Edward Jackson

Edward Jackson

1566748676

TensorFlow is dead, long live TensorFlow!

TensorFlow is ann open source machine learning library for research and production. TensorFlow is dead, long live TensorFlow

If you’re an AI enthusiast and you didn’t see the big news this month, you might have just snoozed through an off-the-charts earthquake. Everything is about to change!

Last year I wrote 9 Things You Need To Know About TensorFlow… but there’s one thing you need to know above all others: TensorFlow 2.0 is here!

The revolution is here! Welcome to TensorFlow 2.0.
It’s a radical makeover. The consequences of what just happened are going to have major ripple effects on every industry, just you wait. If you’re a TF beginner in mid-2019, you’re extra lucky because you picked the best possible time to enter AI (though you might want to start from scratch if your old tutorials have the word “session” in them).

In a nutshell:TensorFlow has just gone full Keras. Those of you who know those words just fell out of your chairs. Boom!

A prickly experience

I doubt that many people have accused TensorFlow 1.x of being easy to love. It’s the industrial lathe of AI… and about as user-friendly. At best, you might feel grateful for being able to accomplish your AI mission at mind-boggling scale.

You’d also attract some raised eyebrows if you claimed that TensorFlow 1.x was easy to get the hang of. Its steep learning curve made it mostly inaccessible to the casual user, but mastering it meant you could talk about it the way you’d brag about that toe you lost while climbing Everest. Was it fun? No, c’mon, really: was it fun?

You‘re not the only one — it’s what TensorFlow 1.x tutorials used to feel like for everybody.

TensorFlow’s core strength is performance. It was built for taking models from research to production at massive scale and it delivers, but TF 1.x made you sweat for it. Persevere and you’d be able to join the ranks of ML practitioners who use it for incredible things, like finding new planets and pioneering medicine.

What a pity that such a powerful tool was in the hands of so few… until now.

Don’t worry about what tensors are. We

just called them (generalized) matrices where I grew up. The name

TensorFlow is a nod to the fact that TF’s very good at performing

distributed computations involving multidimensional arrays (er,

matrices), which you’ll find handy for

AI](http://bit.ly/quaesita_emperor) “http://bit.ly/quaesita_emperor)”) at scale.

Image source.](http://karlstratos.com/drawings/drawings.html). “http://karlstratos.com/drawings/drawings.html).”)

Cute and cuddly Keras

Now that we’ve covered cactuses, let’s talk about something you’d actually want to hug. Overheard at my place of work: “I think I have an actual crush on Keras.”

Keras is a specification for building models layer-by-layer that works with multiple machine learning frameworks (so it’s not a TF thing), but you might know it as a high level API accessed from within TensorFlow as tf.keras.

Incidentally, I’m writing this section on Keras’ 4th birthday (Mar 27, 2019) for an extra dose of warm fuzzies.

Keras was built from the ground up to be Pythonic and always put people first — it was designed to be inviting, flexible, and simple to learn.

Why don’t we have both?

Why must we choose between Keras’s cuddliness and traditional TensorFlow’s mighty performance? What don’t we have both?

Great idea! Let’s have both! That’s TensorFlow 2.0 in a nutshell.

This is TensorFlow 2.0. You can mash those orange buttons yourself here.](http://bit.ly/tfoview). “http://bit.ly/tfoview).”)

The revolution is here! Welcome to TensorFlow 2.0.### The usability revolution

Going forward, Keras will be the high level API for TensorFlow and it’s extended so that you can use all the advanced features of TensorFlow directly from tf.keras.

The revolution is here! Welcome to TensorFlow 2.0.

In the new version, everything you’ve hated most about TensorFlow 1.x gets the guillotine. Having to perform a dark ritual just to add two numbers together? Dead. TensorFlow Sessions? Dead. A million ways to do the exact same thing? Dead. Rewriting code if you switch hardware or scale? Dead. Reams of boilerplate to write? Dead. Horrible unactionable error messages? Dead. Steep learning curve? Dead.

The revolution is here! Welcome to TensorFlow 2.0.
You’re expecting the obvious catch, aren’t you? Worse performance? Guess again! We’re not giving up performance.

