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Machine Learning is Fun! – Medium. What is machine learning?

Machine Learning is Fun! – Medium

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data. For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code.

“Machine learning” is an umbrella term covering lots of these kinds of generic algorithms. Richard Socher - Parsing Natural Scenes And Natural Language With Recursive Neural Networks. For remarks, critical comments or other thoughts on the paper.

Richard Socher - Parsing Natural Scenes And Natural Language With Recursive Neural Networks

Save what you write before you post, then type in the password, post (nothing happens), then copy the text and re-post. It's to prevent spammers. I came across this on the Facebook group "Strong Artificial Intelligence" Thank you for the source code and all the explanations. I'd like to reference this page on the website of the hard science novel 'Memories with Maya' You'll understand my enthusiasm, because of the creative spin on AI done, in that story... “I granted the AI access to the cameras on your Wizer,” he said. “The AI also gets help from GPS data. Deep Learning: Recurrent Neural Networks in Python. Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades.

Deep Learning: Recurrent Neural Networks in Python

So what’s going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I’ll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we’re going to extend it so that it becomes the parity problem - you’ll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.

Unsupervised Deep Learning in Python. This course is the next logical step in my deep learning, data science, and machine learning series.

Unsupervised Deep Learning in Python

I’ve done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? Unsupervised deep learning! In these course we’ll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we’ll look at a special type of unsupervised neural network called the autoencoder. Last, we’ll look at restricted Boltzmann machines (RBMs). Finally, we’ll bring all these concepts together and I’ll show you visually what happens when you use PCA and t-SNE on the features that the autoencoders and RBMs have learned, and we’ll see that even without labels the results suggest that a pattern has been found. How to choose machine learning algorithms.

The answer to the question "What machine learning algorithm should I use?

How to choose machine learning algorithms

" is always "It depends. " It depends on the size, quality, and nature of the data. It depends what you want to do with the answer. It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. Extracting, transforming and selecting features - Spark 2.0.1 Documentation. This section covers algorithms for working with features, roughly divided into these groups: Extraction: Extracting features from “raw” data Transformation: Scaling, converting, or modifying features Selection: Selecting a subset from a larger set of features Table of Contents Term frequency-inverse document frequency (TF-IDF) is a feature vectorization method widely used in text mining to reflect the importance of a term to a document in the corpus.

Extracting, transforming and selecting features - Spark 2.0.1 Documentation

Using of machine learning for invoice data recognition. Machine learning (ML) algorithms allows computers to define and apply rules which were not described explicitly by developer.

Using of machine learning for invoice data recognition

This algorithms can resolve at least the following kinds of problems: classification, clustering, regression. There are quite a lot of ways to apply these algorithms in business applications (here is one of general articles about it). TensorFlow Deep Learning Library. Machine Learning - Stanford University. About this course: Machine learning is the science of getting computers to act without being explicitly programmed.

Machine Learning - Stanford University

In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. Tutorial — Lasagne 0.2.dev1 documentation. Let’s now investigate what’s needed to make that happen!

Tutorial — Lasagne 0.2.dev1 documentation

To follow along, open up the source code in your favorite editor (or online: mnist.py). Loading data¶ The first piece of code defines a function load_dataset(). Basic Tensor Functionality — Theano 0.8.2 documentation. Theano supports any kind of Python object, but its focus is support for symbolic matrix expressions.

Basic Tensor Functionality — Theano 0.8.2 documentation

When you type, the x is a TensorVariable instance. The T.fmatrix object itself is an instance of TensorType. Theano knows what type of variable x is because x.type points back to T.fmatrix. This chapter explains the various ways of creating tensor variables, the attributes and methods of TensorVariable and TensorType, and various basic symbolic math and arithmetic that Theano supports for tensor variables. Creation¶ Theano provides a list of predefined tensor types that can be used to create a tensor variables. >>> x = scalar('myvar', dtype='int32')>>> x = iscalar('myvar')>>> x = TensorType(dtype='int32', broadcastable=())('myvar')

