TensorFlow是一个端到端的开源机器学习/深度学习平台。它具有一个由库，工具和社区资源组成的综合生态系统，可让AI / ML工程师，科学家，分析人员构建和部署基于ML的深度学习应用程序。TensorFlow的名称源自神经网络对多维数据数组或张量执行的操作。深度学习是机器学习的一个子领域，它是一组受大脑结构和功能启发的算法。
TensorFlow是Google创建并用于设计，构建和训练深度学习模型的机器学习框架。您可以使用TensorFlow库来进行数值计算，这本身似乎并不太特殊，但是这些计算是通过数据流图完成的。在这些图中，节点表示数学运算，而边沿表示数据，通常是多维数据数组或张量，在这些边沿之间进行通信。
简而言之，TensorFlow是一个开源且最受欢迎的深度学习库，用于研究和生产。Python中的TensorFlow是一个符号数学库，它使用数据流和可微分的编程来执行专注于深度神经网络的训练和推理的各种任务。TensorFlow设法将一套全面而灵活的技术功能结合在一起，并且非常易于使用。
近年来，在人工智能世界中出现了非凡的发展，从广为人知的无人驾驶汽车发展到现在的模仿机器或真正擅长视频游戏的机器。这些进步的核心是围绕着多种工具来帮助推导深度学习和其他机器学习模型，其中包括Torch，Caffe和Theano。但是，自Google Brain于2015年11月通过其自己的框架TensorFlow开源以来，该软件库的普及率迅速飙升，成为最受欢迎的深度学习框架。
TensorFlow使您能够构建数据流图和结构，以将输入作为称为Tensor的多维数组来定义数据在图中的移动方式。它使您可以构造可以在这些输入上执行的操作流程图，该流程图一端作为输出，另一端作为输出。
Google，IBM，Netflix，迪士尼，Twitter，Micron等顶级组织都使用TensorFlow。
Uplatz在TensorFlow上提供了这一广泛的课程。这个TensorFlow课程涵盖TensorFlow基础知识，组件，通往高级主题的管道，例如线性回归，分类器，创建，训练和评估神经网络（如CNN，RNN，自动编码器等）以及TensorFlow示例。
TensorFlow培训的设计方式使您能够轻松，高效地在TensorFlow上实施深度学习项目。在本TensorFlow课程中，您将学习神经网络的基础知识以及如何使用TensorFlow构建深度学习模型。TensorFlow培训为软件工程师提供了一种实用的深度学习方法。您将获得动手实践，以构建自己的最先进的图像分类器和其他深度学习模型。您还将在现实世界中的移动设备，云和浏览器中使用TensorFlow模型。最后，您将使用高级技术和算法来处理大型数据集。您将获得开始创建自己的AI应用程序和模型所需的技能。
您将使用TensorFlow框架掌握深度学习的概念和模型，并实现深度学习算法，为您成为深度学习工程师的职业做好准备。了解如何使用TensorFlow构建神经网络以及如何对其进行训练，评估和优化。
TensorFlow完全基于Python。本课程还对Python编程概念，NumPy，Matplotlib和Pandas进行了很好的介绍，因此您可以在继续学习TensorFlow概念之前，通过本课程本身掌握这些技能。本TensorFlow教程的目的是描述所有TensorFlow对象和方法。
此TensorFlow课程还包括TensorBoard可视化工具的全面描述。在通过简单的神经网络实现将其用于深度学习之前，通过使用它来解决一般的数值问题（这超出了机器学习通常涉及的范围），您将了解此工具的机制。
TensorFlow架构
TensorFlow架构分为三个部分：
预处理数据
建立模型
训练和估计模型
之所以称为TensorFlow，是因为它以多维数组（也称为张量）的形式接受输入。您可以构造要在该输入上执行的某种操作流程图（称为图形）。输入从一端进入，然后流经此系统的多个操作，然后从另一端出来作为输出。
Duration: 29h 18m | Video: .MP4 1280×720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | Size: 11.2 GB
Genre: eLearning | Language: English
TensorFlow concepts, components, pipeline, ANN, Classification, Regression, Object Identification, CNN, RNN, TensorBoard
What you’ll learn
End-to-end knowledge of TensorFlow
TensorFlow concepts, development, coding, applications
TensorFlow components & pipelines
TensorFlow examples
Introduction to Python, Linear Algebra, Matplotlib, NumPy, Pandas
Introduction to Files
Introduction to Machine Learning
TensorFlow Playground & Perceptrons
TensorFlow and Artificial Intelligence
Building Artificial Neural Networks (ANN) with TensorFlow
Types of ANN and Components of Neural Networks
TensorFlow Classification and Linear Regression
TensorFlow vs. PyTorch vs. Theano vs. Keras
Object Identification in TensorFlow
TensorFlow Superkeyword
CNN & RNN, RNN Time Series
TensorBoard – TensorFlow’s visualization toolkit
Requirements
Enthusiasm and determination to make your mark on the world!