TensorFlow is now cuddly and this is a game-changer, because it means that one of the most potent tools of our time just dropped the bulk of its barriers to entry. Tech enthusiasts from all walks of life are finally empowered to join in because the new version opens access beyond researchers and other highly-motivated folks with an impressive pain threshold.

The revolution is here! Welcome to TensorFlow 2.0.
Everyone is welcome. Want to play? Then come play!

Eager to please

In TensorFlow 2.0, eager execution is now the default. You can take advantage of graphs even in eager context, which makes your debugging and prototyping easy, while the TensorFlow runtime takes care of performance and scaling under the hood.

Wrangling graphs in TensorFlow 1.x (declarative programming) was disorienting for many, but it’s all just a bad dream now with eager execution (imperative programming). If you skipped learning it before, so much the better. TF 2.0 is a fresh start for everyone.

As easy as one… one… one…

Many APIs got consolidated across TensorFlow under Keras, so now it’s easier to know what you should use when. For example, now you only need to work with one set of optimizers and one set of metrics. How many sets of layers? You guessed it! One! Keras-style, naturally.

In fact, the whole ecosystem of tools got a spring cleaning, from data processing pipelines to easy model exporting to TensorBoard integration with Keras, which is now a… one-liner!

There are also great tools that let you switch and optimize distribution strategies for amazing scaling efficiency without losing any of the convenience of Keras.

Those distribution strategies are pretty, aren’t they?

The catch!

If the catch isn’t performance, what is it? There has to be a catch, right?

Actually, the catch was your suffering up to now. TensorFlow demanded quite a lot of patience from its users while a friendly version was brewing. This wasn’t a matter of sadism. Making tools for deep learning is new territory, and we’re all charting it as we go along. Wrong turns were inevitable, but we learned a lot along the way.

The revolution is here! Welcome to TensorFlow 2.0.
The TensorFlow community put in a lot of elbow grease to make the initial magic happen, and then more effort again to polish the best gems while scraping out less fortunate designs. The plan was never to force you to use a rough draft forever, but perhaps you habituated so well to the discomfort that you didn’t realize it was temporary. Thank you for your patience!
The revolution is here! Welcome to TensorFlow 2.0.
The reward is everything you appreciate about TensorFlow 1.x made friendly under a consistent API with tons of duplicate functionality removed so it’s cleaner to use. Even the errors are cleaned up to be concise, simple to understand, and actionable. Mighty performance stays!

What’s the big deal?

Haters (who’re gonna hate) might say that much of v2.0 could be cobbled together in v1.x if you searched hard enough, so what’s all the fuss about? Well, not everyone wants to spend our days digging around in clutter for buried treasure. The makeover and clean-up are worth a standing ovation. But that’s not the biggest big deal.

The point not to miss is this: TensorFlow just announced an uncompromising focus on usability.

The revolution is here! Welcome to TensorFlow 2.0.
AI lets you automate tasks you can’t come up with instructions for. It lets you automate the ineffable. Democratization means that AI at scale will no longer be the province of a tiny tech elite.
The revolution is here! Welcome to TensorFlow 2.0.
Imagine a future where “I know how to make things with Python and “I know how to make things with AI are equally commonplace statements… Exactly! I’m almost tempted to use that buzzword “disruptive” here.

The great migration

We know it’s hard work to upgrade to a new version, especially when the changes are so dramatic. If you’re about to embark on migrating your codebase to 2.0, you’re not alone — we’ll be doing the same here at Google with one of the largest codebases in the world. As we go along, we’ll be sharing migration guides to help you out.

The revolution is here! Welcome to TensorFlow 2.0.
If you rely on specific functionality, you won’t be left in the lurch — except for contrib, all TF 1.x functions will live on in the compat.v1 compatibility module. We’re also giving you a script which automatically updates your code so it runs on TensorFlow 2.0. Learn more in the video below.

This video’s is a great resource if you’re eager to dig deeper into TF 2.0 and geek out on code snippets.

Your clean slate

TF 2.0 is a beginner’s paradise, so it will be a downer for those who’ve been looking forward to watching newbies suffer the way you once suffered. If you were hoping to use TensorFlow for hazing new recruits, you might need to search for some other way to inflict existential horror.

