Top 10 Machine Learning Frameworks

When delving into the world of machine learning (ML), choosing one framework from many alternatives can be an intimidating task. You might already be familiar with the names, but it’s useful to evaluate the options during the decision-making process. There are different frameworks, libraries, applications, toolkits, and datasets in the machine learning world that can be very confusing, especially if you’re a beginner. Being accustomed to the popular ML frameworks is necessary when it comes to choosing one to build your application. This is why we compiled a list of the top 10 machine learning frameworks.

1. Tensorflow

Tensorflow was developed by the Google Brain Team for different language understanding and perceptual tasks. This open source framework is being used for extensive research on deep neural networks and machine learning. Being the second machine learning framework by Google Brain, it is compatible with most new CPUs and GPUs. Many of the popular Google services that we use on a daily basis such as Gmail, Speech recognition, Google Photos and even Google Search are equipped with Tensorflow.

Tensorflow uses data flow graphs to perform complicated numerical tasks. The mathematical computations are elaborated using a directed graph containing edges and nodes. These nodes are used to implement the operations and can also act as the endpoints where data is fed. The edges also represent the input/output associations between different nodes.

2. Caffe

Caffe is a machine learning framework that was designed with better expression, speed, and modularity as the focus points. It was developed for computer vision/image classification by leveraging Convolutional Neural Networks(CNNs). Caffe is popular for its Model Zoo, which is a set of pre-trained models that doesn’t require any coding to implement. Continue reading…