Deep Learning with Tensorflow (edX)

Much of the world's data is unstructured. Think images, sound, and textual data. Learn how to apply Deep Learning with TensorFlow to this type of data to solve real-world problems. 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.

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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 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.

This course is part of the Deep Learning Professional Certificate Program.

What you'll learn

- 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.

Syllabus

Module 1 – Introduction to TensorFlow

* HelloWorld with TensorFlow

* Linear Regression

* Nonlinear Regression

* Logistic Regression

Module 2 – Convolutional Neural Networks (CNN)

* CNN Application

* Understanding CNNs

Module 3 – Recurrent Neural Networks (RNN)

* Intro to RNN Model

* Long Short-Term memory (LSTM)

Module 4 - Restricted Boltzmann Machine

* Restricted Boltzmann Machine

* Collaborative Filtering with RBM

Module 5 - Autoencoders

* Introduction to Autoencoders and Applications

* Autoencoders

* Deep Belief Network