TensorFlow - the advance machine learning software framework
What is TensorFlow?
TensorFlow is an advanced machine learning software framework that helps to calculate complex mathematics by representing them as data flow graphs. It is an open source software library and most popular machine learning Github repository which was originally developed by researchers and engineers at Google Brain within Google’s Machine Intelligence research organization for the purposes of machine conducting learning and deep neural networks research.
There has a huge difference between traditional programming and machine learning. In traditional programming - we write code that tells computers “how to do a task” but, in case of machine learning - we don't explicitly tell the computers how to do. Instead, we train then with data then machine learning algorithm uses the training data and come up with its own rules to complete a task.
TensorFlow doesn't exactly give every developer the ability to harness machine learning, but it does provide both a Python and C/C++ API to link into a developer’s program. It has a unique way of solving problems. This unique way allows for solving machine learning problems very efficiently. The recently TensorFlow Object Detection API has produced state-of-the-art results.
TensorFlow was first released in November 2015 to make it easier for developers to design, build, and train deep learning models. In November 2017, Google also launched TensorFlow Lite version of developer preview and aimed at building a machine learning solutions directly for mobile and embedded IoT devices. The lightweight version of TensorFlow promises fast performance on small devices and works across iOS and Android devices. It does by utilizing the graphics processing unit and also making it easy to distribute the work across multiple GPUs and computers.
Some notable features of Tensorflow
One of the very important Tensorflow Features is that it is flexible in its operability, meaning it has modularity and the parts of it which you want to make standalone, it offers you that option.
With TensorFlow, you can easily visualize each and every part of the graph which is not an option while using Numpy or SciKit.
Parallel Neural Network Training
TensorFlow offers to a pipeline in the sense that you can train multiple neural networks and multiple GPUs which makes the models very efficient on large-scale systems.
The best thing about this machine learning library is that it is open source so anyone can use it as long as they have internet connectivity. So, people manipulate the library in ways unimaginable and come up with an amazing variety of useful products, it has become another DIY community which has a huge forum for people getting started with it and for those who find it hard to use it or to get help with their work.
Tensorflow has feature columns that could be thought of as intermediaries between raw data and estimators, therefore, bridging input data with your model.
It has been developed by Google, so there already is a large team of software engineers who work on stability improvements continuously.
So, where you gonna use TensorFlow?
To import or generate data
Machine learning algorithms will depend on data. This data will either generate or use an outside source. So, TensorFlow comes preloaded with famous datasets like MNIST, CIFAR-10, etc which we need. TensorFlow provides different types of nodes for a variety of tasks. Each node takes zero or more tensors as inputs and produces a tensor as an output.
To transform and normalize the data
Sometimes data will need to transform because we don’t use directly. TensorFlow has built in functions that can normalize the data for you. The Object Detection API code is designed to support transfer learning as well.
Using on mobile devices
TensorFlow is working to help developers to make lean mobile apps, by continuing to reduce the code footprint or by supporting quantization. TensorFlow is also for developing the design to provide speed and accuracy that need to be considered on mobile devices or in embedded applications. The newer Android demos, TF Detect, also trained by using the TensorFlow Object Detection API.
The most important uses of TensorFlow are text-based applications such as sentimental analysis (CRM, Social Media), Threat Detection (Social Media, Government) and Fraud Detection (Insurance, Finance). Language Detection is one of the most popular uses of text-based applications. Google also found out that for shorter texts, summarization can be learned with a technique called sequence-to-sequence learning. This can be used to produce headlines for news articles. Another Google use case is SmartReply. It automatically generates email responses.
Mostly used by Social Media, Telecom and Handset Manufacturers; Face Recognition, Image Search, Motion Detection, Machine Vision and Photo Clustering can be used also in Automotive, Aviation, and Healthcare Industries. Image Recognition aims to recognize and identify people and objects in images as well as understanding the content and context. TensorFlow object recognition algorithms classify and identify arbitrary objects within larger images. This is usually used in engineering applications to identify shapes for modeling purposes (3D space construction from 2D images) and by social networks for photo tagging.
One of the most well-known uses of TensorFlow is Sound based applications. With the proper data feed, neural networks are capable of understanding audio signals. Language understanding is another common use case for Voice Recognition. Speech-to-text applications can be used to determine snippets of sound in greater audio files, and transcribe the spoken word as text. Sound based applications also can be used in CRM. A use case scenario might be: TensorFlow algorithms standing in for customer service agents, and route customers to the relevant information they need, and faster than the agents.
TensorFlow neural networks also work on video data. This is mainly used in Motion Detection, Real-Time Thread Detection in Gaming, Security, Airports, and UX/UI fields. Recently, Universities are working on Large scale Video Classification datasets like YouTube-8M aiming to accelerate research on large-scale video understanding, representation learning, noisy data modeling, transfer learning, and domain adaptation approaches for video.
We don’t have to forget that The TensorFlow ecosystem also provides many tools and libraries to help you work more effectively. Such as -
You can use TensorBoard to view your TensorFlow model graphs and zoom in on the details of graph subsections. TensorBoard can be used to compare training runs, collect runtime stats, and generate histograms.
If using Google Cloud Storage or BigQuery for datasets, or Apache Beam for data preprocessing then Google Cloud Datalab provides a Jupyter-based environment with these tools like - NumPy, pandas, scikit-learn, and Matplotlib. And all these are along with TensorFlow, preinstalled and bundled together. Datalab is open source, so it can be possible to modify its notebook environment and it's too easy to do that.
Facets is a recently released open source data visualization tool which helps to understand a machine learning datasets, get the sense and see at a glance how the features interact with each other.
The TensorFlow debugger provides a view of the internal structure and states of running TensorFlow graphs during training and inference.