How to Make an Evolutionary Tetris AI

Let’s use an evolutionary algorithm to improve a Tetris AI! We’ll be coding this in Javascript (gasp) because I want to try something different. Through the process of selection, crossover, and mutation our AI will eventually be able to reach the high score of 500 in record time.


Code for this video:
https://github.com/llSourcell/How_to_…

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More Learning resources:
https://www.youtube.com/watch?v=L–Ix…
https://luckytoilet.wordpress.com/201…
https://codemyroad.wordpress.com/2013…
http://www.cs.uml.edu/ecg/uploads/AIf…
http://cs229.stanford.edu/proj2015/23…

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How to Use Tensorboard (LIVE)

We’re going to learn how the visualizer that comes with Tensorflow works in this live stream. We’ll go through a bunch of different features and test out its functionality both programmatically and visually.


4:41 code begins
37:07 tensorboard visualization begins

Code for this video:
https://github.com/llSourcell/how_to_…

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More Learning resources:
https://www.tensorflow.org/get_starte…
http://ischlag.github.io/2016/06/04/h…
https://www.youtube.com/watch?v=3bown…
https://blog.altoros.com/visualizing-…
http://www.titiapps.com/hands-on-tens…

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https://www.patreon.com/user?u=3191693

Streaming Live from UploadVR’s Studio in San Francisco!: https://www.youtube.com/uploadvr

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How to Generate Music with Tensorflow (LIVE)

This live session will focus on the details of music generation using the Tensorflow library. The goal is for you to understand the details of how to encode music, feed it to a well tuned model, and use it to generate really cool sounds. And I’m going to NOT use Google Hangouts, instead I’ll do this with a green screen and a DSLR camera 🙂


Code for this video:
https://github.com/llSourcell/music_d…

Please subscribe! And like. And comment. That’s what keeps me going.

My Udacity course is open for enrollments until this Saturday at midnight:
https://www.udacity.com/course/deep-l…

More Learning Resources:
http://www.asimovinstitute.org/analyz…
http://www.hexahedria.com/2015/08/03/…
https://github.com/hexahedria/biaxial…
http://www.hexahedria.com/2016/08/08/…
https://magenta.tensorflow.org/
https://github.com/farizrahman4u/seq2seq
http://stackoverflow.com/questions/14…
https://github.com/vishnubob/python-midi

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693

Streaming Live from UploadVR’s Studio in San Francisco!: https://www.youtube.com/uploadvr

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How to Make a Text Summarizer – Intro to Deep Learning #10

I’ll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We’ll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory.


Code for this video (Challenge included):
https://github.com/llSourcell/How_to_…

Jie’s Winning Code:
https://github.com/jiexunsee/rudiment…

More Learning resources:
https://www.quora.com/Has-Deep-Learni…
https://research.googleblog.com/2016/…
https://en.wikipedia.org/wiki/Automat…
http://deeplearning.net/tutorial/rnns…
http://machinelearningmastery.com/tex…

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http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693

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What to Expect for Big Data and Apache Spark in 2017

Big data remains a rapidly evolving field with new applications and infrastructure appearing every year. In this talk, Matei Zaharia will cover new trends in 2016 / 2017 and how Apache Spark is moving to meet them. In particular, he will talk about work Databricks is doing to make Apache Spark interact better with native code (e.g. deep learning libraries), support heterogeneous hardware, and simplify production data pipelines in both streaming and batch settings through Structured Streaming.

This talk was originally presented at Spark Summit East 2017.

You can view the slides on Slideshare:
http://www.slideshare.net/databricks/…

 

 

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Intro – Training a neural network to play a game with TensorFlow and Open AI

This tutorial mini series is focused on training a neural network to play the Open AI environment called CartPole.

The idea of CartPole is that there is a pole standing up on top of a cart. The goal is to balance this pole by wiggling/moving the cart from side to side to keep the pole balanced upright.