Installation guides

APIs. Datasets. Considerations when setting up deep learning hardware. In last week’s blog post, I discussed my investment in an NVIDIA DIGITS DevBox for deep learning. It was quite the investment, weighing in at a staggering (and wallet breaking) $15,000 — more than I ever thought I would spend on a computer that lives in my office (I normally like hardware that exists in the cloud where I don’t need to physically interact with it). That said, this is an investment that will drive the PyImageSearch blog over the coming months. I’ve made the commitment do to more more deep learning tutorials — specifically tutorials that involve Convolutional Neural Networks and image classification. In fact, over Memorial Day weekend, I spent two hours inside Trello brainstorming and planning the next year’s worth of tutorials on the PyImageSearch blog. Getting Started with Deep Learning and Python.

Update – January 27, 2015: Based on the feedback from commenters, I have updated the source code in the download to include the original MNIST dataset! No external downloads required! Update – March 2015, 2015: The nolearn package has now deprecated and removed the dbn module. When you go to install the nolearn package, be sure to clone down the repository, checkout the 0.5b1 version, and then install it. Do not install the current version without first checking out the 0.5b1 version! In the future I will post an update on how to use the updated nolearn package! Time Series Prediction With Deep Learning in Keras. Time Series prediction is a difficult problem both to frame and to address with machine learning. In this post, you will discover how to develop neural network models for time series prediction in Python using the Keras deep learning library. After reading this post you will know: About the airline passengers univariate time series prediction problem.How to phrase time series prediction as a regression problem and develop a neural network model for it.How to frame time series prediction with a time lag and develop a neural network model for it.

Let’s get started. Update Oct/2016: Replaced graphs with more accurate versions, commented on the limited performance of the first method. Problem Description. My Top 9 Favorite Python Deep Learning Libraries. What is the Difference Between Deep Learning and “Regular” Machine Learning? Another concise explanation of a machine learning concept by Sebastian Raschka. This time, Sebastian explains the difference between Deep Learning and "regular" machine learning. That's an interesting question, and I try to answer this is a very general way. The tl;dr version of this is: Deep learning is essentially a set of techniques that help we to parameterize deep neural network structures, neural networks with many, many layers and parameters. And if we are interested, a more concrete example: Let's start with multi-layer perceptrons (MLPs)...

7 Steps to Mastering Machine Learning With Python. There are many Python machine learning resources freely available online. Implementing a Neural Network from Scratch in Python – An Introduction – WildML. A Neural Network in 11 lines of Python (Part 1) - i am trask. Python-machine-learning-book/README.md at master · rasbt/python-machine-learning-book. ImageSearchEngineResourceGuide. The 10 Algorithms Machine Learning Engineers Need to Know. Data types — NumPy v1.11 Manual. Array types and conversions between types Numpy supports a much greater variety of numerical types than Python does.

This section shows which are available, and how to modify an array’s data-type. Additionally to intc the platform dependent C integer types short, long, longlong and their unsigned versions are defined. Jupyter Notebook Viewer. So let us define the layers for the convolutional net. In general, layers are assembled in a list. Each element of the list is a tuple -- first a Lasagne layer, next a dictionary containing the arguments of the layer.

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Introduction to Deep Neural Network Programming in Python – Derek Janni – Data Scientist. Generative Models. Neural networks and deep learning. CS231n Convolutional Neural Networks for Visual Recognition. Table of Contents: Convolutional Neural Networks are very similar to ordinary Neural Networks from the previous chapter: they are made up of neurons that have learnable weights and biases.

Each neuron receives some inputs, performs a dot product and optionally follows it with a non-linearity. The whole network still expresses a single differentiable score function: from the raw image pixels on one end to class scores at the other. And they still have a loss function (e.g. SVM/Softmax) on the last (fully-connected) layer and all the tips/tricks we developed for learning regular Neural Networks still apply. So what does change? Architecture Overview Recall: Regular Neural Nets. Regular Neural Nets don’t scale well to full images. 3D volumes of neurons. Left: A regular 3-layer Neural Network. A ConvNet is made up of Layers. Layers used to build ConvNets Example Architecture: Overview. Introduction to Deep Neural Network Programming in Python – Derek Janni – Data Scientist.