大厦
最近开课时间(周末班/连续班/晚班):2019年1月26日
   实验设备
     ☆资深工程师授课
        
        ☆注重质量 ☆边讲边练

        ☆合格学员免费推荐工作
        ★实验设备请点击这儿查看★
   质量保障

        1、培训过程中,如有部分内容理解不透或消化不好,可免费在以后培训班中重听;
        2、课程完成后,授课老师留给学员手机和Email,保障培训效果,免费提供半年的技术支持。
        3、培训合格学员可享受免费推荐就业机会。

课程大纲
 

Machine Learning and Recursive Neural Networks (RNN) basics

NN and RNN
Backpropagation
Long short-term memory (LSTM)
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

Tutorial Files
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 GPUs¹
Manipulating TensorFlow Model Files
TensorFlow Serving

Introduction
Basic Serving Tutorial
Advanced Serving Tutorial
Serving Inception Model Tutorial
Convolutional Neural Networks

Overview
Goals
Highlights of the Tutorial
Model Architecture
Code Organization
CIFAR-10 Model
Model Inputs
Model Prediction
Model Training
Launching and Training the Model
Evaluating a Model
Training a Model Using Multiple GPU Cards¹
Placing Variables and Operations on Devices
Launching and Training the Model on Multiple GPU cards
Deep Learning for MNIST

Setup
Load MNIST Data
Start TensorFlow InteractiveSession
Build a Softmax Regression Model
Placeholders
Variables
Predicted Class and Cost Function
Train the Model
Evaluate the Model
Build a Multilayer Convolutional Network
Weight Initialization
Convolution and Pooling
First Convolutional Layer
Second Convolutional Layer
Densely Connected Layer
Readout Layer
Train and Evaluate the Model
Image Recognition

Inception-v3
C++
Java
¹ Topics related to the use of GPUs are not available as a part of a remote course. They can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs, with 64-bit Linux installed (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.

 
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