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:

Introduction

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.

 

Use Kindle Unlimited to Read Coding Interview Prep Books for Free!

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.

https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101