I liked the course “Intro to Machine Learning” by Udacity.

This image summarizes what it was about:

### Yet another free deep learning online course

This course is held one of the contributors to Keras and Tensoflow.

Here you go:

https://campus.datacamp.com/courses/deep-learning-in-python

### 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.

### Book: Neural Networks and Deep Learning

Here is another book on deep learning and neural nets:

Neural Networks and Deep Learning by Michael Nielsen / May 2017

The other one i mentioned before is the Deep Learning Book

You can read both for free online.

### Hacker’s guide to Neural Networks

The guy who wrote the convnetjs javascript library for running nerual nets in your browser or node, has this great series on his blog describing neural nets to coders that lke to tinker with code samples rather than reading long texts.

### 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.

### 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.

### 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.

### 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.

### 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.

### 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.

### 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.

### 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

### 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.

### 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.

### The best free introductory course to machine learning according to the Internet

Many people have recommended the Machine Learning course held by Andrew Ng at Stanford University that is available via Coursera. Some say it is the best course they have heard. You can participate for free if you want, but if you want a certificate that you have taken the course you can buy the course for a small fee.

### 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

### Deep learning for Self driving cars

I found a great course for learning to create self driving cards using Deep learning, Deep Reinforcemet Learning, Convolutional Neural Networks and Recurrent Neural Network for different parts of the tasks needed to be solved in producing an autonomous vehicle that can adapt to traffic, control a car, learn to drive and steer through time.

### 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|