TensorFlow 2.x Essentials – 2021

TensorFlow 2.x现在是数据科学市场上最热门的需求之一。由于其定制，处理大数据的能力，速度，机器学习，深度学习和概率模型的开发以及模型定制（研究和开发），使其在当今世界的行业中具有广泛的应用。许多行业正在寻找具有这些技能的数据科学家。本课程涵盖使用TensorFlow 2.x的建模技术。
我们从TensorFlow 2.x编程开始，这是必需的基本技能，然后我们将对海量数据进行必要的预处理。
然后，在整个课程中，我们将使用TensorFlow中的梯度下降算法构建自定义回归模型。

Created by Devi Guskra, Data Science Anywhere Team | Published 3/2021
Duration: 1h 55m | 5 sections | 19 lectures | Video: 1280×720, 44 KHz | 705 MB
Genre: eLearning | Language: English + Sub

Learn TensorFlow 2.0 essential for model building in Python

What you’ll learn
TensoFlow 2.x
TensorBoard
Gradient Descent
Linear Regression
Customizing Model Design in TensorFlow

Requirements
Basics of PythonGmail AccountFamiliar with Google Drive
Description
Welcome to TensorFlow 2.x Essentials 2021
TensorFlow 2.x is now one of the hottest demands in the Data Science market. Because of its customization, ability to handle big data, speed, development of machine learning, deep learning, and probabilistic models and model customization (research and development) make it has huge applications in the industries in the current world. Many industries looking for a Data Scientist with these skills. This course covers modelling techniques using TensorFlow 2.x.
We start with programming in TensorFlow 2.x which is the essential skill required and then we will do the necessary pre-processing to huge data.
Then throughout the course, we will work on building a custom regression model using a gradient descent algorithm in TensorFlow.
What you will Learn
· Python
· TensorFlow 2.x
· TensorBoard
· Dense Network
· Linear Regression
· Gradient Descent Algorithm
· Gradient Descent in TensorFlow 2.x
· Custom Model Training
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We know that TensorFlow is one of those topics that always leaves some doubts. You can feel free to ask a question in Q & A. I will answer your questions.
Until now.
This course is exactly what you need to comprehend once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes – everything is included.
Who this course is for:Anyone who are stating to learn deep learning