Deep Learning v14.0.5

Nanodegree key: nd101

Version: 14.0.5

Locale: en-us

This Nanodegree trains the learner about foundational topics in the exciting field of deep learning, the technology behind state-of-the-art artificial intelligence.

Content

Part 01 : Welcome to the Deep Learning Nanodegree Program

The Deep Learning Nanodegree program offers a solid introduction to the world of artificial intelligence. In this program, you’ll master fundamentals that will enable you to go further in the field, launch or advance a career, and join the next generation of deep learning talent that will help define a beneficial, new, AI-powered future for our world. You will study cutting-edge topics such as Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, and build projects in PyTorch.

Module 01: Welcome to the [NAME] Nanodegree Program
- Lesson 01: An Introduction to Your Nanodegree Program
  Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.
- Lesson 02: Getting Help
  You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Part 02 : Introduction to Deep Learning

This course covers foundational deep learning theory and practice. We begin with how to think about deep learning and when it is the right tool to use. The course covers the fundamental algorithms of deep learning, deep learning architecture and goals, and interweaves the theory with implementation in PyTorch.

Module 01: Intro to Deep Learning
- Lesson 01: Introduction to Deep Learning
  Meet your instructor, get an overview of the course, and find a few interesting resources in this introductory lesson.
- Lesson 02: Deep Learning
  This introductory lesson on Deep Learning covers how experts think about deep learning and how to know when deep learning is the right tool for the job, including some examples.
- Lesson 03: Minimizing Error Function with Gradient Descent
  Beginning with PyTorch and moving into both Error Functions, Gradient Descent, and Backpropagation, this lesson provides an overview of foundational neural network concepts.
- Lesson 04: Intro to Neural Networks
  This introduction to neural networks explains how algorithms inspired by the human brain operate and puts to use those concepts when designing a neural network to solve particular problems.
- Lesson 05: Training Neural Networks
  Learn how to train neural networks and avoid overfitting or underfitting by employing techniques like Early Stopping, Regularization, Dropout, Local Minima, and Random Restart!
- Lesson 06: Developing a Handwritten Digits Classifier with PyTorch
  In this project, you will use your skills in designing and training neural networks to classify handwritten digits using the well-known MNIST dataset.
  
  Project Description - Developing a Handwritten Digits Classifier with PyTorch Project
  
  Project Rubric - Developing a Handwritten Digits Classifier with PyTorch Project

Part 03 : Convolutional Neural Networks

This course introduces Convolutional Neural Networks, the most widely used type of neural networks specialized in image processing. You will learn the main characteristics of CNNs that make them so useful for image processing, their inner workings, and how to build them from scratch to complete image classification tasks. You will learn what are the most successful CNN architectures, and what are their main characteristics. You will apply these architectures to custom datasets using transfer learning. You will also learn about autoencoders, a very important architecture at the basis of many modern CNNs, and how to use them for anomaly detection as well as image denoising. Finally, you will learn how to use CNNs for object detection and semantic segmentation.

Module 01: CNNs
- Lesson 01: Introduction to CNNs
  In this lesson we will look at the main applications of CNNs, understand professional roles involved in the development of a CNN-based application, and learn about the history of CNNs.
- Lesson 02: CNN Concepts
  In this lesson we will recap how to use a Multi-Layer Perceptron for image classification, understand the limitations of this approach, and learn how CNNs can overcome these limitations.
- Lesson 03: CNNs in Depth
  In this lesson we will study in depth the basic layers used in CNNs, build a CNN from scratch in PyTorch, use it to classify images, improve its performance, and export it for production.
- Lesson 04: Transfer Learning
  In this lesson we will learn about key CNN architectures and their innovations, and apply multiple ways of adapting them to our use cases with transfer learning.
- Lesson 05: Autoencoders
  In this lesson we will design and train linear and CNN-based autoencoders for anomaly detection and for image denoising.
- Lesson 06: Object Detection and Segmentation
  In this lesson we will study applications of CNNs beyond image classification. We will train and evaluate an object detection model as well as a semantic segmentation model on custom datasets.
- Lesson 07: Landmark Classification & Tagging for Social Media
  In this project, you will apply the skills you have acquired in the Convolutional Neural Network (CNN) course to build a landmark classifier.
  
  Project Description - Landmark Classification & Tagging for Social Media Project
  
  Project Rubric - Landmark Classification & Tagging for Social Media Project

Part 04 : RNNs & Transformers

This course covers multiple RNN architectures, discusses design patterns for those models, and arrives at the latest Transformer architectures.

Module 01: RNNs

Part 05 : Building Generative Adversarial Networks

Learn to understand and implement a Deep Convolutional GAN (generative adversarial network) to generate realistic images, with Ian Goodfellow, the inventor of GANs, and Jun-Yan Zhu, the creator of CycleGANs.

Module 01: GANs
- Lesson 01: Introduction to Generative Adversarial Networks
  Introduction to this course, prerequisites, and your course instructor.
- Lesson 02: 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 03: Training a Deep Convolutional GANs
  In this lesson, you'll implement a Deep Convolution GAN to generate complex color images.
- Lesson 04: Image to Image Translation
  Jun-Yan Zhu, one of the creators of the CycleGAN, will lead you through Pix2Pix and CycleGAN formulations that learn to do image-to-image translation tasks.
- Lesson 05: Modern GANs
  In this lesson, you will implement more advanced GAN architectural techniques that have had a significant impact on the realism of generated images.
- Lesson 06: Face Generation
  Define two adversarial networks, a generator, and a discriminator, and train them until you can generate realistic faces.
  
  Project Description - Face Generation Project
  
  Project Rubric - Face Generation Project

Part 06 : Career Services

Module 01:
- Lesson 01: Take 30 Min to Improve your LinkedIn
  Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.
  
  Project Description - Improve Your LinkedIn Profile
  
  Project Rubric - Improve Your LinkedIn Profile
- Lesson 02: Optimize Your GitHub Profile
  Other professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.
  
  Project Description - Optimize Your GitHub Profile
  
  Project Rubric - Optimize Your GitHub Profile

Part 07 : Congratulations!

Congratulations on finishing your program!

Module 01: Congratulations!
- Lesson 01: Congratulations!
  Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.