As a follow-up to my book review of Rise of the Robots by Martin Ford, I’m not going to waste any time reiterating my call to action:
Students, Software Developers, and DevOps Engineers should check out the fantastic work going on in the world of AI and machine learning.
And here’s why…
Because what you’ll discover about Deep Learning Framework is mind-blowing!
In fact, I’m more charged-up about deep learning than ever before because machine learning technologies are trending and need smart people to build and support them. [Hint! Hint!]
And that’s why in this Introduction To Deep Learning Guide, we’re going to show beginners how you can get started with Google’s free deep learning software, i.e., TensorFlow Tools.
Are you or someone you know tired of the same old job? Then this NEW opportunity is for you!
What to expect in this guide:
Before we’re done, I’m going to equip you with TensorFlow learning information, real examples of deep learning, and resources to start training that’ll level-up your Dev skills.
7 key takeaways you’re going to discover in this introduction to deep learning.
- Who needs to take this opportunity seriously and why you should not waste any more time.
- Everyday examples where learning algorithms are used that you may not know about.
- The growing machine learning job market with a list of jobs currently available, and an example of a fantastic opportunity.
- Three deep learning framework tools for you to examine as you research for yourself.
- Where to download TensorFlow to get started on your journey in machine learning.
- Where to quickly get TensorFlow training to help ramp-up your TensorFlow coding skills in no time.
- Machine learning and TensorFlow Books for Beginners.
Make sure you take good notes and bookmark this guide for later…
Before we begin, picture this:
The other day I was going through my daily routine of reading tech news, and I stumbled upon an article on The Verge that fired up my interest again in machine learning, Google’s latest platform play is artificial intelligence, and it’s already winning.
Boom, my head exploded!
I think it’s awesome Google provided free deep learning software tools for anyone who wants to dive into learning AI coding!
But what’s more important than free software is these are the same tools Google’s been using for a while. And according to The Verge – since 2015 these tools have been available in open-source. Hmm… 2015 was when Martin Ford releases his book – Rise of the Robots!
Let’s get down to business…
Understanding Machine Learning
#1 – Why you should take this TensorFlow learning opportunity serious.
Keynote (TensorFlow Dev Summit 2017)
Is this opportunity for you?
Well, that depends on whether you’re a hustler.
What I mean is will you take action and get going or will you keep analyzing and reviewing for 10 more days, or 10 more months, before you finally make up your mind to give it a try!
I’ve been around Tech for decades, and I’ve watched what happens during the software development lifecycle.
Do you know most developers exist spending 8 – 10 hours a day writing pieces of code alone in a 6’ x 6’ cubicle? There’s no romance in that…
And I’ll be 100% honest with you; I’m not going to try to convince anyone to take action if they’re not ready.
Yup, from here forward I’m only talking to those readers ready to take immediate action and do something productive with this information.
In a minute I’ll share the link to where you can download TensorFlow, read the documentation, watch tutorials, etc.
And for the Docker crowd, I’m also going to share the link so you can pull down a Docker version of TensorFlow to run it in containers.
Then you’ll get a link to where you can get your first lessons on developing with TensorFlow. That’s correct; I’ll share where you can get online TesnorFlow training.
If you’re hungry for excitement and want an exciting career, follow me to takeaway #2 because we’re about to get real serious about AI…
[ Image Source ]
Here’s a link to the associated Stanford lecture: How to manage your experiments in TensorFlow.
#2 – Everyday deep learning example where algorithms are used.
Google examples of machine learning algorithms.
- Google Search – That’s no surprise, right?
- Google Translate – If my understanding is correct, TensorFlow was used to create the algorithms that can read and translate languages on Google Translate.
- Google Maps – And deep learning image recognition can scan a photo of a landmark like the Statue of Liberty and then using Google Maps give you the GPS coordinates.
Now that’s powerful coding!
I never connected these dots before, but if you look around there are more examples of deep learning frameworks all around us: face recognition at airports, traffic management in your city, and crunching data for elections…
My ears are ringing from imagining all the cool services yet to be created that will use TensorFlow coupled with other machine learning tools.
Hang on because in takeaway #3 you’re going to see why you don’t want to wait another day to start developing your coding skills with deep learning frame work, the tools are available now for you to dive right in and be at the forefront of AI technology advances.
Watch this machine learning tutorial…
Deep Learning Tutorial
Deep learning software is amazing…
I know developing code using deep learning software or coding machine learning algorithms isn’t for everyone, but look – if you’re just beginning your studies as a college student and still unsure what direction to go, or you’re looking for a new career to transition to, then I recommend you think seriously about developing your coding skills using TensorFlow and other similar coding tools.
As a software developer or student, understanding how deep learning frameworks are used should be a key focus for you.
If you’re already going to spend your time and money taking coding classes then shouldn’t you learn skills that move you towards a future career by learning to code algorithms?
And just to be consistent with my views on learning, don’t just settle for the basics, become the best in your class and master multiple deep learning frameworks, so you become indispensable.
#3 – The growing machine learning job market.
Martin Ford opened my mind to the future, but Google is opening opportunities and giving anyone the tools which will put in the time and effort to learn TensorFlow and TensorFlow lite.
Let’s take a peek at the job market for developers with TensorFlow coding skills.
