TensorFlow™ is an open source software library for high performance numerical computation. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices.

TensorFlow Pros & Cons

The more advanced the technology, the more useful it can be, but of course everything has its downside and so does this machine learning library. When comparing TensorFlow with other libraries like Scikit, Torch, Theano, Neon, there are drawbacks in a number of features that the library lets you manipulate. This library is maintained and updated by Google, the tech giant, so needless to say, it has come a far way since its initial release. So here are the TensorFlow Advantages and Disadvantages.

TensorFlow Pros and Cons

Auto-differentiation autodiff (no more taking derivatives by hand.)
Debugging Tensorflow lets you execute subparts of a graph which gives it an upper-hand as you can introduce and retrieve discrete data onto an edge and therefore offers great debugging method.
Flexibility from Raspberry Pi, Android, Windows, iOS, Linux to server farms
Graphs Tensorflow has better computational graph visualizations, which are indigenous when compared to other libraries like Torch and Theano.
Large community (> 10,000 commits and > 3000 TF-related repos in 1 year)
Library Management Backed by Google, TensorFlow has the advantage of the seamless performance, quick updates and frequent new releases with new features.
Pipelining TensorFLow is highly parallel and designed to use various backends software (GPU, ASIC) etc.
Portability deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API
Scalability The libraries can be deployed on a gamut of hardware machines, starting from cellular devices to computers with complex setups.
Checkpoints (for managing experiments)

TensorFlow Cons

Benchmark Tests TensorFlow lacks behind in both speed and usage when compared to its competitors.
Missing Symbolic Loops The feature that’s most required when it comes to variable length sequences are the symbolic loops. Unfortunately, TensorFlow does not offer this feature, but there is a workaround using finite unfolding (bucketing).
No GPU support (Nvidia language support only) Currently, the only supported GPUs are that of NVIDIA and the only full language support is of Python which makes it a downside as there is a rise of other languages in deep learning as well like Lau.
No support for Windows There is still a wide variety of users who are comfortable with a windows environment rather than a Linux in their systems and TensorFlow currently does not assuage these users. But, you need not worry if you are a Windows user as you can install it within a conda environment or using the python package library, pip.

TensorFlow Applications

Among the applications for which TensorFlow is the foundation, are automated image captioning software, such as DeepDream. RankBrain now handles a substantial number of search queries, replacing and supplementing traditional static algorithm-based search results.

TensorFlow Current Uses

TensorFlow Current Uses
Deep Speech (Mozilla Speech Recognition) A TensorFlow implementation motivated by Baidu’s Deep Speech architecture.
Inception Image Classification Model (Google) Baseline model and follow on research into highly accurate computer vision models, starting with the model that won the 2014 Imagenet image classification challenge
Massively Multitask Networks for Drug Discovery (Google and Stanford University) Specialized in drug discovery. A deep neural network model for identifying promising drug candidates.
On-Device Computer Vision for OCR (Google) On-device computer vision model to do optical character recognition to enable real-time translation.
RankBrain (Google Information Retrieval) A large-scale deployment of deep neural nets for search ranking on www.google.com.
SmartReply (Google) Deep LSTM model to automatically generate email responses

Machine Learning Crash Course (MLCC)

On March 1, 2018, Google released its Machine Learning Crash Course (MLCC). Originally designed to help equip Google employees with practical artificial intelligence and machine learning fundamentals, Google rolled out its free TensorFlow workshops in several cities around the world before finally releasing the course to the public. To start the course, please check this page: Machine Learning Crash Course (MLCC).

TensorFlow AI Jobs

These are some positions which can help you have an idea what recruiters are looking for in terms of experience, coding language, education, certificate … etc.

TensorFlow AI Jobs
INDEED Indeed TensorFlow AI Jobs
Monster Monster TensorFlow AI Jobs

TensorFlow AI Videos

These are some videos related to TensorFlow and artificial intelligence. Enjoy!

TensorFlow Code Practice

Would you like to practice your coding skills? Here are some great resources:

TensorFlow Code Practice
Practice TensorFlow A Neural Network Playground – TensorFlow
Programming Exercises First Steps with TensorFlow

More information: TensorFlow offers a huge list of benefits to all. The usage of TensorFlow is such that it cannot be limited to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complex processes like Artificial Intelligence (AI), Machine Learning (ML), natural language processing, data science etc. We hope this page was helpful and provided you with some information about AI with TensorFlow. Check out our main page for more components of artificial intelligence resources.