Tensorflow Cheat Sheet

Posted on by



In May 20 1 7 Google announced the second-generation of the TPU, as well as the availability of the TPUs in Google Compute Engine. The second-generation TPUs deliver up to 180 teraflops of performance, and when organized into clusters of 64 TPUs provide up to 11.5 petaflops.

  1. Theano Theano is another deep-learning library with python-wrapper (was inspiration for Tensorflow) Theano and TensorFlow are very similar systems. TensorFlow has better support for distributed systems though, and has development funded by Google, while Theano is.
  2. TensorFlow is an end-to-end open-source platform from Google developed to address the needs of machine learning. This cheat sheet embraces: the basics of data set management and feature engineering; a reference machine learning workflow with TensorFlow 2.0; model serialization and deserialization examples.


Tensorflow cheat sheet free

TensorFlow is an open source software library developed by Google for numerical computation with data flow graphs. This TensorFlow guide covers why the library matters, how to use it, and more.

TensorFlow was originally a deep learning research project of the Google Brain Team that has since become–by way of collaboration with 50 teams at Google–a new, open source library deployed across the Google ecosystem, including Google Assistant, Google Photos, Gmail, search, and more. With TensorFlow in place, Google is able to apply deep learning across numerous areas using perceptual and language-understanding tasks. (Note: This article about TensorFlow is also available as a free PDF download.)

This cheat sheet is an easy way to get up to speed on TensorFlow. We’ll update this guide periodically when news and updates about TensorFlow are released.

SEE: How to build a successful developer career (free PDF) (TechRepublic)

Executive summary

  • What is TensorFlow? Google has the single greatest machine learning infrastructure in the world, and with TensorFlow, Google now has the ability to share that. TensorFlow is an open source library of tools that enable software developers to apply deep learning to their products.
  • Why does TensorFlow matter? AI has become crucial to the evolution of how users interact with services and devices. Having such a powerful set of libraries available can enable developers to include this powerful deep learning evolution to their products.
  • Who does TensorFlow affect? TensorFlow will have a lasting effect on developers and users. Since the library was made open source, it is available to all developers, which means their products can be significantly enhanced to bring a higher level of intelligence and accuracy to their products.
  • When was TensorFlow released? TensorFlow was originally released November 9, 2015, and the stable release was made available on February 15, 2017. Google has now released TensorFlow 2.4, which includes a number of new features and profiler tools.
  • How do I start using TensorFlow? Developers can download the source code from the TensorFlow GitHub repository. Users are already seeing its effects in the Google ecosystem.
Tensorflow 2.0 pdf

SEE: How to implement AI and machine learning (ZDNet special feature) | Download the free PDF version (TechRepublic)

What is TensorFlow?

When you have a photo of the Eiffel Tower, Google Photos can identify the image. This is possible thanks to deep learning and developments like TensorFlow. Prior to TensorFlow there was a division between the researchers of machine learning and those developing real products; that division made it challenging for developers to include deep learning in their software. With TensorFlow, that division is gone.

TensorFlow delivers a set of modules (providing for both Python and C/C++ APIs) that enable constructing and executing TensorFlow computations, which are then expressed in stateful data flow graphs. These graphs make it possible for applications like Google Photos to become incredibly accurate at recognizing locations in images based on popular landmarks.

SEE: All of TechRepublic’s cheat sheets and smart person’s guides

In 2011, Google developed a product called DistBelief that worked on the positive reinforcement model. The machine would be given a picture of a cat and asked if it was a picture of a cat. If the machine guessed correctly, it was told so. An incorrect guess would lead to an adjustment so that it could better recognize the image.

TensorFlow improves on this concept by sorting through layers of data called Nodes. Diving deeper into the layers would allow for more and complex questions about an image. For example, a first-layer question might simply require the machine to recognize a round shape. In deeper layers, the machine might be asked to recognize a cat’s eye. The flow process (from input, through the layers of data, to output) is called a tensor…hence the name TensorFlow.

What is TensorFlow 2.0?

