Paper: Introduction to Convolutional Nerual Networks by Jianxin Wu

In this recently published paper, Jianxin Wu helps the reader understand
how a CNN runs at the mathematical level. It is self contained and you should not need any further material to understand it from a mathematical viewpoint.

With CNN, the important part is understanding what happens when you adjust the different parameters. Bu in order to make sense of those it is much easier when you know the underlying principles behind it.

Here you go

Another Deep Learning School

I recently posted about Deep Learning Summer School 2016

Here is a link to another Bay Area Deep Learning School one that was the same year in September at Stanford CA.

 

Introduction to Feedforward Neural Networks​

Hugo Larochelle​

I will cover some of the fundamental concepts behind feedforward neural networks. I’ll start by briefly reviewing the basic multi-layer architecture of feedforward networks, as well as backpropagation from automatic differentiation and stochastic gradient descent (SGD). Then, I’ll discuss the most recent ideas that are now commonly used for training deep neural networks, such as variants of SGD, dropout, batch normalization and unsupervised pretraining.

Video, Slides

Deep Learning for Computer Vision

Andrej Karpathy

I will cover the design of convolutional neural network (ConvNet) architectures for image understanding, the history of state of the art models on the ImageNet Large Scale Visual Recognition Challenge, and some of the most recent patterns of developments in this area. I will also talk about ConvNet architectures in the context of related visual recognition tasks such as object detection, segmentation, and video processing.

Video, Slides

Deep Learning for NLP

Richard Socher

I will describe the foundations of deep learning for natural language processing: word vectors, recurrent neural networks, tasks and models influenced by linguistics. I will end with some recent models that put together all these basic lego blocks into a very powerful deep architecture called dynamic memory network.

Video, Slides

Tensorflow Tutorial

Sherry Moore

Video, Slides

Foundations of Deep Unsupervised Learning

Ruslan Salakhutdinov

Building intelligent systems that are capable of extracting meaningful
representations from high-dimensional data lies at the core of solving many Artificial Intelligence tasks, including visual object recognition, information retrieval, speech perception, and language understanding. In this tutorial I will discuss mathematical basics of many popular unsupervised models, including Sparse Coding, Autoencoders, Restricted Boltzmann Machines (RBMs), Deep Boltzmann Machines (DBMs), and Variational Autoencoders (VAE). I will furtherdemonstrate that these models are capable of extracting useful hierarchical representations from high dimensional data with applications in visual object recognition, information retrieval, and natural language processing. Finally, time permitting, I will briefly discuss models that can generate natural language descriptions (captions) of images, as well as generate images from captions using attention mechanism.

Video

Nuts and bolts of applying deep learning

Andrew Ng

Video

Deep Reinforement Learning

John Schulman, OpenAI

I’ll start by providing an overview of the state of the art in deep reinforcement learning, including recent applications to video games (e.g., Atari), board games (AlphaGo) and simulated robotics. Then I’ll give a tutorial introduction to the two methods that lie at the core of these results: policy gradients and Q-learning. Finally, I’ll present a new analysis that shows the close similarity between these two methods. A theme of the talk will be to not only ask “what works?”, but also “when does it work?” and “why does it work?”; and to find the kind of answers that are actionable for tuning one’s implementation and designing better algorithms.

Video, Slides

Theano Tutorial

Pascal Lamblin

Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently, on CPU or GPU. Since its introduction, Theano has been one of the most popular frameworks in the machine learning community, and multiple frameworks for deep learning have been built on top of it (Lasagne, Keras, Blocks, …). This tutorial will focus first on the concepts behind Theano and how to build and evaluate simple expressions, and then we will see how more complex models can be defined and trained.

Video, Slides

Deep Learning for Speech

Adam Coates

Traditional speech recognition systems are built from numerous modules, each requiring its own challenging engineering. With deep learning it is now possible to create neural networks that perform most of the tasks of a traditional engine “end to end”, dramatically simplifying the development of new speech systems and opening a path to human-level performance. In this tutorial, we will walk through the steps for constructing one type of end-to-end system similar to Baidu’s “Deep Speech” model. We will put all of the pieces together to form a “scale model” of a state of the art speech system; small-scale versions of the neural networks now powering production speech engines.

Video, Slides

Torch Tutorial

Alex Wiltschko

Torch is an open platform for scientific computing in the Lua language, with a focus on machine learning, in particular deep learning. Torch is distinguished from other array libraries by having first-class support for GPU computation, and a clear, interactive and imperative style. Further, through the “NN” library, Torch has broad support for building and training neural networks by composing primitive blocks or layers together in compute graphs. Torch, although benefitting from
extensive industry support, is a community owned and community developed ecosystem. All neural net libraries, including Torch NN, TensorFlow and Theano, rely on automatic differentiation (AD) to manage the computation of gradients of complex compositions of functions. I will present some general background on automatic differentiation (AD), which is the fundamental abstraction of gradient based optimization, and demonstrate
Twitter’s flexible implementation of AD in the library torch-autograd

Video, Slides

Sequence to Sequence Learning for NLP and Speech

Quoc Le

I will first present the foundations of sequence to sequence (seq2seq) learning and attention models, and their applications in machine translation and speech recognition. Then I will discuss attention with pointers and functions. Finally I will describe how reinforcement learning can play a role in seq2seq and attention models.

