What is TensorFlow?
A popular open-source framework for machine learning using data flow graphs, TensorFlow is utilized extensively by data scientists, software developers, and educators. Graph nodes stand for mathematical operations, and graph edges are tensors, which are multidimensional data arrays that connect these nodes.
- Machine learning algorithms can be represented as a graph of linked actions because to its adaptable design. You don’t need to rewrite any code to train or run them on GPUs, CPUs, and TPUs on a wide variety of platforms, from mobile devices to desktop computers to powerful servers.
- This allows programmers from diverse backgrounds to work together more efficiently by using the same tools.
- Although the Google Brain Team originally created the system to study deep neural networks (DNNs) and machine learning, it is now useful to many other fields due to its broad nature.
Features
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Easy model development
TensorFlow’s high-level APIs make it easy to construct Neural Network-based Machine Learning models.
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Complex Numeric Computations
The input dataset is extensive; therefore, mathematical computations are easy to do.
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Machine Learning APIs
TensorFlow has a plethora of low-level and high-level Machine Learning APIs. C and Python both have stable APIs.
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Simple installation and computation on CPU and GPU
TensorFlow is compatible with both CPU and GPU for model development and training. You can compare and contrast computations done on CPU and GPU.
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Contains pre-trained models
Google has invested a lot of time and effort into TensorFlow, so it comes with a plethora of pre-trained models and datasets.
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Embracing Keras
Keras is an extension of TensorFlow and Theano that provides access to TensorFlow’s high-level API. Keras is one of the most used TensorFlow APIs nowadays.
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Open Source
Building and deploying Machine Learning models has never been easier than with TensorFlow, an open-source platform that researchers and developers can utilize for free.
Use of TensorFlow
- Image and face recognition
- Health apps like cancer detection, etc.
- Recommender Systems
- Hire virtual assistants
- Self-driving cars
- Processing of Natural Language
Importance of TensorFlow for Your Needs
1. Software developers
Many popular operating systems and hardware platforms are compatible with TensorFlow. With the introduction of TensorFlow 2.0 in late 2019, deploying TensorFlow models on a broader variety of platforms is much easier. Installing models built using TensorFlow is simple because of how well they work together.
2. Data scientists
With TensorFlow’s flexible model development options, you can express your creative ideas and new algorithms with ease and pick the perfect tool for the job every time. As one of the most popular libraries for building ML models, TensorFlow makes it easy to locate code written by other researchers who are attempting to duplicate their work. Therefore, you won’t have to waste time writing boilerplate or repetitive code.
Pros & Cons
Pros
- Execution of low-level operations across many acceleration platforms
- Automatic computation of gradients
- Production-level scalability
- Interoperable graph exportation
- High-level API
- Eager execution
Cons
- Missing Symbolic Loops
- Too Many Frequent Updates
- Limited GPU Support
- Low Implementation Speed
- Homonym Inconsistency
- No supports for windows
Pricing & Plan
Free Plan
TensorFlow’s open-source nature and lack of cost make it possible to use all of its features and tools without any limitations.
FAQs
1. How does TensorFlow work?
For a wide variety of machine learning applications, including image and face recognition, NLP, recommender systems, and more, TensorFlow is the tool of choice.
2. Does TensorFlow support CPU and GPU?
Sure, TensorFlow can do calculations on both CPU and GPU, giving you more options and better performance.
3. Can a beginner use TensorFlow?
The combination of TensorFlow’s high-level APIs and thorough documentation makes it ideal for both novice and experienced programmers.
