What is Tensorflow with Examples
- Learn TensorFlow - From Scratch
Learn TensorFlow - From Scratch
TensorFlow is an open-source software library to Facilitate Machine Learning for dataflow programming across a range of tasks. It is a symbolic math library and also used for machine learning applications such as neural networks.
Quick Facts about Tensorflow
- It’s an open-source software library, popularly known as Deep Learning technology
- Coded in Python Language
- Used by Google across its product range like speech recognition systems, Gmail, Search and other for learning
- Its used in perceptual and language understanding tasks
Watch a video about Tensorflow
Tensorflow is an open source software library from Google for numerical computation using data flow graphs. The central unit of data in a Tensorflow is called the Tensor. To have a proper understanding about Tensors you need to know that tensors provide a natural and compact mathematical framework for formulating and solving problems in areas of elasticity, fluid mechanics, and general relativity. And a tensor is something which is a generalization of vectors and matrices. A single number is a 0th order tensor; a vector is a 1st order tensor, a model is a 2nd order tensor, and so on.
Its intended application is in neural networks, but in fact, it’s quite more general than that.
Well, a Tensorflow is a vector representing the array of numbers Insert vectors wherever you wish to. The Vector can be put on from top to bottom, i.e., from head to tail. You don’t have to put in the efforts in invoking in the actual nos. The matrix has been organized or added with the Tensor just mainly to coordinate with the system. Tensor is overly used and involved to highlight or to mean multidimensional array.
Applications of Tensorflow
These are few of the fields our analysts picked up from the tensorflow.org to indicate the areas/places where TensorFlow is in an application:
|Rank Brain||Information Retrieval||A large-scale deployment of deep neural nets for search ranking on www.google.com||“Google Turning Over Its Lucrative Search to AI Machines”|
|Inception Image Classification Model||Baseline model and follow on research into highly accurate computer vision models, starting with the model that won the 2014 ImageNet image classification challenge||Baseline model described in Arxiv paper|
|SmartReply||Deep LSTM model to automatically generate email responses||Google research blog post|
|Massively Multitask Networks for Drug Discovery||Google and Stanford University||Drug discovery||A deep neural network model for identifying promising drug candidates.||Arxiv paper|
|On-Device Computer Vision for OCR||On-device computer vision model to do optical character recognition to enable real-time translation.||Google Research blog post|
Machine Learning continues to evolve at a faster pace, and it even introduces us to more advanced algorithms and branches of it such as Deep Learning.
Deep Learning works on using Neural Networks that is how a human brain functions. The method is basically based on recognizing the information, data, command given to the machine, it recognizes what the data is trying to represent and learn the patterns.
1. Voice/Speech or Speaker Recognition
- Voice recognition – Applied and used mostly in IoT, Automotive, Security and UX/UI
- Voice search – Applied and used mostly in Telecoms, Handset Manufacturers
- Sentiment Analysis – Applied and used mostly in CRM
- Flaw Detection (engine noise) – Applied and used mostly in Automotive and Aviation
2. Text-Based User Interface Applications:
Text-Based Applications indicate that the Tensor Flow even helps in detecting the language. And language detection is one of the most powerful functions popularly used in Text based Applications.
The basic examples are:
– Text Summarization
– Google Translate
3. Image Recognition
The feature, Image recognition, the Social Media. What happens when a snap is clicked and uploaded. How the system is able to recognize the face of somebody or a unique pattern after it has observed it multiple times on its screen.
Machine Vision, Search an Image, detecting its motion and then Face recognition, Telecom, Handset Manufacturers and Photo clustering could be really helpful and advantageous in the Healthcare, Automotive, and Aviation based Industry.
What basically happens with the Image Recognition feature is that the device is able to do the identification and recognition of the image and the object in the picture by understanding its concept in whole.
4. Analysis of Time Series Components:
Company these days are very smart. They are mainly focusing on their Customer behavior, the shifts in Customer Behavior and the patterns and deals which excite them.
Companies like Amazon, Netflix, Flipkart and follow the tactics of Time Series. They have already analyzed what do the buyers like to purchase or what do the viewers want to watch.
They will then give goodies or put a discount on such items for the customers having maximum interest in a particular item. You get to watch a few episodes of a season for free just for trial. This creates a sense of liking for the product, item or the Show or videos and the user will end up taking it in future.
There are various other uses of the Time Series feature of algorithms under Tensorflow. These are mainly evident in the field of Finance, Government, Accounting, Security, and many Risk Detection techniques, the application of Predictive Analysis and Enterprise/Resource Planning.
5. Moving object detection and tracking in Videos:
The Neural Network of TensorFlow just doesn’t go well for the image Recognition or detection but is even applied in the Video Detection while working on video data. So, with this feature, the data of the video can be recognized.
Supposedly working on the finding of some tech information or a learning path, the video detection will help in recognizing the understanding of the requirements and will import in the same information to the users. This might relevantly happen on YouTube or other such Videograms and Portals.
Video Detection is mainly used in Real-Time Thread Detection in Gaming, Security, Motion Detection, Airports and even in the UX/UI fields.
Things you should know before you start Learning Tensorflow
To be able to access and understand the tool Tensor Flow, you need not be an Expert in Tensorflow or Python. The basic requirement which a person needs to have is the basic understanding of any one Programming Language. How to program in Python. A bit about arrays. A person should be able to understand Basic Math and Statistics (mean, standard deviation, etc.). The most important one to expertise in this is the passion. A person, the user or the learner should be enthusiastic and willing to learn about Tensor Flow.
If your system is not going to have the Python installed then it is recommended to install the later mentioned Python Version in your system.
The Python versions that your System compulsorily needs to have before installing TensorFlow are as following:
Linux | Mac– 2.7, 3.3, or later
Windows- 3.5 or later