TensorFlow features a comprehensive, flexible ecosystem of tools and community resources to use best-in-class machine learning techniques to facilitate streamlined building and development. TensorFlow is an open-source library that provides software engineers and data scientists with a tool to build, train and work with deep learning models in order to draw predictions from data.
Why Use TensorFlow?
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.
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, 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.
Do You Need to Use Python for TensorFlow?
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.
Is TensorFlow Difficult to Learn?
TensorFlow is considered both difficult to learn and use, largely due to the amount of programming skill needed.
While TensorFlow is powerful 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.
