Building Deep Learning Models with TensorFlow

The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems.
Learning Outcomes:
After completing this course, learners will be able to:
•\texplain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines.
•\tdescribe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.
•\tunderstand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
•\tapply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.

What skills will you gain?

Tensorflow

Deep Learning

Artificial Neural Networks

Convolutional Neural Network

Human Learning

Modeling

Machine Learning

Regression

PyTorch

Python Programming