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How to Generate Art – Intro to Deep Learning #8

We’re going to learn how to use deep learning to convert an image into the style of an artist that we choose. We’ll go over the history of computer generated art, then dive into the details of how this process works and why deep learning does it so well.

Coding challenge for this video:
https://github.com/llSourcell/How-to-…

Itai’s winning code:
https://github.com/etai83/lstm_stock_…

Andreas’ runner up code:
https://github.com/AndysDeepAbstracti…

More learning resources:
https://harishnarayanan.org/writing/a…
https://ml4a.github.io/ml4a/style_tra…
http://genekogan.com/works/style-tran…
https://arxiv.org/abs/1508.06576
https://jvns.ca/blog/2017/02/12/neura…

Style transfer apps:
http://www.pikazoapp.com/
http://deepart.io/
https://artisto.my.com/
https://prisma-ai.com/

Please subscribe! And like. And comment. That’s what keeps me going.

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693

Song at the beginning is called Everyday by Carly Comando

 

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Deep Learning Libraries by Language

Python

  1. Theano is a python library for defining and evaluating mathematical expressions with numerical arrays. It makes it easy to write deep learning algorithms in python. On the top of the Theano many more libraries are built.

    1. Keras is a minimalist, highly modular neural network library in the spirit of Torch, written in Python, that uses Theano under the hood for optimized tensor manipulation on GPU and CPU.

    2. Pylearn2 is a library that wraps a lot of models and training algorithms such as Stochastic Gradient Descent that are commonly used in Deep Learning. Its functional libraries are built on top of Theano.

    3. Lasagne is a lightweight library to build and train neural networks in Theano. It is governed by simplicity, transparency, modularity, pragmatism , focus and restraint principles.

    4. Blocks a framework that helps you build neural network models on top of Theano.

  2. Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Google’s DeepDream is based on Caffe Framework. This framework is a BSD-licensed C++ library with Python Interface.

  3. nolearn contains a number of wrappers and abstractions around existing neural network libraries, most notably Lasagne, along with a few machine learning utility modules.

  4. Gensim is deep learning toolkit implemented in python programming language intended for handling large text collections, using efficient algorithms.

  5. Chainer bridge the gap between algorithms and implementations of deep learning. Its powerful, flexible and intuitive and is considered as the flexible framework for Deep Learning.

  6. deepnet is a GPU-based python implementation of deep learning algorithms like Feed-forward Neural Nets, Restricted Boltzmann Machines, Deep Belief Nets, Autoencoders, Deep Boltzmann Machines and Convolutional Neural Nets.

  7. Hebel is a library for deep learning with neural networks in Python using GPU acceleration with CUDA through PyCUDA. It implements the most important types of neural network models and offers a variety of different activation functions and training methods such as momentum, Nesterov momentum, dropout, and early stopping.

  8. CXXNET is fast, concise, distributed deep learning framework based on MShadow. It is a lightweight and easy extensible C++/CUDA neural network toolkit with friendly Python/Matlab interface for training and prediction.

  9. DeepPy is a Pythonic deep learning framework built on top of NumPy.

  10. DeepLearning is deep learning library, developed with C++ and python.

  11. Neon is Nervana’s Python based Deep Learning framework.

Matlab

  1. ConvNet Convolutional neural net is a type of deep learning classification algorithms, that can learn useful features from raw data by themselves and is performed by tuning its weighs.

  2. DeepLearnToolBox is a matlab/octave toolbox for deep learning and includes Deep Belief Nets, Stacked Autoencoders, convolutional neural nets.

  3. cuda-convnet is a fast C++/CUDA implementation of convolutional (or more generally, feed-forward) neural networks. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Training is done using the backpropagation algorithm.

  4. MatConvNet  is a MATLAB toolbox implementing Convolutional Neural Networks (CNNs) for computer vision applications. It is simple, efficient, and can run and learn state-of-the-art CNNs

CPP

  1. eblearn is an open-source C++ library of machine learning by New York University’s machine learning lab, led by Yann LeCun. In particular, implementations of convolutional neural networks with energy-based models along with a GUI, demos and tutorials.