Description
TensorFlow is an end-to-end open-source machine learning / deep learning platform. It has a comprehensive ecosystem of libraries, tools, and community resources that lets AI/ML engineers, scientists, analysts build and deploy ML-powered deep learning applications. The name TensorFlow is derived from the operations which neural networks perform on multidimensional data arrays or tensors. Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain.
TensorFlow is a machine learning framework that Google created and used to design, build, and train deep learning models. You can use the TensorFlow library do to numerical computations, which in itself doesn’t seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges.
In simple words, TensorFlow is an open-source and most popular deep learning library for research and production. TensorFlow in Python is a symbolic math library that uses dataflow and differentiable programming to perform various tasks focused on training and inference of deep neural networks. TensorFlow manages to combine a comprehensive and flexible set of technical features with great ease of use.
There have been some remarkable developments lately in the world of artificial intelligence, from much publicized progress with self-driving cars to machines now composing imitations or being really good at video games. Central to these advances are a number of tools around to help derive deep learning and other machine learning models, with Torch, Caffe, and Theano amongst those at the fore. However, since Google Brain went open source in November 2015 with their own framework, TensorFlow, the popularity of this software library has skyrocketed to be the most popular deep learning framework.
TensorFlow enables you to build dataflow graphs and structures to define how data moves through a graph by taking inputs as a multi-dimensional array called Tensor. It allows you to construct a flowchart of operations that can be performed on these inputs, which goes at one end and comes at the other end as output.
Top organizations such as Google, IBM, Netflix, Disney, Twitter, Micron, all use TensorFlow.
Uplatz provides this extensive course on TensorFlow. This TensorFlow course covers TensorFlow basics, components, pipelines to advanced topics like linear regression, classifier, create, train and evaluate a neural network like CNN, RNN, auto encoders etc. with TensorFlow examples.
The TensorFlow training is designed in such a way that you’ll be able to easily implement deep learning project on TensorFlow in an easy and efficient way. In this TensorFlow course you will learn the fundamentals of neural networks and how to build deep learning models using TensorFlow. This TensorFlow training provides a practical approach to deep learning for software engineers. You’ll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models. You’ll also use your TensorFlow models in the real world on mobile devices, in the cloud, and in browsers. Finally, you’ll use advanced techniques and algorithms to work with large datasets. You will acquire skills necessary to start creating your own AI applications and models.
You’ll master deep learning concepts and models using TensorFlow frameworks and implement deep learning algorithms, preparing you for a career as Deep Learning Engineer. Learn how to build a neural network and how to train, evaluate and optimize it with TensorFlow.
TensorFlow is completely based on Python. This course also provides a sound introduction to Python programming concepts, NumPy, Matplotlib, and Pandas so that you can acquire those skills in this course itself before moving on to learn the TensorFlow concepts. The aim of this TensorFlow tutorial is to describe all TensorFlow objects and method.
This TensorFlow course also includes a comprehensive description of TensorBoard visualization tool. You will gain an understanding of the mechanics of this tool by using it to solve a general numerical problem, quite outside of what machine learning usually involves, before introducing its uses in deep learning with a simple neural network implementation.
TensorFlow Architecture
TensorFlow architecture works in three parts:
Preprocessing the data
Build the model
Train and estimate the model
It is called TensorFlow because it takes input as a multi-dimensional array, also known as tensors. You can construct a sort of flowchart of operations (called a Graph) that you want to perform on that input. The input goes in at one end, and then it flows through this system of multiple operations and comes out the other end as output.
This is why it is called TensorFlow because the tensor goes in it flows through a list of operations, and then it comes out the other side.
TensorFlow – Course Syllabus
Who this course is for:
Machine Learning & Deep Learning Engineers
Data Scientists & Senior Data Scientists
Beginners and newbies aspiring for a career in Machine Learning / Deep Learning
Data Analysts & Advanced Data Analytics Professionals
TensorFlow Engineers
Machine Learning Developers – TensorFlow/Hadoop
Software Developers – AI/ML/Deep Learning
Anyone wishing to learn TensorFlow algorithms and applications
Deep Learning Engineers – Python/TensorFlow
Artificial Intelligence Engineers and Senior ML/DL Engineers
Researchers and PhD students
Data Engineers
AI & RPA Developers – TensorFlow/ML
AI/ML Developers
Machine Learning Leads & Enthusiasts
TensorFlow and Advanced ML Developers