The revolution is here! Welcome to TensorFlow 2.0.
Sitting out might have been the smartest move, because now’s the best time to arrive on the scene. As of March 2019, TensorFlow 2.0 is available in alpha (that’s a preview, you hipster you), so learning it now gets you ready in time for the full release that the community is gearing up for over the next quarter.
The revolution is here! Welcome to TensorFlow 2.0.
Following the dramatic changes, you won’t be as much of a beginner as you imagined. The playing field got leveled, the game got easier, and there’s a seat saved just for you. Welcome! I’m glad you’re finally here and I hope you’re as excited about this new world of possibilities as I am.

Dive in!

Check out the shiny redesigned tensorflow.org for tutorials, examples, documentation, and tools to get you started… or dive straight in with:

pip install tensorflow==2.0.0-alpha0

You’ll find detailed instructions here.

#tensorflow #machine-learning #python

What is GEEK

Buddha Community

TensorFlow is dead, long live TensorFlow!

Heru

1554312056

By “Following the dramatic changes, you won’t be as much of a beginner as you imagined. The playing field got leveled, the game got easier, and there’s a seat saved just for you. Welcome! I’m glad you’re finally here and I hope you’re as excited about this new world of possibilities as I am.”> By “Following the dramatic changes, you won’t be as much of a beginner as you imagined. The playing field got leveled, the game got easier, and there’s a seat saved just for you. Welcome! I’m glad you’re finally here and I hope you’re as excited about this new world of possibilities as I am.”> By “Following the dramatic changes, you won’t be as much of a beginner as you imagined. The playing field got leveled, the game got easier, and there’s a seat saved just for you. Welcome! I’m glad you’re finally here and I hope you’re as excited about this new world of possibilities as I am.”

Alex  Sam

Alex Sam

1593782362

Top Chat Software for Live Streaming & Broadcasting Web & Mobile Apps

Do you Increase your Website Engagment? 

I analysed, ranked and reviewed best live video streaming chat APIs and SDKs for your web & mobile app based on client reviews and ratings. portfolio, usecases, cost, secure streaming, live chat features, cost, support, etc.

Turn your viewers into participatients with Live Streaming Chat Solutions. There are lot of Real-time chat apis & SDks Providers have in online market now. You can easily integrte and customize real time chat solutions into your new or existing live video streaming web and iOS & android applications. Below have mentioned best real time chat api & SDk Proivders.

Live video streaming chat api
Live video streaming chat apis

Here are The Most Popular Live Video Streaming Chat APIs & SDKs to be Considered for your Mobile App

1. CONTUS Fly - Real-time Messaging Platform for Live Streaming Apps & Webs

CONTUS Fly is one of the leading real time messaging software providers in the market for a decade. Their messaging platforms are completely customizable since they provide Chat APIs and SDKs to integrate real time chat feasibility on your live streaming applications irrespective of audience base. Engage your audience like a live concert, stadium like experience through digitally. Create channels for every live streaming event, sports or anything that would create buzz. Enable audience to interact with each other over voice, video chats and real-time text chats with engaging emojis. CONTUS Fly enables users to add emojis and stickers to captivate each audience and create fun.

Highlight Features of CONTUS Fly Live Video Streaming Platform Includes:

  1. Chat for Live Video Streaming
  2. Video & Audio Recording
  3. Video Calling
  4. Drawing whitebord
  5. Screen Sharing
  6. End to End Encryption

2. Apphitect -Instant chat for Live Streaming Platforms

To make every live streaming and broadcasting videos more engaging and entertaining, Apphitect’s instant messaging comes with exciting Instant messaging chat APIs to add chat into streaming applications. Apphitect is built with multiple real time communication features like video chat, voice chat and real-time chat to your streaming apps. Their solution surprisingly has a wide range of features to communicate, engage and increase subscription benefits.

Highlight Features of Apphitect Live Insterative Broadcasting Software Includes:

  1. Live Video Streaming Chat
  2. Cross Platform Support
  3. Audio & Video Recording
  4. Live Video Calling
  5. Emoji & Stickers

3. MirrorFly - Enterprise Real Time Chat for Streaming Websites

One of the enterprise-grade real-time chat solutions built to create virtual chat experience for live streaming events and websites for big brands and startups. Irrespective of audience base, category, MirrorFly provides customizable real time chat APIs to add virtual communication mediums on live streaming and broadcasting applications. Their solution comes with absolute moderation tools and open channels to talk and listen with your audience. MirrorFly’s server infrastructure has the potential to handle concurrent messages and users and to achieve maximum sales conversion.

