Sunday, November 12, 2017

Udacity Offers Free Preview of its Deep Learning Nanodegree

"What will you create?" with Deep Learning? Previously the deep learning foundation is taught by Youtube star Sraj Raval, quite a personality, look him up! Luis Serrano head of Machine Learning at Udacity is revamping the series with a course developer - Matt. Luis is a machine learning specialist PhD who has taught, done research and worked as a Google Engineer. Matt has used datas science and python for his PhD work. They just offered a free preview of this Nanodegree. Here are some reviews, observations and commentaries.

Luis, Matt
will guide you through this Udacity
Deep Learning Nanodegree process

You can meet the instructors. Learn about Neural Networks including:

  • Convolutional Networks
  • Recurrent Neural Networks
  • Generative Adversarial Networks
  • Deep Reinforcement Learning. 

Real world projects of this nanodegree:

  • Create original art like Picasso, Hokusai (Japanese wood print painting), using deep learning transfer learning (though you can do this right now. Google Developer demo shows you how)
  • Teach a car to navigate in simulated traffic (Deep Reinforcement Learning)
  • Train a gaming agent to play Flappy Bird
There will also be stories about Sebastian Thrun's work at Stanford skin cancer detection by Alexis Cook. Understand how Sebastian's team devised this new life saving algorithm. Technically after learning Convolutional Neural Network (CNN) you can analyze medical MRI, X-rays and more. 

Exposure to technologies:

  • Keras
  • Tensorflow
Exposure to experts:
  • Sebastian Thrun
  • Google AI, Google Brain
Information covered in this nanodegree: CNN, RNN, GAN, Reinforcement learning, projects. See the infograph below

Deep Learning Nanodegree has 5 parts:


Create art with transfer learning
Linear regression, machine learning

Neural Networks

Build simple neural networks from scratch using python 
Gradient Descent, backpropagation
Project 1, predict bike ridership using a NN
Model evaluation, validation, 
Guest instructor Andrew Trask author of Grokking Deep Learning
Predict text and predicting sentiment

Convolutional Networks

Computer vision
Build Convolutinal networks in Tensorflow
Project 2 use CNN to classify dog breeds
Build autoencoder with CNN
A network architecture for Image compression and denoising
Use pretrained neural network VGGnet to classify images of flowers the network has not seen, using transfer learning

Recurrent Neural Networks (RNN)

Transform sequences like text, music, time series data,
Build a RNN generate new text character by character
Natural language processing, Word embedding, Word2Vec model, Semantic relationship between words, 
Combine embedding and RNN to predict sentiment of movie reviews
Project 3: generate tv scripts from episodes of the simpsons

Generative Adversarial Networks (GANs)

Great for real-world data.
Generating image as in the CycleGAN project
Guest instructor Ian Goodfellow from CycleGAN implementation
semi supervised learning
training classifier with data with mostly missing abels. 
semi-supervised learning, a technique for training classifiers with data mostly missing labels.
project 4 use deep covolutional GAN to generate human faces.


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Deep Reinforcement Learning

Artificial intelligence
AlphaGo by DeepMind, Video gaming agent, robotics
Design agents that can learn to take actions in a simulated environment
Project 5 Deep Reinforcement Learning agent to control several quadcopter flying tasks, including take-off, hover, and landing.

Learning Resources and support: there will be community forum, a slack channel and a waffle board.

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