Building Deep Learning Models with TensorFlow

Most of the data in the world are unlabeled or unstructured. Deep neural networks are unable to capture the relevant structure, such as images, sounds, and textual data. These types of data are best suited for deep networks, which can discover hidden structures. This course will teach you how to use TensorFlow library for deep learning on different data types to solve real-world problems.

Learning Outcomes TensorFlow is used for classification, regression, classification, and minimization error functions. Learn about the different types of Deep Architectures such as Convolutional, Recurrent, and Autoencoders. TensorFlow is used to adjust the biases and weights of the Neural Networks being trained.