Highlight Features of MirrorFly Live Streaming Chat API Includes:

  1. Face to Face Video Calling
  2. Live Interactive Broadcasting
  3. Call Recording
  4. Digital Whiteboard
  5. Group Video Calling

4. Applozic - Real-time Chat Plugin for Live Broadcasting & Video Streaming apps

When it comes to building a live streaming chat app software that covers the entire platforms and demand All-in-One package (features, Customization to any extent) with a one-time payment for lifetime performance, then undoubtedly Contus Fly makes the right choice to partner with. The company offers live broadcasting SDK for Android/iOS and chat APIs for customization.

Highlight Features of Applozic Chat Live Streaming Platform Includes:

  1. Real time Communication
  2. Cross Platform Support
  3. Live Audio Broadcasting
  4. Push Notifications
  5. Secure Image Sharing

5. Sendbird - Top Real time Chat for Live Video Streams

Being a leading real time chat platform provider in the market, Sendbird has its own hallmark of communication features to the world’s most prominent live streaming applications. Their real time chat solution enables broadcasting and streaming platform’ owners to create a physical equivalent digital chat experience for the audience during any live event streaming to interact, collaborate and cheer together within the same streaming screen. By creating open channels and groups, you can enable the audience to interact with each other during any streaming, engage them with polls, stickers, multiple communication channels and more.

Highlight Features of Sendbird Live Streaming Chat API Includes:

  1. Chat for Streaming website
  2. Messaging Data
  3. Multi Platforms
  4. Push Notifications
  5. End to End Encryption

6. Agora - Interactive Live Chat for Live Video Streaming

Agora, a deep integratable API available in the market to deliver live interactive streaming experience for workplace, enterprises, gaming, retail, telehealth and social live streaming websites. With easy-to-embed SDKs, Agora empowers businesses to add HD and low latency video and voice chat features into any streaming platforms and channels. Their easy-to-embed real time chat features encourage higher levels of user engagement and opportunity to drive more audience.

7. Enablex - A Redefined Communication APIs for In-app Chat

Their smart and secure chat APIs deliver real-time chat feasibility for live and on-demand video streaming websites. The real time chat features provides users to communicate and engage within the same streaming platform irrespective of interaction medium and audience count. Enablex offers platform-as-a-service communication solutions for real time messaging integration with APIs hosting possibility on public, private and cloud deployment. Their APIs are enriched with multiple communication features and engagement tools like live-polls, stickers and more.

8. Pubnub - In-app Chat Platforms for Live Event Streaming Websites

In order to increase user engagement with live and remote audiences, Pubnub offers real time messaging chat functionality with interactive features to drive event-based engagement with mass chat. Their in-app chat feature enhances live programs, event streaming and blogging content with live polling, multiple chats and more. It also enables live streaming websites to build community, channels and super groups during live streaming to bring the entire audience base to one place.

9. Vonage - Communication APIs for In-app Messagings

Vonage is a prime provider of communication APIs for major industrial sectors and enterprise workplaces. With its API, businesses such as live streaming applications can integrate in-app messaging features into any streaming platforms on Android, iOS and Web to empower user engagement. Their APIs are powered with scalable infrastructure and provide multiple communication mediums such as in-app voice, video and chat proactively engaging the audience.

10. Firekast - Live Chat Widget for Video Streaming Player

Firekast provides a customizable live chat widget with HTML code for streaming players to enable chat within any streaming or on-demand videos. The chat widget gives the ability for brands and content owners to make the audience to interact with each other for better engagement and proactivity during streaming. The Firekast Live chat comes with moderator tools that will allow administrators to delete or ban abusive content and users from the channel or groups. Firekast’s live chat comes with a private chat widget to create public or private chat rooms to make effective collaboration and discussions.
 

Conclusion
And this is all the real time chat providers in the market to implement chat functionality in any live streaming or broadcasting platforms. More than delivering entertaining live content, creating a massive engagement and buzz for every live event is the smarter way to turn every audience into a protiable subscriber. Picking up the right software provider is more important than just handling the integration process.

#live #live-streaming-solutions #live-streaming-chat-api #live-streaming-chat-sdk #chat-api-for-live-broadcasting

Edward Jackson

Edward Jackson

1566748676

TensorFlow is dead, long live TensorFlow!

