Running head: IPv4 vs IPv6 1
IPv4 vs IPv6
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IPV4 VS IPV62
IPv4 vs IPv6
IPV6 is
Recurrent nets and LSTM
Nando de Freitas
Outline of the lecture
This lecture introduces you sequence models. The goal is for you to
learn about:
Recurrent neural networks
The vanishing and exploding gradients problem
Long-short term memory (LSTM) netwo
Max-margin learning
Nando de Freitas
Outline of the lecture
Max margin learning is an extremely powerful idea for learning
features with auxiliary tasks, and then use these features to solve tasks
with few data. The goal of this lecture is for you to lear
Convnets
Nando de Freitas
Outline of the lecture
This lecture introduces you to convolutional neural networks. These
models have revolutionized speech and object recognition. The goal
is for you to learn
Convnets for object recognition and language
How
Neural networks
Nando de Freitas
Outline of the lecture
This lecture introduces you to the fascinating subject of classification
and regression with artificial neural networks. In particular, it
Introduces multi-layer perceptrons (MLPs)
Teaches you how
Nonlinear ridge regression
Risk, regularization, and cross-validation
Nando de Freitas
Outline of the lecture
This lecture will teach you how to fit nonlinear functions by using
bases functions and how to control model complexity. The goal is for
you to:
Maximum Likelihood
Nando de Freitas
Outline of the lecture
In this lecture, we formulate the problem of linear prediction using
probabilities. We also introduce the maximum likelihood estimate and
show that it coincides with the least squares estimate. Th
Optimization
Nando de Freitas
Outline of the lecture
Many machine learning problems can be cast as optimization problems.
This lecture introduces optimization. The objective is for you to learn:
The definitions of gradient and Hessian.
The gradient desc
Logistic regression: a simple ANN
Nando de Freitas
Outline of the lecture
This lecture describes the construction of binary classifiers using a
technique called Logistic Regression. The objective is for you to learn:
How to apply logistic regression to d
Backpropagation: A modular approach
(Torch NN)
Nando de Freitas
Outline of the lecture
This lecture describes modular ways of formulating and learning
distributed representations of data. The objective is for you to learn:
How to specify models such as l
Linear regression
Nando de Freitas
Outline
This lecture introduces us to the topic of supervised learning. Here the
data consists of input-output pairs. Inputs are also often referred to as
covariates, predictors and features; while outputs are known as
v
Reinforcement learning
Nando de Freitas
The Promise of Reinforcement Learning
Learning to act through trial and error.
Environment
cfw_ observation, reward
An agent interacts with an
environment and learns by
maximizing a scalar reward signal.
cfw_ actio