Deep Learning

Online Course

Udacity
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Deep Learning
  1. What is the course about?
  2. Prerequisites & Facts
  3. Cost, Certificate & Degree
  4. Reviews & Experiences

What is the course about?

Deep Learning
The course Deep Learning is an online class provided by Udacity. It may be possible to receive a verified certification or use the course to prepare for a degree.

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 challenges like image classification and generation, time-series prediction, and model deployment.

Course description
  • Deep Learning
  • 4 months (12 hrs/week)
  • Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website.
  • Master building and implementing neural networks for image recognition, sequence generation, image generation, and more.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • You’ll need intermediate experience with Python to start this program. Some basic knowledge of machine learning is beneficial, although not required, to start this program. Prepare now with AI Programming with Python.
  • You’ll need intermediate experience with Python to start this program. Some basic knowledge of machine learning is beneficial, although not required, to start this program. Prepare now with AI Programming with Python.
  • 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
  • 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.
  • In this program, you’ll master deep learning fundamentals that will prepare you to launch or advance a career, and additionally pursue further advanced studies in the field of artificial intelligence. You will study cutting-edge topics such as neural, convolutional, recurrent neural, and generative adversarial networks, as well as sentiment analysis model deployment. You will build projects in Keras and NumPy, in addition to TensorFlow PyTorch. You will learn from experts in the field, and gain exclusive insights from working professionals. For anyone interested in building expertise with this transformational technology, this Nanodegree program is an ideal point-of-entry.
  • This program is designed to build on your skills in deep learning. As such, it doesn’t prepare you for a specific job, but expands your skills in the deep learning domain. These skills can be applied to various applications and also qualify you to pursue further studies in the field.
  • If you are interested in the fields of artificial intelligence and machine learning, this Nanodegree program is the perfect way to get started!
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Prerequisites & Facts

Deep Learning

Course Topic

Career Development, Personal Development

University, College, Institution

Udacity

Course Skill Level

see course summary

Course Language

English

Place of class

Online, self-paced (see curriculum for more information)

Degree

Certificate

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Degree & Cost

Deep Learning

To obtain a verified certificate from Udacity you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate (399 USD) or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.
Udacity
Deep Learning
provided by Udacity
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Reviews

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Here you can find information, reviews and user experiences for the course “Deep Learning“. The provider of the course – “Udacity” – will be glad to answer any questions you may have about the class, click here to use the offical support channels. It would be great if you could share your experience of participating in the course – Your honest review will surely help others to choose the right class!
School: Udacity
Topic: Computer Science, Deep Learning

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