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What is TensorFlow?

TensorFlow is an open-source library that is used to provide software engineers and data scientists with a tool for building, training and working with deep learning models in order to draw predictions from data. The platform features a comprehensive, flexible ecosystem of tools and community resources to use best-in-class machine learning techniques to facilitate streamlined building and development.

What is TensorFlow used for?
Answer Part 1

TensorFlow is used to streamline the development and training of machine learning models. It is primarily used for classification, perception, understanding, discovery, prediction and creation, with real-world applications.

Answer Part 2

TensorFlow was created by Google Brain in 2011 to offer data scientists and engineers powerful software for streamlining arduous tasks associated with training machine learning models, such as developing and executing advanced analytics applications. The library uses data flow graphs to build models and create large-scale, multilayered neural networks. TensorFlow’s classification, perception, understanding, discovery, prediction and creation capabilities facilitate real-world technology uses like creating image and voice recognition technology and time series algorithms.

For image recognition, or the ability to identify objects within images, TensorFlow possesses object recognition algorithms that classify and identify arbitrary objects within larger images. When applied at scale, this allows machine learning models to analyze several thousand images containing any object, like a cup, for example, and be able to identify a cup it has never seen before. Image recognition technology has many applications across industries, including healthcare, construction and more.

Is TensorFlow difficult to learn?
Answer Part 1

TensorFlow is considered both difficult to learn and use, largely due to the amount of programming skill needed.

Answer Part 2

While TensorFlow is highly capable and streamlines the development and training of machine learning models, the power that TensorFlow delivers requires extensive knowledge of how to use it. First, users must understand that TensorFlow does not abstract many of the hard parts of programming. APIs, such as Dad Jokes and Twilio, wrap pieces of code that would be otherwise challenging to program individually, while TensorFlow does not. Instead, TensorFlow focuses on streamlining more demanding model training tasks. Users must possess a great deal of machine learning knowledge and how models should be trained in order to properly utilize the software. Understanding concepts such as epochs, loss functions, how types of models differ, cross-entropy loss, padding and activation functions is key.

Additionally, gathering and preparing data for TensorFlow can be an arduous and detail-oriented task. Machine learning models require a bevy of input data to be trained accurately, meaning users must gather enough data to feed into the model and label it in a way the model can interpret. This requires users to find ways to download large quantities of data and label each individual data element before it can be fed into TensorFlow.

For all of these reasons, the learning curve for using TensorFlow can be intense, but the library provides exceptional performance and the ability to significantly reduce the workflow of machine learning engineers and data scientists.

Do you need Python for Tensorflow?
Answer Part 1

TensorFlow supports many programming languages, but Python is the most commonly used language on the platform.

Answer Part 2

TensorFlow can utilize a variety of programming languages, including JavaScript, Swift, C, Go, Java, Haskell and C#. While users have the choice of whichever language they are most comfortable with, Python is the most popular option due to its popularity and plentiful data science libraries. 

TensorFlow’s core is written in a combination of highly optimized C++ and CUDA, which is NVIDIA’s proprietary language for programming GPUs, meaning that TensorFlow allows developers to build machine learning models within TensorFlow constructs that are then executed by C++ code, faster and more efficient than Python when it comes to training machine learning models.



TensorFlow Courses to Grow Your Skill Set

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Learn TensorFlow and other top skills by taking one of Udemy’s top-rated data science courses.


Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0


What you'll learn:

  • How to use Tensorflow 2.0 in Data Science



Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques!


What you'll learn:

  • Understand how Neural…



Learn to use Python for Deep Learning with Google's latest Tensorflow 2 library and Keras!


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  • Learn to use TensorFlow 2.0 for Deep Learning…



Pass the TensorFlow Developer Certification Exam by Google. Become an AI, Machine Learning, and Deep Learning expert!


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TensorFlow Certifications to Expand Your Career Possibilities

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Prove your TensorFlow knowledge with a data science certification from Udacity.

Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects.

This program is intended for students with experience in Python, who have not yet studied Machine Learning topics.

3 months
10 hours

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