Sofia Kelly

Sofia Kelly

1553241075

First impressions of TensorFlow Dev Summit, 2019

The 2019 edition of the TensorFlow dev summit got off to a great start on a rather cold and rainy morning in Sunnyvale, CA. This time around, the TensorFlow team has made certain visible changes from previous year’s edition:

  1. Summit is now a 2-day event with the first day full of talks and demos whereas the second day focussing on hands-on sessions where attendees can code along with the TensorFlow team members and engage them on the real-life problems they are solving.
  2. The event has a much larger invitee list and this is visible from their venue (Google Event Center)
  3. Talks are more technical with a lot of code snippets and live demos using Google Colab.

Let’s go over the 15 key takeaways from a full day of talks and the hands-on sessions. To view all the talks, TensorFlow team has been fairly quick this time to put up the videos on their youtube channel.

15 Key Takeaways

  1. Summit is now a 2-day event with the first day full of talks and demos whereas the second day focussing on hands-on sessions where attendees can code along with the TensorFlow team members and engage them on the real-life problems they are solving.
  2. The event has a much larger invitee list and this is visible from their venue (Google Event Center)
  3. Talks are more technical with a lot of code snippets and live demos using Google Colab.

  1. TensorFlow in collaboration with Udacity and Coursera has launched two new courses.

  1. The announcement of TensorFlow World, a conference where engineers, innovators, executives, and product managers can come and discuss their product/service offering that has been powered by TensorFlow.

  1. One of my favorite contribution to the TensorFlow ecosystem is TensorFlow Extended (TFX) and the TFX team has certainly delivered what it promised in the 2018 edition. All the various components in TFX (DataValidator, Trainer, ModelValidator, Pusher) now work together for an end-to-end ML offering. Bonus: TFX now integrates with open source orchestrators such as AirFlow and KubeFlow.

  1. TensorFlow has stepped into the hardware space with the launch of the Raspberry pi style Coral DevBoard powered by the edge TPU ML accelerator. Priced at 150$, it is more expensive than a Raspberry Pi but cheaper than an Nvidia Jetson.

  1. TensorFlow lite team focussed their talk on speaking about their expanding list of use-cases, from Google assistant to YouDao’s on-device translation service. Keeping up with the usability theme of TensorFlow 2.0, TensorFlow lite has focussed on reducing the footprint of the models and making inference faster. Documentation for TensorFlow lite has also been improved.

  2. TensorFlow is now supported by the Julia programming language (tensorflow.jl). To give you a hint of why you might prefer Julia over Python for your next project, have a look at the code syntax and runtime for the below piece of code.

  1. Ever considered doing machine learning on decentralized data, checkout TensorFlow Federated.

  2. Sonnet, a high-level library built by DeepMind on top of TensorFlow, announced it’s support for TF2.0.

  3. Building models too big to fit on an off-the-shelf cloud instance? Need model parallelism? Checkout Mesh-TensorFlow.

There were a lot more updates and examples of how TensorFlow is being used in the industry and research. The above key takeaways capture just a glimpse of what was covered in the summit. To go over all the content, refer to TensorFlow’s YouTube channel. Consider applying to TensorFlow Dev Summit 2020 if you would like to meet some really smart people applying machine learning to real-life use-cases.

About the author: Gaurav is a data science manager at EY’s Innovation Advisory in Dublin, Ireland. His interests include building scalable machine learning systems for computer vision applications. Find more at gauravkaila.com

Suggest:

☞ Tools to Scale Your Production Machine Learning

☞ TensorFlow for JavaScript

☞ Machine Learning at Uber Natural Language Processing Use Cases

☞ Machine Learning Strategies for Time Series Forecasting

☞ What is Python and Why You Must Learn It in [2019]

☞ Recognizing Traffic Lights With Deep Learning

#tensorflow

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Buddha Community

First impressions of TensorFlow Dev Summit, 2019
Sofia Kelly

Sofia Kelly

1553241075

First impressions of TensorFlow Dev Summit, 2019

The 2019 edition of the TensorFlow dev summit got off to a great start on a rather cold and rainy morning in Sunnyvale, CA. This time around, the TensorFlow team has made certain visible changes from previous year’s edition:

  1. Summit is now a 2-day event with the first day full of talks and demos whereas the second day focussing on hands-on sessions where attendees can code along with the TensorFlow team members and engage them on the real-life problems they are solving.
  2. The event has a much larger invitee list and this is visible from their venue (Google Event Center)
  3. Talks are more technical with a lot of code snippets and live demos using Google Colab.