Just entering “TensorFlow” in the search box, page one of 14 has jobs for:
- Search Engineer
- Data Scientist Senior – Machine Vision
- Data Scientist
- Software Engineer – Deep Learning/Machine Learning
- Data Scientist
- Machine Learning Software Engineer
- Software Engineer, Ad Ranking Platform
- Deep Learning Engineer
- Senior Manager, Generative Dialogue
- Machine Learning Scientist
- Data Science Intern
- Research Analyst
- Autopilot Internship (Fall 2017)
- Machine Learning Applied Research Engineer – Intern
- Deep Learning Engineer
- Director, Deep Learning
- Computer Vision Staff Engineer
- Senior Deep Learning Engineer
- Director, Natural Language Processing
- Senior Deep Learning Engineer
The job that caught my attention was the “Autopilot Internship” so I decided to click the link and OMG!
Look where it is – Tesla Motors…
I told you takeaway #3 would amaze you, and #4 is good too!
Do you see now why I’m challenging you to take action? This job at Tesla could be yours if you were ready! Tesla Motors, not Gotcha Garage! 🙂
My guess is this internship job at Tesla Motors will go to a smart person in their twenties who is a hustler and paid the price to skill-up…
#4 – Deep Learning Framework Tools for you to examine.
Here’s a list of 3 deep learning framework tools that you can add to your toolchain.
- The Verge article talks about a collaboration between Google and Keras, which is another Deep Learning library for TensorFlow (Keras home | Keras Docker hub | Keras GitHub).
- Then there is Theano which is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays. I can see how this would be important in an algorithm (Theano home | Theano Docker hub | Theano Github).
- Caffe is a deep learning framework made to handle expression, speed, and more (Caffe home | Caffe Docker hub | Caffe Github).
Add these alternatives to TensorFlow machine learning tools to your plan.
#5 – TensorFlow tools.
What you’ll find below are links to TensorFlow download resources…
TensorFlow Open-Source Software Library – The TensorFlow Open-Source Software Library is loaded with machine learning library documentation, examples, and tutorials to get you started.
Download TenorFlow tools & TensorFlow Lite and begin discovering.
TensorFlow Public Repository on Docker – If you’re up-to-it and have the grit, the TensorFlow Public Repository on Docker is where you can pull down TensorFlow and install it using Docker.
Here’s the official TensorFlow page on Docker with step-by-step instructions to pull down the image and get you started.
TensorFlow Open-Source Software Library on Github – tensoflow/tensoflow.
The TensorFlow website is full of resources that will help you develop your knowledge and skills; deep learning library, machine learning tutorials, and machine learning basics are only a few topics you’ll find there.
Before you start downloading software or spinning up containers, lets finish takeaway #6 and #7…
#6 – Where to get TensorFlow training?
After I read the Verge article about machine learning, I couldn’t get the idea out of my head about all the opportunities just sitting there waiting for people to develop them.
And now after reading a job listing, what would be more exciting than working for Elon Musk at Tesla Motors and helping innovate autopilot for auto-driving cars, or possibly, SpaceX?
Are you feeling my excitement? Boom, my head exploded again…
I wanted to find out what it takes to learn TensorFlow, so I jumped on Chrome and went to my favorite online learning website, you guessed it – Udemy.
Within minutes I added 3 TensorFlow training courses to my cart.
- Machine Learning with TensorFlow
- Building Machine Learning Systems with TensorFlow
- Learning Path: TensorFlow: The Road to TensorFlow-2nd Edition
Each training course has several sample videos to watch, click here to view these courses yourself…
I’m sure there are a lot of places where you can find deep learning framework training, but for me, I prefer Udemy because most of the courses are created by people with hands-on experience vs professional trainers with little or no practical experience.
Another new resource for big data and deep learning is Woz U Academy.
#7 – Machine Learning & TensorFlow Books for Beginners.
First off, what caught my attention while reviewing all the machine learning book reviews on Amazon is there aren’t many books about TensorFlow. Hmm…
And second, I also noticed most of the books didn’t have very many reviews.
And then it hit me!
These are good indicators of an emerging technology that hasn’t been saturated yet! Hence, it supports my call to action…
The best book on tensors appears to be:
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron. Click here to view.
Next on the book list:
TensorFlow For Machine Intelligence: A Hands-on Introduction to Learning Algorithms by Sam Abrahams, Danijar Hafner, Erik Erwitt & Ariel Scarpinelli. Click here to view.
Amazon has more machine learning books for beginners, but these TensorFlow books seem to be the best books on machine learning with the most active reviews.
Go ahead and take a few minutes to review all the deep learning software books available…
Turning The Takeaways Into An Action Plan
In these 7 takeaways we’ve reviewed machine learning to give you a basic understanding of machine learning and why now is a great time to create an action plan for your career as a deep learning framework developer, specifically focusing on Google TensorFlow tools.
If you still lack motivation, go ahead and take another look at the huge opportunity I posted above for an “Autopilot Internship” with Tesla Motors.
Then we identified who should be excited about deep learning framework opportunities: Students, Software Developers, and DevOps Engineers. And why now is the perfect time to take action and ramp-up your Dev skills while the AI job market is trending upwards.
And finally for beginners, we reviewed open source machine learning tools that are available for immediate download, as-well-as several TensorFlow training courses, and then we wrapped up with the best books on machine learning.
Now it’s up to you to take this guide and create your action plan, especially if you’re stuck in a career rut…
What’s Next for Google TensorFlow?
I have a feeling we’ve only seen the beginning of Google TensorFlow and the cool stuff is yet to come that will soon emerge on Android and across every technology that uses deep learning…Maybe someone who reads this TensorFlow for beginner’s guide will create the next big AI app?
We can all wait and see what deep learning algorithms Google, Tesla, Facebook and Amazon come up with, or we can decide to join the journey.
Now do it!
Take action now and start learning TensorFlow tools.
Please share this guide and don’t forget to bookmark this page so you can read it again later…Thanks!