Deep Learning with TensorFlow

Overview

In this Deep Learning course with TensorFlow training, participants will become familiar with the language and fundamental concepts of artificial neural networks, PyTorch, autoencoders, and more. Upon completion, they will be able to build deep learning models, interpret results, and build your own deep learning project.

Objectives

At the end of Deep Learning with TensorFlow training course, participants will be able to

• Understand the concepts of Keras and TensorFlow, its main functions, operations, and the execution pipeline
• Implement deep learning algorithms, understand neural networks, and traverse the layers of data abstraction
• Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks, and high-level interfaces
• Build deep learning models using Keras and TensorFlow frameworks and interpret the results
• Understand the language and fundamental concepts of artificial neural networks, application of autoencoders, and Pytorch and its elements
• Troubleshoot and improve deep learning models
• Build your own deep learning project
• Differentiate between machine learning, deep learning, and artificial intelligence

Prerequisites

Participants in this training should have familiarity with programming fundamentals, a fair understanding of the basics of statistics and mathematics, and a good understanding of machine learning concepts.

Course Outline