TensorFlow is ann open source machine learning library for research and production. TensorFlow is dead, long live TensorFlow

If you’re an AI enthusiast and you didn’t see the big news this month, you might have just snoozed through an off-the-charts earthquake. Everything is about to change!

Last year I wrote 9 Things You Need To Know About TensorFlow… but there’s one thing you need to know above all others: TensorFlow 2.0 is here!

The revolution is here! Welcome to TensorFlow 2.0.
It’s a radical makeover. The consequences of what just happened are going to have major ripple effects on every industry, just you wait. If you’re a TF beginner in mid-2019, you’re extra lucky because you picked the best possible time to enter AI (though you might want to start from scratch if your old tutorials have the word “session” in them).

In a nutshell:TensorFlow has just gone full Keras. Those of you who know those words just fell out of your chairs. Boom!

A prickly experience

I doubt that many people have accused TensorFlow 1.x of being easy to love. It’s the industrial lathe of AI… and about as user-friendly. At best, you might feel grateful for being able to accomplish your AI mission at mind-boggling scale.

You’d also attract some raised eyebrows if you claimed that TensorFlow 1.x was easy to get the hang of. Its steep learning curve made it mostly inaccessible to the casual user, but mastering it meant you could talk about it the way you’d brag about that toe you lost while climbing Everest. Was it fun? No, c’mon, really: was it fun?

You‘re not the only one — it’s what TensorFlow 1.x tutorials used to feel like for everybody.

TensorFlow’s core strength is performance. It was built for taking models from research to production at massive scale and it delivers, but TF 1.x made you sweat for it. Persevere and you’d be able to join the ranks of ML practitioners who use it for incredible things, like finding new planets and pioneering medicine.

What a pity that such a powerful tool was in the hands of so few… until now.

Don’t worry about what tensors are. We

just called them (generalized) matrices where I grew up. The name

TensorFlow is a nod to the fact that TF’s very good at performing

distributed computations involving multidimensional arrays (er,

matrices), which you’ll find handy for

AI](http://bit.ly/quaesita_emperor) “http://bit.ly/quaesita_emperor)”) at scale.

Image source.](http://karlstratos.com/drawings/drawings.html). “http://karlstratos.com/drawings/drawings.html).”)

Cute and cuddly Keras

Now that we’ve covered cactuses, let’s talk about something you’d actually want to hug. Overheard at my place of work: “I think I have an actual crush on Keras.”

Keras is a specification for building models layer-by-layer that works with multiple machine learning frameworks (so it’s not a TF thing), but you might know it as a high level API accessed from within TensorFlow as tf.keras.

Incidentally, I’m writing this section on Keras’ 4th birthday (Mar 27, 2019) for an extra dose of warm fuzzies.

Keras was built from the ground up to be Pythonic and always put people first — it was designed to be inviting, flexible, and simple to learn.

Why don’t we have both?

Why must we choose between Keras’s cuddliness and traditional TensorFlow’s mighty performance? What don’t we have both?

Great idea! Let’s have both! That’s TensorFlow 2.0 in a nutshell.

This is TensorFlow 2.0. You can mash those orange buttons yourself here.](http://bit.ly/tfoview). “http://bit.ly/tfoview).”)

The revolution is here! Welcome to TensorFlow 2.0.### The usability revolution

Going forward, Keras will be the high level API for TensorFlow and it’s extended so that you can use all the advanced features of TensorFlow directly from tf.keras.

The revolution is here! Welcome to TensorFlow 2.0.

In the new version, everything you’ve hated most about TensorFlow 1.x gets the guillotine. Having to perform a dark ritual just to add two numbers together? Dead. TensorFlow Sessions? Dead. A million ways to do the exact same thing? Dead. Rewriting code if you switch hardware or scale? Dead. Reams of boilerplate to write? Dead. Horrible unactionable error messages? Dead. Steep learning curve? Dead.

The revolution is here! Welcome to TensorFlow 2.0.
You’re expecting the obvious catch, aren’t you? Worse performance? Guess again! We’re not giving up performance.

