使用TensorFlow的高级深度学习

深度神经网络，卷积神经网络，递归神经网络，LSTM，GRU，张量流

你会学到什么
人工神经网络，多层感知器
卷积神经网络，递归神经网络，LSTM，GRUs
TensorFlow，Keras，Google Colab
真实世界项目和案例研究

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2声道
语言：英语+中英文字幕（云桥CG资源站 机译） |时长:57节课(8小时42米)|大小解压后:3.21 GB


要求
没有编程经验，有数据科学基础知识者优先

描述
本课程简化了高级深度学习概念，如深度神经网络、卷积神经网络、递归神经网络、长短期记忆(LSTM)、门控递归单元(GRU)等。TensorFlow、Keras、Google Colab、真实世界项目以及回归和分类等主题的案例研究都有详细描述。将详细讨论自动驾驶汽车等高级案例研究。目前这门课程没有多少案例研究。目标是尽快包含至少20个真实世界的项目。

还将包括对象检测等主题的案例研究。TensorFlow和Keras基础知识和高级概念已经过详细讨论。本课程的最终目标是让学习者能够利用深度学习解决现实世界的问题。完成本课程后，学习者还应能够通过Google TensorFlow认证考试，这是一项著名的认证。学员在完成课程后还将获得Udemy颁发的结业证书。Advanced Deep Learning With TensorFlow

学完这门课程后，学习者将精通以下主题。

a)理论深度学习概念。

b)卷积神经网络

长短期记忆

d)生成性对抗网络

e)编码器-解码器模型

f)注意力模型

g)物体检测

h)图像分割

I)迁移学习

j)使用Python打开CV

k)构建和部署深度神经网络

l)专业谷歌张量流开发者

m)使用Google Colab编写深度学习代码

n)用于深度神经网络的Python编程

建议学员在观看本课程的编程视频时练习张量流代码。

前几个部分已经上传，课程处于更新阶段，其余部分将很快添加。

这门课程是给谁的
深度学习初学者
对深度学习感兴趣的学生、专业人士和学习者

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 57 lectures (8h 42m) | Size: 3.1 GB

Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, LSTM, GRU,TensorFlow

What you’ll learn
Artificial Neural Networks, Multilayered Perceptron
Convolutional Neural Networks, Recurrent Neural Networks,LSTM,GRUs
TensorFlow, Keras, Google Colab
Real World Projects and Case Studies

Requirements
No Programming Experience, Basic knowledge of Data Science will be an advantage

Description
This Course simplifies the advanced Deep Learning concepts like Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short Term Memory (LSTM), Gated Recurrent Units(GRU), etc. TensorFlow, Keras, Google Colab, Real World Projects and Case Studies on topics like Regression and Classification have been described in great detail. Advanced Case studies like Self Driving Cars will be discussed in great detail. Currently the course has few case studies.The objective is to include at least 20 real world projects soon.

Case studies on topics like Object detection will also be included. TensorFlow and Keras basics and advanced concepts have been discussed in great detail. The ultimate goal of this course is to make the learner able to solve real world problems using deep learning. After completion of this course the Learner shall also be able to pass the Google TensorFlow Certification Examination which is one of the prestigious Certification. Learner will also get the certificate of completion from Udemy after completing the Course.

After taking this course the learner will be expert in following topics.

a) Theoretical Deep Learning Concepts.

b) Convolutional Neural Networks

c) Long-short term memory

d) Generative Adversarial Networks

e) Encoder- Decoder Models

f) Attention Models

g) Object detection

h) Image Segmentation

i) Transfer Learning

j) Open CV using Python

k) Building and deploying Deep Neural Networks

l) Professional Google Tensor Flow developer

m) Using Google Colab for writing Deep Learning code

n) Python programming for Deep Neural Networks

The Learners are advised to practice the Tensor Flow code as they watch the videos on Programming from this course.

First Few sections have been uploaded, The course is in updation phase and the remaining sections will be added soon.

Who this course is for
Deep Learning beginners
Students, Professionals, Learners who are curious about Deep Learning
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