Let’s go over the 15 key takeaways from a full day of talks and the hands-on sessions. To view all the talks, TensorFlow team has been fairly quick this time to put up the videos on their youtube channel.

15 Key Takeaways

  1. Summit is now a 2-day event with the first day full of talks and demos whereas the second day focussing on hands-on sessions where attendees can code along with the TensorFlow team members and engage them on the real-life problems they are solving.
  2. The event has a much larger invitee list and this is visible from their venue (Google Event Center)
  3. Talks are more technical with a lot of code snippets and live demos using Google Colab.

  1. TensorFlow in collaboration with Udacity and Coursera has launched two new courses.

  1. The announcement of TensorFlow World, a conference where engineers, innovators, executives, and product managers can come and discuss their product/service offering that has been powered by TensorFlow.

  1. One of my favorite contribution to the TensorFlow ecosystem is TensorFlow Extended (TFX) and the TFX team has certainly delivered what it promised in the 2018 edition. All the various components in TFX (DataValidator, Trainer, ModelValidator, Pusher) now work together for an end-to-end ML offering. Bonus: TFX now integrates with open source orchestrators such as AirFlow and KubeFlow.

  1. TensorFlow has stepped into the hardware space with the launch of the Raspberry pi style Coral DevBoard powered by the edge TPU ML accelerator. Priced at 150$, it is more expensive than a Raspberry Pi but cheaper than an Nvidia Jetson.

  1. TensorFlow lite team focussed their talk on speaking about their expanding list of use-cases, from Google assistant to YouDao’s on-device translation service. Keeping up with the usability theme of TensorFlow 2.0, TensorFlow lite has focussed on reducing the footprint of the models and making inference faster. Documentation for TensorFlow lite has also been improved.

  2. TensorFlow is now supported by the Julia programming language (tensorflow.jl). To give you a hint of why you might prefer Julia over Python for your next project, have a look at the code syntax and runtime for the below piece of code.

  1. Ever considered doing machine learning on decentralized data, checkout TensorFlow Federated.

  2. Sonnet, a high-level library built by DeepMind on top of TensorFlow, announced it’s support for TF2.0.

  3. Building models too big to fit on an off-the-shelf cloud instance? Need model parallelism? Checkout Mesh-TensorFlow.

There were a lot more updates and examples of how TensorFlow is being used in the industry and research. The above key takeaways capture just a glimpse of what was covered in the summit. To go over all the content, refer to TensorFlow’s YouTube channel. Consider applying to TensorFlow Dev Summit 2020 if you would like to meet some really smart people applying machine learning to real-life use-cases.

About the author: Gaurav is a data science manager at EY’s Innovation Advisory in Dublin, Ireland. His interests include building scalable machine learning systems for computer vision applications. Find more at gauravkaila.com

Suggest:

☞ Tools to Scale Your Production Machine Learning

☞ TensorFlow for JavaScript

☞ Machine Learning at Uber Natural Language Processing Use Cases

☞ Machine Learning Strategies for Time Series Forecasting

☞ What is Python and Why You Must Learn It in [2019]

☞ Recognizing Traffic Lights With Deep Learning

#tensorflow

5 Steps to Passing the TensorFlow Developer Certificate

Deep Learning is one of the most in demand skills on the market and TensorFlow is the most popular DL Framework. One of the best ways in my opinion to show that you are comfortable with DL fundaments is taking this TensorFlow Developer Certificate. I completed mine last week and now I am giving tips to those who want to validate your DL skills and I hope you love Memes!

  1. Do the DeepLearning.AI TensorFlow Developer Professional Certificate Course on Coursera Laurence Moroney and by Andrew Ng.