TensorFlow is now cuddly and this is a game-changer, because it means that one of the most potent tools of our time just dropped the bulk of its barriers to entry. Tech enthusiasts from all walks of life are finally empowered to join in because the new version opens access beyond researchers and other highly-motivated folks with an impressive pain threshold.

The revolution is here! Welcome to TensorFlow 2.0.
Everyone is welcome. Want to play? Then come play!

Eager to please

In TensorFlow 2.0, eager execution is now the default. You can take advantage of graphs even in eager context, which makes your debugging and prototyping easy, while the TensorFlow runtime takes care of performance and scaling under the hood.

Wrangling graphs in TensorFlow 1.x (declarative programming) was disorienting for many, but it’s all just a bad dream now with eager execution (imperative programming). If you skipped learning it before, so much the better. TF 2.0 is a fresh start for everyone.

As easy as one… one… one…

Many APIs got consolidated across TensorFlow under Keras, so now it’s easier to know what you should use when. For example, now you only need to work with one set of optimizers and one set of metrics. How many sets of layers? You guessed it! One! Keras-style, naturally.

In fact, the whole ecosystem of tools got a spring cleaning, from data processing pipelines to easy model exporting to TensorBoard integration with Keras, which is now a… one-liner!

There are also great tools that let you switch and optimize distribution strategies for amazing scaling efficiency without losing any of the convenience of Keras.

Those distribution strategies are pretty, aren’t they?

The catch!

If the catch isn’t performance, what is it? There has to be a catch, right?

Actually, the catch was your suffering up to now. TensorFlow demanded quite a lot of patience from its users while a friendly version was brewing. This wasn’t a matter of sadism. Making tools for deep learning is new territory, and we’re all charting it as we go along. Wrong turns were inevitable, but we learned a lot along the way.

The revolution is here! Welcome to TensorFlow 2.0.
The TensorFlow community put in a lot of elbow grease to make the initial magic happen, and then more effort again to polish the best gems while scraping out less fortunate designs. The plan was never to force you to use a rough draft forever, but perhaps you habituated so well to the discomfort that you didn’t realize it was temporary. Thank you for your patience!
The revolution is here! Welcome to TensorFlow 2.0.
The reward is everything you appreciate about TensorFlow 1.x made friendly under a consistent API with tons of duplicate functionality removed so it’s cleaner to use. Even the errors are cleaned up to be concise, simple to understand, and actionable. Mighty performance stays!

What’s the big deal?

Haters (who’re gonna hate) might say that much of v2.0 could be cobbled together in v1.x if you searched hard enough, so what’s all the fuss about? Well, not everyone wants to spend our days digging around in clutter for buried treasure. The makeover and clean-up are worth a standing ovation. But that’s not the biggest big deal.

The point not to miss is this: TensorFlow just announced an uncompromising focus on usability.

The revolution is here! Welcome to TensorFlow 2.0.
AI lets you automate tasks you can’t come up with instructions for. It lets you automate the ineffable. Democratization means that AI at scale will no longer be the province of a tiny tech elite.
The revolution is here! Welcome to TensorFlow 2.0.
Imagine a future where “I know how to make things with Python and “I know how to make things with AI are equally commonplace statements… Exactly! I’m almost tempted to use that buzzword “disruptive” here.

The great migration

We know it’s hard work to upgrade to a new version, especially when the changes are so dramatic. If you’re about to embark on migrating your codebase to 2.0, you’re not alone — we’ll be doing the same here at Google with one of the largest codebases in the world. As we go along, we’ll be sharing migration guides to help you out.

The revolution is here! Welcome to TensorFlow 2.0.
If you rely on specific functionality, you won’t be left in the lurch — except for contrib, all TF 1.x functions will live on in the compat.v1 compatibility module. We’re also giving you a script which automatically updates your code so it runs on TensorFlow 2.0. Learn more in the video below.

This video’s is a great resource if you’re eager to dig deeper into TF 2.0 and geek out on code snippets.

Your clean slate

TF 2.0 is a beginner’s paradise, so it will be a downer for those who’ve been looking forward to watching newbies suffer the way you once suffered. If you were hoping to use TensorFlow for hazing new recruits, you might need to search for some other way to inflict existential horror.

