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Deep Learning

Offered By: Amazon via Udacity

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Deep Learning Courses Artificial Intelligence Courses PyTorch Courses Transfer Learning Courses Image Processing Courses Transformers Courses Autoencoders Courses

Course Description

Overview

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

Syllabus

  • 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.
  • 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.
  • 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.
  • RNNs and Transformers
    • This course covers multiple RNN architectures and discusses design patterns for those models. You'll also learn about transformer architectures.
  • 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.
  • Congratulations!
    • Congratulations on finishing your program!
  • Career Services

Taught by

Mat Leonard, Luis Serrano, Cezanne Camacho, Alexis Cook, Jennifer Staab, Sean Carrell, Ortal Arel, Jay Alammar, Vyom S., Peter L., Nohemy V., Sebastian P., Karim B. and Harshit A.

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