About this Course

This course is a part of the Artificial Intelligence Nanodegree Program.

Skill Level
Advanced
Included in Course
  • Rich Learning Content

  • Interactive Quizzes

  • Taught by Industry Pros

  • Real World Projects

  • Student Support Community

  • Personalized Career Support

Join the Path to Greatness

This course is part of a Nanodegree Program. It is a step towards a new career in Artificial Intelligence.

Nanodegree Course

Artificial Intelligence - Deep Learning

Enhance your skill set and boost your hirability through innovative, independent learning.

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What You Will Learn

Lesson 1

Deep Neural Networks

  • Luis will give you solid foundations on Deep Learning, and teach you how to apply Neural Networks to analyze real data!
Lesson 1

Deep Neural Networks

  • Luis will give you solid foundations on Deep Learning, and teach you how to apply Neural Networks to analyze real data!
Lesson 2

Convolutional Neural Networks

  • Alexis explains the theory behind Convolutional Neural Networks and how they help us dramatically improve performance in image classification.
Lesson 2

Convolutional Neural Networks

  • Alexis explains the theory behind Convolutional Neural Networks and how they help us dramatically improve performance in image classification.
Lesson 3

CNN Project: Dog Breed Classifier

  • In this convolutional neural networks project, you will learn how to build a pipeline to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed.
Lesson 3

CNN Project: Dog Breed Classifier

  • In this convolutional neural networks project, you will learn how to build a pipeline to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed.
Lesson 4

Intro to TensorFlow : Autoencoders

  • Autoencoders are neural networks used for data compression, image denoising, and dimensionality reduction. In this lesson, Mat will teach how to build autoencoders using TensorFlow.
Lesson 4

Intro to TensorFlow : Autoencoders

  • Autoencoders are neural networks used for data compression, image denoising, and dimensionality reduction. In this lesson, Mat will teach how to build autoencoders using TensorFlow.
Lesson 5

Recurrent Neural Networks

  • Jeremy explains Recurrent Neural Networks, and their cutting edge applications to text-based sequence generation
Lesson 5

Recurrent Neural Networks

  • Jeremy explains Recurrent Neural Networks, and their cutting edge applications to text-based sequence generation
Lesson 6

Long Short-Term Memory Networks (LSTM)

  • Luis explains Long Short-Term Memory Networks (LSTM), and similar architectures which have the benefits of preserving long term memory.
Lesson 6

Long Short-Term Memory Networks (LSTM)

  • Luis explains Long Short-Term Memory Networks (LSTM), and similar architectures which have the benefits of preserving long term memory.
Lesson 7

Implementing RNNs and LSTMs

  • In this lesson, Mat will review the concepts of RNNs and LSTMs, and then you'll see how a character-wise recurrent network is implemented in TensorFlow.
Lesson 7

Implementing RNNs and LSTMs

  • In this lesson, Mat will review the concepts of RNNs and LSTMs, and then you'll see how a character-wise recurrent network is implemented in TensorFlow.
Lesson 8

Hyperparamaters

  • In this section, Jay will teach you about some important hyperparameters used for our deep learning work, including those used for Recurrent Neural Networks.
Lesson 8

Hyperparamaters

  • In this section, Jay will teach you about some important hyperparameters used for our deep learning work, including those used for Recurrent Neural Networks.
Lesson 9

Sentiment Prediction with RNN

  • In this lesson you'll implement a sentiment prediction RNN
Lesson 9

Sentiment Prediction with RNN

  • In this lesson you'll implement a sentiment prediction RNN
Lesson 10

RNN Project: Time Series Prediction and Text Generation

  • In this Recurrent Neural Network project you'll build RNNs that can generate sequences based on input data.
Lesson 10

RNN Project: Time Series Prediction and Text Generation

  • In this Recurrent Neural Network project you'll build RNNs that can generate sequences based on input data.
Lesson 11

Generative Adversarial Networks

  • Ian Goodfellow, the inventor of GANs, introduces you to these exciting models. You'll also implement your own GAN on the MNIST dataset.
Lesson 11

Generative Adversarial Networks

  • Ian Goodfellow, the inventor of GANs, introduces you to these exciting models. You'll also implement your own GAN on the MNIST dataset.
Lesson 12

Deep Convolutional GANs

  • In this lesson you'll implement a Deep Convolution GAN to generate complex color images of house numbers.
Lesson 12

Deep Convolutional GANs

  • In this lesson you'll implement a Deep Convolution GAN to generate complex color images of house numbers.
Lesson 13

Semisupervised Learning

  • Ian Goodfellow leads you through a semi-supervised GAN model, a classifier that can learn from mostly unlabeled data.
Lesson 13

Semisupervised Learning

  • Ian Goodfellow leads you through a semi-supervised GAN model, a classifier that can learn from mostly unlabeled data.

Prerequisites and Requirements

Differential Calculus, Linear Algebra, and Python

See the Technology Requirements for using Udacity.

Why Take This Course

In this deep learning course, you will learn about Convolutional Neural Networks, Recurrent Neural Networks, and Transfer Learning.

What do I get?
  • Instructor videos
  • Learn by doing exercises
  • Taught by industry professionals
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