The revolution is here! Welcome to TensorFlow 2.0.
Sitting out might have been the smartest move, because now’s the best time to arrive on the scene. As of March 2019, TensorFlow 2.0 is available in alpha (that’s a preview, you hipster you), so learning it now gets you ready in time for the full release that the community is gearing up for over the next quarter.
The revolution is here! Welcome to TensorFlow 2.0.
Following the dramatic changes, you won’t be as much of a beginner as you imagined. The playing field got leveled, the game got easier, and there’s a seat saved just for you. Welcome! I’m glad you’re finally here and I hope you’re as excited about this new world of possibilities as I am.

Dive in!

Check out the shiny redesigned tensorflow.org for tutorials, examples, documentation, and tools to get you started… or dive straight in with:

pip install tensorflow==2.0.0-alpha0

You’ll find detailed instructions here.

#tensorflow #machine-learning #python

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

What is LONG coin (LONG)?

Brief and simple:

LONG COIN - Cryptocurrency and Social Blockchain Network

We have added the ability to send a string of 256 characters to blockchain transactions. That is, through the blockchain, you can send / receive messages (a kind of twitter) and build more complex structures such as blockchain stores, social networks, lotteries, games, etc.

About privacy:

All messages are also encrypted and the developer has no keys. You encrypt with your private key and the recipient’s public key, which is its address. Where is the best place to hide a needle? In a haystack? No. Best in a stack of other needles. The entire blockchain is flooded with messages and it is not possible to track and decipher yours. (Encryption algorithm - ecdh and aes cbc)

Specification

Coin name: LONG COIN

Coin Ticker: LONG

Hash Algorithm: SHA-256

Message encryption algorithm: ecdh and aes cbc

Coin Type: POW

Block time: 2 minutes

Premine: 0

Confirmation period: 30 blocks

Block Reward: 10,000

Fixed commission: 1 LONG / KB

  • SMS — 1 LONG

  • Standard financial transactions - 1 LONG

  • Limit on the volume of data transactions - 64kB (max commission 64 LONG)

Multicast transactions with simultaneous transmission of coins and messages

How and Where to Buy LONG ?

LONG has been listed on a number of crypto exchanges, unlike other main cryptocurrencies, it cannot be directly purchased with fiats money. However, You can still easily buy this coin by first buying Bitcoin, ETH, USDT from any large exchanges and then transfer to the exchange that offers to trade this coin, in this guide article we will walk you through in detail the steps to buy LONG

You will have to first buy one of the major cryptocurrencies, usually either Bitcoin (BTC), Ethereum (ETH), Tether (USDT)…

We will use Binance Exchange here as it is one of the largest crypto exchanges that accept fiat deposits.

Binance is a popular cryptocurrency exchange which was started in China but then moved their headquarters to the crypto-friendly Island of Malta in the EU. Binance is popular for its crypto to crypto exchange services. Binance exploded onto the scene in the mania of 2017 and has since gone on to become the top crypto exchange in the world.

Once you finished the KYC process. You will be asked to add a payment method. Here you can either choose to provide a credit/debit card or use a bank transfer, and buy one of the major cryptocurrencies, usually either Bitcoin (BTC), Ethereum (ETH), Tether (USDT)

SIGN UP ON BINANCE

Step by Step Guide : What is Binance | How to Create an account on Binance (Updated 2021)

Next step - Transfer your cryptos to an Altcoin Exchange

Since LONG is an altcoin we need to transfer our coins to an exchange that LONG can be traded. Below is a list of exchanges that offers to trade LONG in various market pairs, head to their websites and register for an account.

Once finished you will then need to make a BTC/ETH/USDT deposit to the exchange from Binance depending on the available market pairs. After the deposit is confirmed you may then purchase LONG from the exchange.

Exchange: WhiteBIT, and STEX

Apart from the exchange(s) above, there are a few popular crypto exchanges where they have decent daily trading volumes and a huge user base. This will ensure you will be able to sell your coins at any time and the fees will usually be lower. It is suggested that you also register on these exchanges since once LONG gets listed there it will attract a large amount of trading volumes from the users there, that means you will be having some great trading opportunities!

Top exchanges for token-coin trading. Follow instructions and make unlimited money

BinanceBittrexPoloniexBitfinexHuobiMXCProBITGate.ioCoinbase

Find more information LONG

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#bitcoin #blockchain #long coin #long #cryptocurrency

Mckenzie  Osiki

Mckenzie Osiki

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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