ARTIBA curates a list of libraries from Python’s repository helpful for your machine learning projects. Take a look at the most commonly used libraries.
Over 137,000 libraries exist in Python’s repository. So, how do you choose the right one for your machine learning project? A cheat sheet on proven uses can help.
Nothing beats Python in finding solutions to complex mathematical and computational problems. It is a versatile language that can be easily used across domains and is easier to debug too.
45% of tech organizations use Python for their machine learning and AI projects. – Builtwith.com
Python libraries are a work in progress and their use cases and toolkits are continuously advancing. Therefore, AI engineers need to keep a constant tab on the latest developments, more so if they intend to use Python for their machine learning projects.
*Python can be used by beginners and experienced AI Engineers, which makes it a popular choice across levels. * Before we begin with the cheat sheet, please note that Python libraries can be multi-purpose and can be placed in multiple categories. Also, the use of libraries is not constrained to the highlighted tasks.
This cheat sheet aims to help you dig out the best fit.
Top Python Libraries for Deep Learning We’ve weighed the pros and cons and arrived at these four top picks.
TensorFlow A plug-and-play library with an extensive resource of commonly-used machine learning models and algorithms, TensorFlow comes with facial recognition capabilities. It is one of the biggest open-source Python libraries and a must-have for beginners.
Level: Good for beginners
Keras It runs on top of libraries like CNTK, Theano, and TensorFlow, and offers specific support to deep learning applications. Easier prototyping, modularity, and a user-friendly interface make it an excellent choice for beginners.
Level: Excellent choice for beginners
Keras vs Tensorflow - Learn the differences between Keras and Tensorflow on basis of Ease to use, Fast development,Functionality,flexibility,Performance etc
Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial: How to use Keras, a neural network API written in Python and integrated with TensorFlow. We will learn how to prepare and process data for artificial neural networks, build and train artificial neural networks from scratch, build and train convolutional neural networks (CNNs), implement fine-tuning and transfer learning, and more!
Learn Artificial Neural Networks (ANN) in Python. Build predictive deep learning models using Keras & Tensorflow| Python
This Keras Tutorial With TensorFlow will show you how to use Keras, which is a neural network API written in Python, and integrated with TensorFlow. We will learn how to prepare and process data for artificial neural networks, build and train artificial neural networks from scratch.
Learning AI/ML: The Hard Way. A learning guide on machine learning for beginners.