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Microcredential

Deep Learning

Amazon , Amazon Web Services and Facebook via Udacity Nanodegree

Overview

In this program, you’ll cover Convolutional and Recurrent Neural Networks, Generative Adversarial Networks, Deployment, and more. You’ll use PyTorch, and have access to GPUs to train models faster. You'll learn from authorities like Sebastian Thrun, Ian Goodfellow, Jun-Yan Zhu, and Andrew Trask. This is the ideal point-of-entry into the field of AI.
Deep learning is driving advances in artificial intelligence that are changing our world. Enroll now to build and apply your own deep neural networks to produce amazing solutions to important challenges.

Syllabus

Prerequisite Knowledge

This program has been created specifically for students who are interested in machine learning, AI, and/or deep learning, and who have a working knowledge of Python programming, including NumPy and pandas. Outside of that Python expectation and some familiarity with calculus and linear algebra, it's a beginner-friendly program.See detailed requirements.

  • Introduction

    Get your first taste of deep learning by applying style transfer to your own images, and gain experience using development tools such as Anaconda and Jupyter notebooks.

  • Neural Networks

    Learn neural networks basics, and build your first network with Python and NumPy. Use the modern deep learning framework PyTorch to build multi-layer neural networks, and analyze real data.

    Predicting Bike-Sharing Patterns
  • Convolutional Neural Networks

    Learn how to build convolutional networks and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them. Use these networks to learn data compression and image denoising.

    Dog-Breed Classifier
  • Recurrent Neural Networks

    Build your own recurrent networks and long short-term memory networks with PyTorch; perform sentiment analysis and use recurrent networks to generate new text from TV scripts.

    Generate TV scripts
  • 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.

    Generate Faces
  • Deploying a Sentiment Analysis Model

    Train and deploy your own PyTorch sentiment analysis model. Deployment gives you the ability to use a trained model to analyze new, user input. Build a model, deploy it, and create a gateway for accessing it from a website.

    Deploying a Sentiment Analysis Model

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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Reviews

4.6 rating, based on 13 reviews

Start your review of Deep Learning

  • Anonymous
    Could use a lot of work: not yet worth the money A lot of effort has clearly gone into this course and I believe it may be worthwhile doing at some point, but unfortunately isn't yet worthwhile. The materials are a mix-up of different components merged...
  • Good start point
    The course is a good start point in Deep Learning, but I feel that it could have more theory on some topics. It uses TensorFlow in almost all projects.
  • Anonymous
    BEST INTRO TO DEEP LEARNING BEST INTRO FOR PEOPLE WITH LITTLE OR NO DEEP LEARNING EXPERIENCE START The best thing about the course is the first section. It takes a simple prediction problem, "If grades= something, age=something then will X get accepted...
  • Michael Gruben-trejo
    Good Intro for people with Little or no Deep Learning Experience This ND starts by covering the mathematical foundations of Deep Learning, then moves you through some interesting types of Deep Learning networks and their applications. The ND uses TensorFlow...
  • Anonymous
    Good stepping stone to a deep learning career.
    The lectures are more breadth-focused than depth. Acts as a good introduction to a lot of the deep learning concepts. It is left on the student to explore all the concepts in greater detail, which is mandatory if anyone wants to make a career in this field. The slack channel is an amazing place to communicate with other DL enthusiasts and the office hours with experts gives an insight into how people are actually applying neural nets in research. The projects and mini-projects are the best part of the course as they'll really give your resume a boost if you work hard on them. Overall, highly recommended.
  • Anonymous
    Good starting point for deep learning
    This course will teach you all the basics and some advance methods of deep learning. I highly recommend it if you're interested in machine learning in general.
    Projects are well designed although a little cookie-cutter. The course uses Python which is easy to learn if you haven't used it before. A bit of knowledge about machine learning helps breeze trough the content and understand more advance concepts, although it's not required.
  • Anonymous
    Good experience overall, but intensive!
    It's a great overview of deep learning, with significant project experience in TensorFlow and numpy. My only complaint is that it took much more time each week than initially advertised. The projects were really helpful.
  • A great way to learn deep learning
    The course does a wonderful job of keeping you motivated and helping you along the way. The production quality is second to none, and you learn everything from the ground up.
  • Ravi Chandra
    Best course available out there!
    With the new added content deep learning nanodegree can be very powerful in terms of learning. Got a job just one week after graduating from it as a fresher. So much to learn.
  • Anonymous
    Very good introduction to Deep Learning
  • Project-oriented
  • Anonymous


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