Building Deep Learning Models with TensorFlow

Building Deep Learning Models with TensorFlowCertificate

IBM – Building Deep Learning Models with TensorFlow

About this Course

The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structures 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:

  • • explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines.
  • • describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.
  • • understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
  • • apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.


Boris Kisov


Innovation, IT & Management

10+ years of initiating and delivering sustained results and effective change for companies across a wide range of industries including
innovation, enterprise software, digital marketing, start-ups, advertising technology, e-commerce and government.