🚀 Master the language of AI with our brand new course: "Prompt Engineering for Everyone" Learn more

Offered By: IBM

Deep Learning with TensorFlow

Majority of data in the world are unlabeled and unstructured data, for instance images, sound, and text data. Shallow neural networks cannot easily capture relevant structure in these kind of data, but deep networks are capable of discovering hidden structures within these data. In this course, you will use TensorFlow library to apply deep learning on different data types to solve real world problems.

Continue reading

Course

Machine Learning

10.9k+ Enrolled
4.6
(384 Reviews)

At a Glance

Majority of data in the world are unlabeled and unstructured data, for instance images, sound, and text data. Shallow neural networks cannot easily capture relevant structure in these kind of data, but deep networks are capable of discovering hidden structures within these data. In this course, you will use TensorFlow library to apply deep learning on different data types to solve real world problems.

About This Course

Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which consitutes the vast majority of data in the world.

TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Course Syllabus

Module 1 – Introduction to TensorFlow
  • HelloWorld with TensorFlow
  • Linear Regression
  • Nonlinear Regression
  • Logistic Regression
  • Activation Functions
Module 2 – Convolutional Neural Networks (CNN)
  • CNN History
  • Understanding CNNs
  • CNN Application
Module 3 – Recurrent Neural Networks (RNN)
  • Intro to RNN Model
  • Long Short-Term memory (LSTM)
  • Recursive Neural Tensor Network Theory
  • Recurrent Neural Network Model
Module 4 - Unsupervised Learning
  • Applications of Unsupervised Learning
  • Restricted Boltzmann Machine
  • Collaborative Filtering with RBM
Module 5 - Autoencoders
  • Introduction to Autoencoders and Applications
  • Autoencoders
  • Deep Belief Network

Recommended skills prior to taking this course

  • Neural Network

Requirements

  • Python programming

Course Staff

Dr. Saeed Aghabozorgi, TensorFlow Course Instructor
Saeed Aghabozorgi, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.

Course Development Team

Thanks to course developement team, interns and all individuals contributed to the development of this course: Kiran Mantri, Shashibushan Yenkanchi, Jag Rangrej, Naresh Vempala, Walter Gomes, Anita Vincent, Gabriel Sousa, Francisco Magioli, Victor Costa, Erich Sato, Luis Otavio and Rafael Belo.

Estimated Effort

3 Hours

Level

Beginner

Language

English

Course Code

ML0120EN

Released

November 19, 2020

Last Updated

April 28, 2022

Tell Your Friends!

Saved this page to your clipboard!

Sign up to our newsletter

Stay connected with the latest industry news and knowledge!