Exploring Tensorflow and Keras with TactLabs
Introduction
Hey there! This summer, I got the opportunity to take up a 50-day Learning Challenge with TactLabs and featurepreneur. The technologies I chose for this particular Learning Challenge were Tensorflow and Keras. Being a Machine Learning enthusiast, I really enjoyed learning some of the best Deep Learning Libraries out there and I would like to share my experience and knowledge on the technology with you.
Tensorflow
What is Tensorflow?
TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. Tensorflow can be used across a range of tasks but has a particular focus on the training and inference of deep neural networks.
Tensorflow is a symbolic math library based on dataflow and differentiable programming. It is used for both research and production at Google.
Features of Tensorflow
- Efficiently executing low-level tensor operations on CPU, GPU, or TPU.
- Computing the gradient of arbitrary differentiable expressions.
- Scaling computation to many devices (e.g. the Summit supercomputer at Oak Ridge National Lab, which spans 27,000 GPUs).
- Exporting programs (“graphs”) to external runtimes such as servers, browsers, mobile, and embedded devices.
Installing Tensorflow
TensorFlow is tested and supported on the following 64-bit systems:
- Python 3.6–3.8
- Ubuntu 16.04 or later
- Windows 7 or later
- macOS 10.12.6 or later.
To install TensorFlow, simply use,
pip install tensorflowor if you’re specific about a version, use one of the following
pip install tensorflow == <version>
pip install tensorflow >= <version>
pip install tensorflow <= <version>For example:
pip install tensorflow == 2.0
pip install tensorflow >= 2.0
pip install tensorflow <= 2.0Keras?
What is Keras?
Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research.
It is the high-level API of TensorFlow 2: an approachable, highly-productive interface for solving machine learning problems, with a focus on modern deep learning. It provides essential abstractions and building blocks for developing and shipping machine learning solutions with high iteration velocity.
Keras empowers engineers and researchers to take full advantage of the scalability and cross-platform capabilities of TensorFlow 2: you can run Keras on TPU or large clusters of GPUs, and you can export your Keras models to run in the browser or on a mobile device.
Features of Keras
- Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.
- This makes Keras easy to learn and easy to use. As a Keras user, you are more productive, allowing you to try more ideas than your competition, faster — which in turn helps you win machine learning competitions.
- This ease of use does not come at the cost of reduced flexibility: because Keras integrates deeply with low-level TensorFlow functionality, it enables you to develop highly hackable workflows where any piece of functionality can be customized.
Installing Keras
Installing Keras on your machine is just a command away
pip install kerasMore about Tensorflow and Keras
Tensorflow Documentation
To check out more about Tensorflow, follow the link below:
Keras Documentation
My Learning Documentation
Conclusion
Thanks for stopping by!! I would like to thank Featurepreneur and Tactlabs for encouraging me to learn On-Demand technologies such as Keras and Tensorflow!! Do check out my other articles where I cover topics such as deep learning and other trending technologies.
Stay Safe!! Happy Learning!!
You can check out my Linkedin at https://www.linkedin.com/in/fabchris10/
