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Tensorflow on Google’s Cloud Platform for Data Engineers

The Fourth Course in a Series for Attaining the Google Certified Data Engineer
Instructor:
Mike West
497 students enrolled
English [Auto-generated]
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

1
Introduction

This course is about TensorFlow. 

This is Google's computational engine for machine learning. 

2
Exam Update

In this lecture I discuss created a machine learning course for data engineers. 

This course will be very targeted and specific to questions on the exam. 

3
Is this Course for You?

Is this course right for you? 

Let's find out in this lesson. 

4
Instructor Course Q&A

In this lesson I answer a few questions about the course and the exam. 

5
What's an Array?

Let's define what an array is. 

The very basics. 


6
What is a Multi-Dimensional Array or Tensor?

Tensors are multi-dimensional arrays. 

Let's find out what that is in this lesson. 

7
How Tensors Flow

Tensors and nodes together create a TensorFlow network. 

These networks are represented as graphs in TensorFlow. 

8
Real Numbers Flowing through our Graph

In this lesson let's add some real numbers to our graph. 

This will help show how tensor move through the graph with data in them. 

9
Hello World in TensorFlow

Every programming language has hello world and TensorFlow is no different. 

Let's walk through the most basic TensorFlow example we can. 

10
Course Downloads
11
Summary
12
Quiz

Up and Running in Cloud Datalab

1
Creating Jupyter Notebooks on GCP

Let's learn how to create a datalab vm for the course. 


2
Reconnect to Datalab Virtual Machine

Once we close our datalab VM we will need to be able to log back into it in order to build our models. 

Let's see how simple it is to call our existing datalab notebook from cloud shell. 

3
Download/Upload Notebooks to Datalab

In this lecture let's learn how to upload and download our cloud datalab notebooks. 

4
Lab: Up and Running with Datalab

In this lesson let's set up a virtual machine that we will use throughout the course to host our datalab notebooks. 

5
Summary
6
Quiz

TensorFlow Basics

1
The TensorFlow Code Base

Let's learn about the TensorFlow toolkit in this lesson. 

2
Forward Feeding Graphs

All TensorFlow graphs feed in one direction and that is forward through the graph. 

Let's learn how this operates in this lesson. 

3
Handling Iteration in TensorFlow Graphs

Machine learning involves lots of iteration but TensorFlow doesn't. 

Let's learn how Tensorflow handles that in this lesson. 

4
2 Steps in Every TensorFlow Program

Every TensorFlow program has two steps. 

In this lesson let's learn what they are. 

5
Modeling Larger Computational Graphs

Real world machine learning is machine learning at scale. 

Let's learn how TensorFlow handles that in this lesson. 

6
Resizing After High Utilization Warning

In this lesson let's add more resources to our datalab vm. 

7
Simple End to End Example

In this example let's walk through a very simple end to end model in TensorFlow. 

8
Tensor Dimensions

Let's learn about the dimension of Tensors in this lesson. 

9
Placeholders

In this lesson let's learn what placeholders are and how to use them. 

10
Session Parameters: Fetch and Feed_Dict

In this lesson let's learn how to pass two core parameters into our session. 

11
Node Life Cycle

Let's learn what a node's life cycle in this brief lesson. 

12
Tensor Properties

A tensor have three properties. 

Let's learn what they are in this lesson. 

13
Convert to Tensors

Let's convert our non-tensored arrays to tensors. 


14
Enabling Logging with TensorFlow

In this lesson let's learn how to enable logging on our TensorFlow models. 

15
Lab: Hello World in TensorFlow

In this lesson let's work through the most simple TensorFlow example we can. 

16
Summary
17
Quiz

TensorFlow Demos

1
Numpy Vs TensorFlow

In this lesson let's view the difference between executing a numpy array in Python and a tensor in TensorFlow. 

We will do all of this in our datalab notebooks. 

2
Dataset Creation and Exploration

We are going to do this in our datalab notebooks but our data will be in BigQuery. 

3
Data Wrangling

Let's massage our data and create clean datasets for our machine learning models. 

4
Linear Regression in TensorFlow

In this lesson let'walk through a linear regression model in TensorFlow. 

5
The Mandelbrot Set

A strange example from Google but it's been on a few exams so let's cover it here. 

6
Overfitting and How to Correct it

In this lesson let's learn about overfitting and how to avoid it. 

7
Using Cloud Machine Learning

Real world machine learning is all about scale. 

In this lesson let's learn about scale on GCP. 

8
Model Packaging

Once we have our model it's time to pack it up. 

Let's learn how to package a completed model in this lesson. 

9
Creating a Server Input Function

In this lesson let's learn the basics of creating a prediction service. 

10
Lab: Linear Regression in TensorFlow

In this lesson let's complete a lab specific to linear regression in TensorFlow. 

11
Lab Review: Linear Regression

Let's walk through the lab step by step. 

12
Summary
13
Section Quiz
14
Sample Exam Questions
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