Google is in the process of rolling out TensorFlow 2.0, which includes the following improvements:

  • Helps make API components integrate better with tf.keras (a high-level interface for neural networks that runs on top of multiple backends).
  • Includes TensorFlow.js version 1.0, which allows the use of off-the-shelf JavaScript models, can retrain existing JS models, and enables the building and training of models directly in JavaScript.
  • Includes TensorFlow Federated, which is an open source framework for experimenting with machine learning (and other computations) using decentralized data.
  • Includes TF Privacy, a library for training machine learning models with a focus on privacy for training data.
  • Features eager execution, which is an imperative programming environment that evaluates operations immediately, without building graphs before returning concrete values.
  • Uses tf.function, which allows you to transform a subset of Python syntax into portable, high-performance graphs, and improves performance and deployability of eager execution.
  • Advanced experimentation will be made possible with new extensions Ragged Tensors (the TensorFlow equivalent of nested variable-length lists), TensorFlow Probability (a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning), and Tensor2Tensor (a library of deep learning models and datasets).
  • A conversion tool that automatically updates TensorFlow 1.x Python code so that it can be used with TensorFlow 2.0 compatible APIs (and flags cases where said code cannot be automatically converted).

SEE: Free machine learning courses from Google, Amazon, and Microsoft: What do they offer? (TechRepublic Premium)

Why does TensorFlow matter?

Machine learning is the secret sauce for tomorrow’s innovation. Machine learning, also called deep learning, is considered a class of algorithms that:

  • Use many layers of nonlinear processing units for feature extraction and transformation; and
  • are based on the learning of multiple levels of features or representations of the data; and
  • learn multiple levels of representation corresponding to different levels of abstraction.

Thanks to machine learning, software and devices continue to become smarter. With today’s demanding consumers and the rise of big data, this evolution has become tantamount to the success of a developer and their product. And because TensorFlow was made open source, it means anyone can make use of this incredible leap forward brought to life by Google. In fact, TensorFlow is the first serious framework for deep learning to be made available through the Apache 2.0 license.

With developers and companies able to use the TensorFlow libraries, more and more applications and devices will become smarter, faster, and more reliable. TensorFlow will be able to sort through vast numbers of images at an unprecedented rate.

Cheat

Because Google made TensorFlow open source, the libraries can be both improved upon and expanded into other languages such as Java, Lua, and R. This move brings machine learning (something heretofore only available to research institutes) to every developer, so they can teach their systems and software to recognize images or translate speech. That’s big.

Who does TensorFlow affect?

TensorFlow not only makes it possible for developers to include the spoils of deep learning into their products, but it makes devices and software significantly more intelligent and easier to use. In our modern, mobile, and 24/7 connected world, that means everyone is affected. Software designers, developers, small businesses, enterprises, and consumers are all affected by the end result of deep learning. The fact that Google created a software library that dramatically improves deep learning is a big win for all.

SEE: Research: Companies lack skills to implement and support AI and machine learning (TechRepublic Premium)

When was TensorFlow released?

TensorFlow was originally released November 9, 2015, and the stable release was made available on February 15, 2017. TensorFlow 2.0 alpha is available now, with the public preview coming soon. You can learn more about the TensorFlow 2.0 alpha in the official Get Started with TensorFlow guide.

The libraries, APIs, and development guides are available now, so developers can begin to include TensorFlow into their products. Users are already seeing the results of TensorFlow in the likes of Google Photos, Gmail, Google Search, Google Assistant, and more.

Tensorflow Cheat Sheet

SEE: Git guide for IT pros (free PDF) (TechRepublic)

What new features are found in TensorFlow 2.4?

Among the new features found in the latest release of TensorFlow include:

  • The tf.distribute module now includes experimental support for asynchronous training models with ParameterServerStrategy and custom training loops. In order to get started with this strategy, read through this Parameter Server Training tutorial, which demonstrates how to setup ParameterServerStrategy.
  • MultiWorkerMirroredStrategy is now a part of the stable API and implements distributed training with synchronous data parallelism.
  • The Karas mixed precision API is now part of the stable API and allows for the user of 16-bit and 32-bit floating point types.
  • The tf.keras.optimizers.Optimizer has been refactors, enabling the user of model.fit or custom training loops to write code that will work with any optimizer.
  • The experimental support of a NumPy API subset, tf.experimental.numpy, has been introduced which enables developers to run TensorFlow accelerated NumPy code.
  • New profiler tools have been added so developers can measure the training performance and resource consumption of TensorFlow models.
  • TensorFlow now runs with CUDA 11 and cuDNN 8, which enables support for NVIDIA Ampere GPU architecture.

Competitors to TensorFlow

TensorFlow isn’t alone in the deep learning field; in fact, there are a number of other companies with machine learning frameworks, including the following.

SEE: Linux service control commands (TechRepublic Premium)

How do I start using TensorFlow?

