本帖最后由 cccpwx 于 2016-1-31 23:46 编辑 |
Prerequisites and Requirements
This is an intermediate to advanced level course. Prior to taking this course, and in addition to the prerequisites and requirements outlined for the Machine Learning Engineer Nanodegree program, you should possess the following experience and skills:
Minimum 2 years of programming experience (preferably in Python)
Git and GitHub experience (assignment code is in a GitHub repo)
Basic machine learning knowledge (especially supervised learning)
Basic statistics knowledge (mean, variance, standard deviation, etc.)
Linear algebra (vectors, matrices, etc.)
Calculus (differentiation, integration, partial derivatives, etc.)
Lesson 1: From Machine Learning to Deep Learning
Understand the historical context and motivation for Deep Learning.
Set up a basic supervised classification task and train a black box classifier on it.
Train a logistic classifier “by hand”Optimize a logistic classifier using gradient descent, SGD, Momentum and AdaGrad.
Lesson 2: Deep Neural Networks
Train a simple deep network.
Effectively regularize a simple deep network.
Train a competitive deep network via model exploration and hyperparameter tuning.
Lesson 3: Convolutional Neural Networks
Train a simple convolutional neural net.
Explore the design space for convolutional nets.
Lesson 4: Deep Models for Text and Sequences
Train a text embedding model.
Train a LSTM model.