Video, Slides

Foundations and Challenges of Deep Learning

Yoshua Bengio

Why is deep learning working as well as it does? What are some big challenges that remain ahead? This talk will first survey some key factors in the success of deep learning. First, from the context of the no-free lunch theorem, we will discuss the expressive power of deep netwroks to capture abstract distributed representations. Second, we will discuss our surprising ability to actually optimize the parameters of neural networks in spite of their non-convexity. We will then consider a few challenges ahead, including the core representation question of disentangling the underlying explanatory factors of variation, especially with unsupervised learning, why this is important for bringing reinforcement learning to the next level, and optimization questions that remain challenging, such as learning of long-term dependencies, understanding the optimization landscape of deep networks, and how learning in brains remain a mystery worth attacking from the deep learning perspective.

Video, Slides

ConvNetJS – A JavaScript library for training Neural Networks in your browser

Here is a link to an Open Source JavaScript library that allows you to train neural networks in your browser. It was created by Andrej Karpathy, a PhD student at Stanford University and is currently community maintained.

It currently supports:

  • Common Neural Network modules (fully connected layers, non-linearities)
  • Classification (SVM/Softmax) and Regression (L2) cost functions
  • Ability to specify and train Convolutional Networks that process images
  • An experimental Reinforcement Learning module, based on Deep Q Learning.

Project site: http://cs.stanford.edu/people/karpathy/convnetjs/
Github: https://github.com/karpathy/convnetjs

 

Lasso regression

In order to avoid overfitting in regression due to too many feature while at the same time have enough features to minimize the sum of squared errors in order to get a more accurate fit on the test data, you need to regularize the regression.

This can be done with a Lasso regression where you want to minimize the sim of squared errors + plus a penalty parameters times the coefficient of the regression (which indicates the amount of features)

minimize SSE + λ|β|

 

Deep Learning School

Here you can watch lectures from the 2016 Deep Learning Summer School in Montreal.

Course excerpt:

Deep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning.

The Deep Learning Summer School 2016 is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research.

 

 

Here is the schedule in which you could view the presentations

table.schedule td {
vertical-align: top;
padding: 10px;
}

 01/08/2016  02/08/2016  03/08/2016  04/08/2016  05/08/2016  06/08/2016  07/08/2016
9:00
10:30
Doina
Precup
Rob
Fergus
Yoshua
Bengio
Kyunghyun

Cho
Joelle
Pineau
Ruslan
Salakhutdinov
Bruno
Olshausen
Neuro I
10:30
11:00
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
11:00
12:30
Hugo Larochelle
Antonio Torralba
Sumit
Chopra
Edward Grefenstette
Pieter
Abbeel
Shakir
Mohamed
Surya
Ganguli

and Deep Learning Theory
12:30
14:30
Lunch Lunch LunchWiDL event Lunch Lunch Lunch Lunch
14:30
16:00
Hugo Larochelle

Neural Networks II (click on part II)

Alex Wiltschko
Torch I
Jeff
Dean

& TensorFlow

Julie Bernauer
(NVIDIA)
GPU programming with CUDA
Joelle, Pieter & Doina
Advanced Topics in RL
Contributed
talks

Session 4
Contributed
talks

Session 4
16:00
16:30
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
Coffee
Break
16:30
18:00
Pascal
Lamblin

Practical
Session
 Alex Wiltschko
(Torch)

Frédéric Bastien
(Theano)

Jeff
Dean

& TensorFlow (click on part II)

Contributed
talks

Session 1
Contributed
talks

Session 2
Contributed
Posters

Session 1
Contributed
Posters

Session 2
 Evening Opening Reception
(18:00-20:30)
— by —
Imagia
 Happy Hour
(18:45-22:30)
buses at 18:30
— by —
Maluuba
Happy Hour
(18:30-20:30)
— by —
Creative Destruction Lab

(or you can just follow them in consecutive order at http://videolectures.net/deeplearning2016_montreal/ since they seem to be in the order they were presented.)

Contributed talks:

12:55 Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks
Rajarshi Das

14:29 Professor Forcing: A New Algorithm for Training Recurrent Networks
Anirudh Goyal

10:59 Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
Tegan Maharaj

18:58 Deep multi-view representation learning of brain responses to natural stimuli
Leila Wehbe

14:49 Learning to Communicate with Deep Multi­-Agent Reinforcement Learning
Jakob Foerster

13:57 Model-Based Relative Entropy Stochastic Search
Abbas Abdolmaleki

16:33 Learning Nash Equilibrium for General-Sum Markov Games from Batch Dat
Julien Pérolat

20:30 A Network-based End-to-End Trainable Task-oriented Dialogue System
Tsung-Hsien Wen

15:28 Inference Learning
Patrick Putzky

16:45 Variational Autoencoders with PixelCNN Decoders
Ishaan Gulrajani

13:33 An Infinite Restricted Boltzmann Machine
Marc-Alexandre Côté

15:15 Deep siamese neural network for prediction of long-range interactions in chromatin
Davide Chicco

14:09 Beam Search Message Passing in Bidirectional RNNs: Applications to Fill-in-the-Blank Image Captioning
Qing Sun

18:40 Analyzing the Behavior of Deep Visual Question Answering Models
Aishwarya Agrawal

13:55 Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation
Sina Honari