  2. SINGA is designed to be general to implement the distributed training algorithms of existing systems. It is supported by Apache Software Foundation.

  3. NVIDIA DIGITS is a new system for developing, training and visualizing deep neural networks. It puts the power of deep learning into an intuitive browser-based interface, so that data scientists and researchers can quickly design the best DNN for their data using real-time network behavior visualization.

  4. Intel® Deep Learning Framework provides a unified framework for Intel® platforms accelerating Deep Convolutional Neural Networks.

Java

  1. N-Dimensional Arrays for Java (ND4J)is scientific computing libraries for the JVM. They are meant to be used in production environments, which means routines are designed to run fast with minimum RAM requirements.

  2. Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. It is designed to be used in business environments, rather than as a research tool.

  3. Encog is an advanced machine learning framework which supports Support Vector Machines,Artificial Neural Networks, Genetic Programming, Bayesian Networks, Hidden Markov Models, Genetic Programming and Genetic Algorithms are supported.

JavaScript

  1. Convnet.js is a Javascript library for training Deep Learning models (mainly Neural Networks) entirely in a browser. No software requirements, no compilers, no installations, no GPUs, no sweat.

Lua

  1. Torch is a scientific computing framework with wide support for machine learning algorithms. It is easy to use and efficient, fast scripting language, LuaJIT, and an underlying C/CUDA implementation. Torch is based on Lua programming language.

Julia

  1. Mocha is a Deep Learning framework for Julia, inspired by the C++ framework Caffe. Efficient implementations of general stochastic gradient solvers and common layers in Mocha could be used to train deep / shallow (convolutional) neural networks, with (optional) unsupervised pre-training via (stacked) auto-encoders. Its best feature include Modular architecture, High-level Interface, portability with speed, compatibility and many more.

Lisp

  1. Lush(Lisp Universal Shell) is an object-oriented programming language designed for researchers, experimenters, and engineers interested in large-scale numerical and graphic applications. It comes with rich set of deep learning libraries as a part of machine learning libraries.

Haskell

  1. DNNGraph is a deep neural network model generation DSL in Haskell.

.NET

  1. Accord.NET is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications

R

  1. darch package can be used for generating neural networks with many layers (deep architectures). Training methods includes a pre training with the contrastive divergence method and a fine tuning with common known training algorithms like backpropagation or conjugate gradient.
  2. deepnet implements some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on.

source from teglor

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Deep Learning with Tensorflow – Training a Restricted Boltzmann Machine

Enroll in the course for free at: https://bigdatauniversity.com/courses…

Deep Learning with TensorFlow Introduction

The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this TensorFlow course you’ll use Google’s library to apply deep learning to different data types in order to solve real world problems.

Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layer, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which is the vast majority of data in the world.

TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Connect with Big Data University:
https://www.facebook.com/bigdataunive…
https://twitter.com/bigdatau
https://www.linkedin.com/groups/40604…

ABOUT THIS COURSE
•This course is free.
•It is self-paced.
•It can be taken at any time.
•It can be audited as many times as you wish.

https://bigdatauniversity.com/courses…

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Deep Learning with Tensorflow – Introduction to Autoencoders

Enroll in the course for free at: https://bigdatauniversity.com/courses…

Deep Learning with TensorFlow Introduction

The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this TensorFlow course you’ll use Google’s library to apply deep learning to different data types in order to solve real world problems.

Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layer, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which is the vast majority of data in the world.

TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Connect with Big Data University:
https://www.facebook.com/bigdataunive…
https://twitter.com/bigdatau
https://www.linkedin.com/groups/40604…

ABOUT THIS COURSE
•This course is free.
•It is self-paced.
•It can be taken at any time.
•It can be audited as many times as you wish.

https://bigdatauniversity.com/courses…

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