4.25 (44 reviews)
☑ You'll understand the basics of TensorFlow.
☑ You'll be able to build TensorFlow models on Google's Cloud.
☑ You'll be prepared for TensorFlow questions on the Google Certified Data Engineering Exam.
☑ Upon completion you'll know how to build machine learning models inside Google's Cloud.
Welcome to Tensorflow on the Google Cloud Platform for Data Engineers This is the fourth course in a series of courses designed to help you attain the coveted Google Certified Data Engineer.
Additionally, the series of courses is going to show you the role of the data engineer on the Google Cloud Platform.
NOTE: This is not a course on how to develop machine learning models with TensorFlow. This is a very targeted course on TensorFlow for data engineers. My goal is to give data engineers what they need to know for the exam and provide learners with the foundations of TensorFlow on Google's Cloud Platform.
At this juncture the Google Certified Data Engineer is the only real world certification for data and machine learning engineers.
TensorFlow is an open source software library created by Goggle for doing graph-based computations quickly. It does this by utilizing the GPU(Graphics Processing Unit) and also making it easy to distribute the work across multiple GPUs and computers.
Tensors, in general, are simply arrays of numbers, or functions, that transform according to certain rules under a change of conditions. Nodes in the graphs represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them.
In the course you'll discover how to apply TensorFlow to machine learning, the concept of a Tensor, the anatomy of a simple program, basic constructs such as constants, variables, placeholders, sessions and the computation graph.
You'll work with basic math operations and image transformations to see how common computations are performed.
You'll learn TensorFlow within the context of the Google Cloud Platform.
*Five Reasons to take this Course.*
1) You Want to be a Data Engineer
It's the number one job in the world. (not just within the computer space) The growth potential career wise is second to none. You want the freedom to move anywhere you'd like. You want to be compensated for your efforts. You want to be able to work remotely. The list of benefits goes on.
2) The Google Certified Data Engineer
Google is always ahead of the game. If you were to look back at a timeline of their accomplishments in the data space you might believe they have a crystal ball. They've been a decade ahead of everyone. Now, they are the first and the only cloud vendor to have a data engineering certification. With their track record I'll go with Google.
3) The Growth of Data is Insane
Ninety percent of all the world's data has been created in the last two years. Business around the world generate approximately 450 billions transactions a day. The amount of data collected by all organizations is approximately 2.5 Exabytes a day. That number doubles every month.
4) TensorFlow in Plain English
TensorFlow is a low level language. The basic concept of a tenor is hard to grasp if you aren't familiar with neural networks. In the course we will break down TensorFlow in to bite sized pieces ensuring you learn the fundamentals first. After we've built a base understanding of tensors and how they flow we will move on to more complicated examples.
5) You want to be ahead of the Curve
The data engineer role is fairly new. While your learning, building your skills and becoming certified you are also the first to be part of this burgeoning field. You know that the first to be certified means the first to be hired and first to receive the top compensation package.
Thank you for your interest in Tensorflow on the Google Cloud Platform for Data Engineers and we will see you in the course!!
Welcome to TensorFlow
Is this Course for You?
Instructor Course Q&A
What's an Array?
What is a Multi-Dimensional Array or Tensor?
How Tensors Flow
Real Numbers Flowing through our Graph
Hello World in TensorFlow
Up and Running in Cloud Datalab
Creating Jupyter Notebooks on GCP
Reconnect to Datalab Virtual Machine
Download/Upload Notebooks to Datalab
Lab: Up and Running with Datalab
The TensorFlow Code Base
Forward Feeding Graphs
Handling Iteration in TensorFlow Graphs
2 Steps in Every TensorFlow Program
Modeling Larger Computational Graphs
Resizing After High Utilization Warning
Simple End to End Example
Session Parameters: Fetch and Feed_Dict
Node Life Cycle
Convert to Tensors
Enabling Logging with TensorFlow
Lab: Hello World in TensorFlow
Numpy Vs TensorFlow
Dataset Creation and Exploration
Linear Regression in TensorFlow
The Mandelbrot Set
Overfitting and How to Correct it
Using Cloud Machine Learning
Creating a Server Input Function
Lab: Linear Regression in TensorFlow
Lab Review: Linear Regression
Sample Exam Questions
Good course for learning what Tensorflow is and its concepts and general applications and concepts. I wish the course was a better introduction into practically learning to generate useful tensorflow.
Excellent details and examples. The Tensorflow mappings showing how the tensors flow the data to the nodes for computation was very helpful.