The first thing any developer should do is read the TensorFlow Getting Started guide, which includes a TensorFlow Core Tutorial. If you’re new to machine learning, make sure to check out the following guides:

Developers can install TensorFlow on Linux, Mac, and Windows (or even install from source), or check out their various tools from the official TensorFlow GitHub page.

Finally, developers can take advantage of all the TensorFlow guides:

Open Source Weekly Newsletter

You don’t want to miss our tips, tutorials, and commentary on the Linux OS and open source applications.
Delivered Tuesdays

Also see


Source link

Have a little time to learn Tensorflow 2.0 with your Machine Learning? In this article, I have put together the 10 best Tensorflow cheat sheets for you to hang on the wall above your desk. Whenever you need a reference, keep these handy cheat sheets available!!

Cheat Sheet 1: BecomingHuman.AI

becominghuman.ai has multiple cheat sheets but this one I have found to be one of the best. Easy to read and understand, this cheat sheet is great for beginners and advanced Tensorflow learners alike!

Pros: Rated ‘E’ for everyone.

Cons: Color can be distracting

Cheat Sheet 2: Altoros

Altoros is another great website to find cheat sheets for machine learning on!! They cover a wide range of subjects. This Tensorflow cheat sheet is 3 pages long but it is totality worth the wealth of information you can receive.

Tensorflow Cheat Sheet Pdf

Pros: Easy to read and understand.

Cons: Condensed information can be difficult for some readers.

Cheat Sheet 3: Tech Republic

This cheat sheet from Tech Republic is chock full of information for you! Including an introduction, how to begin, top competitors and additional resources. This is great if you are looking to become an IT Pro

Pros: Great for those who are looking to upgrade their skills.

Cons: It is a lot of reading material (8 pages, condensed)

Tensorflow Cheat Sheet Pdf

Cheat Sheet 4: Dummies

Github

Sometimes, the best way to learn is from a dummy!! Tensorflow for dummies is a great way to get an introduction to Tensorflow, what it is and how it works. Perfect for beginners in machine learning!

Pros: Great information for beginners

Cons: Condensed information, no working examples

Cheat Sheet 5: TensorFlow

To learn Tensorflow, one must go to Tensorflow.org! This website and cheat sheet will teach you everything you need to learn Tensorflow correctly and effectively. There is a lot of materials here so be prepared for a lengthy read! Great for beginners and Advanced Tensorflowers!

Pros: Rated ‘E’ for everyone, the best way to Tensorflow.

Cons: Can be confusing to the absolute beginner.

Tensorflow Cheat Sheet Pdf

Cheat Sheet 6: Github

This is a really great cheat sheet written by Patrick on Github! He shows examples and syntax. This is a great sheet for all Tensorflow learners!!

Pros: Rated ‘E’ for everyone.

Cons: None that I can see.

Cheat sheet 7: Stanford.edu

This cheat sheet shows you the ins and outs of Tensorflow what it is, how it works and how it compares to other data science tools compare. Easily readable for beginners and advanced Tensorflow users alike.

Pros: Easy to understand

Cons: None that I can see

Tensorflow 2.0 Cheat Sheet Pdf

Cheat Sheet 8: Tensorflow Core

This cheat sheet is from Tensorflow Core. It shows api documentation for Tensorflow in Python!! Alongside other languages, it shows the correct explanation and syntax for the method you are trying to perform.

Pros: Easy to read, rated ‘E’ for everyone.

Cons: none that I can see.

Cheat Sheet 9: HackerNoon

To understand Tensorflow, you must understand Deep Learning. This cheat sheet is one to keep handy as a dog-eared reference in the desk drawer or right next to your working laptop.

Camron will take you from beginning to end understanding Tensorflow and deep learning easier with explanations plus the best resources. This is a great resource for those who are serious about looking into Data Science as a career with Python and Tensorflow.

Pros: Tons of resources, rated ‘E’ for everyone.

Tensorflow Cheat Sheet 2019

Cons: A lot of research materials and reading.

Cheat Sheet 10: Cheatography

This cheat sheet will show you the types of models from machine learning you can build with Tensorflow!! It has graphics, explanations and examples on what you need to know for Tensorflow, Machine and Deep Learning!

Pros: Rated ‘E’ for everyone

Cons: None that I can see.

Data Science Cheat Sheet Pdf

Thank you for joining me on another journey to find the top 10 best cheat sheets on Tensorflow! I hope that they are useful to you on your journey in Deep Learning and Tensorflow!

Related Articles:

Related Posts