Machine learning, a subfield of AI, has become a crucial component of developing tools and applications for data analysis and decision-making in the digital age.
Taking steps to fight overfitting is necessary to develop predictive models that make accurate predictions on new data, especially when using complex models like neural networks or decision trees.
Data is a primary component in innovation and the transformation of today’s enterprises. But developing an appropriate data strategy is not an easy task, as modernizing and optimizing data architectures requires highly skilled teams.
Modern machine learning methods have been around for more than 10 years, but now there is a trend to add machine learning to a wider range of applications including medicine, agriculture, and even IoT devices.
TensorFlow is a Machine Learning cross-platform that has started to be adopted widely worldwide. It was released by Google in 2015 and now TensorFlow 2.0 Alpha is available.
The scope of machine learning with Apple is just beginning to be imagined. The number of applications has increased at a humongous rate in the last years. Nowadays, almost all activity that includes data user analysis relies in machine learning.