2. Do the course questions in parallel in PyCharm.

#tensorflow #steps to passing the tensorflow developer certificate #tensorflow developer certificate #certificate #5 steps to passing the tensorflow developer certificate #passing

Dejah  Reinger

Dejah Reinger

1599921480

API-First, Mobile-First, Design-First... How Do I Know Where to Start?

Dear Frustrated,

I understand your frustration and I have some good news and bad news.

Bad News First (First joke!)
  • Stick around another 5-10 years and there will be plenty more firsts to add to your collection!
  • Definitions of these Firsts can vary from expert to expert.
  • You cannot just pick a single first and run with it. No first is an island. You will probably end up using a lot of these…

Good News

While there are a lot of different “first” methodologies out there, some are very similar and have just matured just as our technology stack has.

Here is the first stack I recommend looking at when you are starting a new project:

1. Design-First (Big Picture)

Know the high-level, big-picture view of what you are building. Define the problem you are solving and the requirements to solve it. Are you going to need a Mobile app? Website? Something else?

Have the foresight to realize that whatever you think you will need, it will change in the future. I am not saying design for every possible outcome but use wisdom and listen to your experts.

2. API First

API First means you think of APIs as being in the center of your little universe. APIs run the world and they are the core to every (well, almost every) technical product you put on a user’s phone, computer, watch, tv, etc. If you break this first, you will find yourself in a world of hurt.

Part of this First is the knowledge that you better focus on your API first, before you start looking at your web page, mobile app, etc. If you try to build your mobile app first and then go back and try to create an API that matches the particular needs of that one app, the above world of hurt applies.

Not only this but having a working API will make design/implementation of your mobile app or website MUCH easier!

Another important point to remember. There will most likely be another client that needs what this API is handing out so take that into consideration as well.

3. API Design First and Code-First

I’ve grouped these next two together. Now I know I am going to take a lot of flak for this but hear me out.

Code-First

I agree that you should always design your API first and not just dig into building it, However, code is a legitimate design tool, in the right hands. Not everyone wants to use some WYSIWYG tool that may or may not take add eons to your learning curve and timetable. Good Architects (and I mean GOOD!) can design out an API in a fraction of the time it takes to use some API design tools. I am NOT saying everyone should do this but don’t rule out Code-First because it has the word “Code” in it.

You have to know where to stop though.

Designing your API with code means you are doing design-only. You still have to work with the technical and non-technical members of your team to ensure that your API solves your business problem and is the best solution. If you can’t translate your code-design into some visual format that everyone can see and understand, DON’T use code.

#devops #integration #code first #design first #api first #api

Mckenzie  Osiki

Mckenzie Osiki

1623139838

Transfer Learning on Images with Tensorflow 2 – Predictive Hacks

In this tutorial, we will provide you an example of how you can build a powerful neural network model to classify images of **cats **and dogs using transfer learning by considering as base model a pre-trained model trained on ImageNet and then we will train additional new layers for our cats and dogs classification model.

The Data

We will work with a sample of 600 images from the Dogs vs Cats dataset, which was used for a 2013 Kaggle competition.

#python #transfer learning #tensorflow #images #transfer learning on images with tensorflow #tensorflow 2

TensorFlow Lite Object Detection using Raspberry Pi and Pi Camera

I have not created the Object Detection model, I have just merely cloned Google’s Tensor Flow Lite model and followed their Raspberry Pi Tutorial which they talked about in the Readme! You don’t need to use this article if you understand everything from the Readme. I merely talk about what I did!

Prerequisites:

  • I have used a Raspberry Pi 3 Model B and PI Camera Board (3D printed a case for camera board). **I had this connected before starting and did not include this in the 90 minutes **(plenty of YouTube videos showing how to do this depending on what Pi model you have. I used a video like this a while ago!)

  • I have used my Apple Macbook which is Linux at heart and so is the Raspberry Pi. By using Apple you don’t need to install any applications to interact with the Raspberry Pi, but on Windows you do (I will explain where to go in the article if you use windows)

#raspberry-pi #object-detection #raspberry-pi-camera #tensorflow-lite #tensorflow #tensorflow lite object detection using raspberry pi and pi camera