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Deep Learning with TensorFlow - Welcome Video Lecture - Tensorflow Learning
Text Transcript from Video
Hello, and welcome to 'Deep Learning with
TensorFlow'.
Throughout the five modules of this course,
we're going to show you how you can use Google's
open source TensorFlow library for your deep
learning applications.
In module 1, after introducing you to the
TensorFlow library and walking you through
a 'hello, world' example, we'll go over a
few basic machine learning algorithms.
This will include linear regression, nonlinear
regression, and logistic regression.
In addition, we'll cover the different activation
functions provided by the library.
In module 2, we'll introduce you to the convolutional
neural network, a powerful model that's capable
of object recognition.
Like the brain, it works by passing the inputs
through a sequence of increasingly complex
layers.
We'll explain the convolution operation in
detail, and then we'll show you how to build
a convolutional net with TensorFlow, in order
to recognize handwritten digits.
In module 3, we'll provide an overview of
sequential data and recurrent neural networks,
as well as the long short-term memory model.
We'll also go over recursive neural tensor
networks and a few natural language processing
applications.
In module 4, we'll introduce you to unsupervised
learning.
Our main focus will be on the restricted Boltzmann
machine, which detects patterns by reconstructing
its input.
After creating and training a Restricted Boltzmann
machine in TensorFlow, we'll show you how
to build a movie recommendation system.
Module 5 will explain the concept of an autoencoder,
an unsupervised learning model for detecting
patterns.
The module includes a TensorFlow implementation
of an autoencoder, as well as an implementation
of a Deep Belief Network.
There's a lot to go over, but after completing
this course, you'll be well on your way to
using TensorFlow in your own applications.
Thanks for watching, and we hope you'll enjoy
'Deep Learning with TensorFlow'!
TensorFlow'.
Throughout the five modules of this course,
we're going to show you how you can use Google's
open source TensorFlow library for your deep
learning applications.
In module 1, after introducing you to the
TensorFlow library and walking you through
a 'hello, world' example, we'll go over a
few basic machine learning algorithms.
This will include linear regression, nonlinear
regression, and logistic regression.
In addition, we'll cover the different activation
functions provided by the library.
In module 2, we'll introduce you to the convolutional
neural network, a powerful model that's capable
of object recognition.
Like the brain, it works by passing the inputs
through a sequence of increasingly complex
layers.
We'll explain the convolution operation in
detail, and then we'll show you how to build
a convolutional net with TensorFlow, in order
to recognize handwritten digits.
In module 3, we'll provide an overview of
sequential data and recurrent neural networks,
as well as the long short-term memory model.
We'll also go over recursive neural tensor
networks and a few natural language processing
applications.
In module 4, we'll introduce you to unsupervised
learning.
Our main focus will be on the restricted Boltzmann
machine, which detects patterns by reconstructing
its input.
After creating and training a Restricted Boltzmann
machine in TensorFlow, we'll show you how
to build a movie recommendation system.
Module 5 will explain the concept of an autoencoder,
an unsupervised learning model for detecting
patterns.
The module includes a TensorFlow implementation
of an autoencoder, as well as an implementation
of a Deep Belief Network.
There's a lot to go over, but after completing
this course, you'll be well on your way to
using TensorFlow in your own applications.
Thanks for watching, and we hope you'll enjoy
'Deep Learning with TensorFlow'!
About this Video
Apr 18, 2026 Last updated
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