What is TensorFlow playground?
TensorFlow playground is an interactive web application that makes it possible for users to test artificial intelligence (AI) algorithms through the use of Google’s TensorFlow machine learning libraries.
It enables users who aren’t very familiar with high-level math and coding to experiment with neural networks for deep learning and other machine learning applications. Neural network operations are interactive and are represented in a visual manner in the TensorFlow Playground.
The app itself was built on Typescript by making use of ds3.js. The browser allows the user to run small neural networks, conduct experiments and get results. There is an open source library included which designed to meet educational requirements. Even though the library is small, it is fast, flexible, scalable and ready to use for learning, research and production. Google even offers training and exercises to help users learn more about neural networks and machine learning.
What is TensorFlow used for?
TensorFlow is an end-to-end platform that enables you to build and deploy machine learning models. It derives its name from the data flow graphs from which numerical calculations are performed. These graphs represent mathematical operations through nodes and data through edges. The edges usually tend to be multidimensional data arrays (or Tensors). Google also uses the term Tensor for TPU (tensor processing unit), the company's AI accelerator ASIC.
TensorFlow provides you with multiple levels of abstraction, making it possible for you to pick the one that suits your needs. It allows you to build and train models through the use of the high-level Keras API, which makes it rather easy to get started with TensorFlow and machine learning.
If you need even more flexibility, you can use eager execution which makes immediate iteration and intuitive debugging possible. For large machine learning training tasks, you can make use of the Distribution Strategy API for distributed training on various hardware configurations without changing the model definition.
TensorFlow offers you a direct path to production. Whether it's on servers, edge devices, or the web, TensorFlow makes it possible for you to train and deploy your model with great ease, irrespective of the language or platform you use.
It even lets you conduct experiments for research powerfully. You can create and train state-of-the-art models without needing to sacrifice speed or performance. You even get a lot of flexibility and control with features like the Keras Functional API and Model Subclassing API to create complex topologies. Eager execution is great for easy prototyping and fast debugging.
There’s also aecosystem of powerful add-on libraries and models that you can experiment with, including Ragged Tensors, TensorFlow Probability, Tensor2Tensor and BERT.
What are advantages of TensorFlow?
TensorFlow is an open source platform and is available to users around the world to be used for the development of any system.
Tensorflow offers you a better way to visualize data with its graphical approach. It even makes it possible to debug nodes with great ease through the use of TensorBoard. This lowers the effort of visiting the whole code and effectively resolves the neural network.
Works well with Keras
TensorFlow is compatible with Keras, which makes it possible for users to code some high-level functionality sections in it. Keras brings a lot of system-specific functionality to TensorFlow, like pipelining, estimators, and eager execution. The Keras functional API supports a variety of topologies with several combinations of inputs, output, and layers.
You can perform pretty much every action on this platform. Its characteristic of being deployed on every machine and graphical representation of a model enables its users to develop any kind of system using TensorFlow. That’s why it has been possible to develop systems like Airbnb, Dropbox, Intel, Snapchat, etc.with the help of TensorFlow.
TensorFlow is used as a hardware acceleration library because of the parallelism of work models. It employs various distribution strategies in GPU and CPU systems. You can choose to run your code on either of the architectures on the basis of the modeling rule. If nothing is specified, the system is will choose a GPU. This process reduces the memory allocation substantially.
TensorFlow even has its architecture TPU which carries out computations much faster than GPU and CPU. If you build a model using TPU, you can easily deploy it on a cloud at a cheaper rate and even execute it at a faster rate.
Deep learning uses TensorFlow for its development because it makes it possible to build neural networks with the help of graphs that represent operations as nodes. It acts in multiple domains such as image recognition, voice detection, motion detection, time series, etc. so it suits the requirements of the users.
What are the disadvantages of TensorFlow?
There are new updates every 2-3 months, which increases the overhead for installing it and binding it with your existing system.
TensorFlow provides homonyms that have very similar names but different implementations, making it confusing to remember and use.
Limitations in architecture
TensorFlow’s architecture TPU only allows you to execute a model, not to train it.
TensorFlow lags when it comes to providing the symbolic loops for indefinite sequences. providing the symbolic loops for indefinite sequences, which is why it is known as a low-level API.
TensorFlow only supports NVIDIA for GPU, and Python for GPU programming. It does not support anything else.
TensorFlow isn’t as fast as its competitors. Because of this, it has less usability than other networks.
Limitations for Windows
TensorFlow does not offer Windows OS users as many features as it offers Linux users. But Windows users can download TensorFlow by making use of the anaconda prompt or the pip package.