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  1. If you want to experiment with neural networks, and you know python, then my best recommendation is: use theano.[1]
  2. Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.[2]
  3. numpy.ndarrays are also used internally in Theano-compiled functions.[2]
  4. Theano can compute derivatives for functions of one or many inputs.[2]
  5. Theano has been powering large-scale computationally intensive scientific research since 2007, but it is also approachable enough to be used in the classroom (IFT6266 at the University of Montreal).[2]
  6. The following code is the original Theano's example.[3]
  7. , b) # values as input and computes a value for c f = theano .[3]
  8. Theano makes it possible to attain high speeds that give a tough competition to hand-crafted C implementations for problems involving large amounts of data.[4]
  9. Theano has got an amazing compiler which can do various optimizations of varying complexity.[4]
  10. Theano is a sort of hybrid between numpy and sympy, an attempt is made to combine the two into one powerful library.[4]
  11. With Theano, we can convert expressions into callable objects.[4]
  12. After all this theory, let’s code a simple script that uses Theano.[5]
  13. While introducing Theano, we said that one of its advantages is that it can use the computer’s GPU.[5]
  14. This command will allow you to use the GPU when you run a code that uses Theano.[5]
  15. Now that you have achieved some Theano basics, you are ready to do something more difficult.[5]
  16. Theano is an open source project released under the BSD license and was developed by the LISA (now MILA) group at the University of Montreal, Quebec, Canada (home of Yoshua Bengio).[6]
  17. The actual syntax of Theano expressions is symbolic, which can be off putting to beginners used to normal software development.[6]
  18. Theano assumes a working Python 2 or Python 3 environment with SciPy.[6]
  19. With a working Python and SciPy environment, it is relatively straightforward to install Theano.[6]
  20. `MILA will stop developing Theano <>`_.[7]
  21. Theano has a steep learning curve for most Python users as the framework for declaring variables and building functions differ greatly from base Python.[8]
  22. No machine learning algorithms exists “out-of-the-box” in Theano, however tutorials and examples exist to get new users started in the right direction.[8]
  23. It took us about a week to get our custom neural networks up and running on GPUs in Theano.[8]
  24. Theano is a Python library that allows us to evaluate mathematical operations including multi-dimensional arrays so efficiently.[9]
  25. Theano attains high speeds that gives a tough competition to C implementations for problems involving large amounts of data.[9]
  26. Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.[9]
  27. Theano is a foundation library mainly used for deep learning research and development and directly to create deep learning models or by convenient libraries such as Keras.[9]
  28. The Theano framework enables you to define, analyze, and optimize mathematical equations using the Python library.[10]
  29. Developers use Theano for manipulate and analyze expressions, including matrix-valued expressions.[10]
  30. Theano focuses on recognizing numerically expressions that are unstable, building symbolic graphs automatically, and compiling parts of your numeric expression into CPU or GPU instructions.[10]
  31. Its API is quite low level, and in order to write effective Theano you need to be quite familiar with the algorithms that are hidden away behind the scenes in other frameworks.[11]
  32. Theano is a go-to library if you have substantial academic machine learning expertise, are looking for very fine grained control of your models, or want to implement a novel or unusual model.[11]
  33. Similar to Lasagne, Blocks is a shot at adding a layer of abstraction on top of Theano to facilitate cleaner, simpler, more standardized definitions of deep learning models than writing raw Theano.[11]
  34. It allows users to choose whether the models they build are executed on Theano’s or TensorFlow’s symbolic graph.[11]
  35. Theano can use g++ or nvcc to compile parts your expression graph into CPU or GPU instructions, which run much faster than pure Python.[12]
  36. Theano can automatically build symbolic graphs for computing gradients.[12]
  37. Theano focuses more on tensor expressions than Sympy, and has more machinery for compilation.[12]
  38. If numpy is to be compared to MATLAB and sympy to Mathematica, Theano is a sort of hybrid of the two which tries to combine the best of both worlds.[12]
  39. Theano is a low-level library for scientific computing based on Python, which is used to target deep learning tasks related to defining, optimizing, and evaluating mathematical expressions.[13]
  40. There are public models for Theano, but each framework used on top also has plenty of tutorials and pre-trained datasets to choose from.[13]
  41. If you use Lasagne or Keras as high-level wrappers on top of Theano, again you’ll have a multitude of tutorials and pre-trained datasets at your fingertips.[13]
  42. Since TensorFlow was designed to replace Theano, a big part of its fanbase left.[13]
  43. Today, we will discuss Theano, what is it, how does it work and how can you build your own neural network using Theano.[14]
  44. We will also perform a brief comparison of Theano with its contemporary deep learning libraries and tools.[14]
  45. Theano is a numerical computation library that allows you to define optimize and evaluate mathematical expressions involving multi-dimensional arrays efficiently.[14]
  46. Before tensorflow Theano was the only framework of choice for developing deep neural networks with Python owing to its simple to use and understand features.[14]
  47. It seems that theano doesn't work with python3.6.[15]
  48. C compiler¶ Theano requires a working C compiler, and numpy/scipy require a compiler as well if you install them via pip .[16]
  49. BLAS¶ Lasagne requires numpy of version 1.6.2 or above, and Theano also requires scipy 0.11 or above.[16]
  50. Stable Lasagne release¶ Lasagne 0.1 requires a more recent version of Theano than the one available on PyPI.[16]
  51. An even more recent version of Theano will often work as well, but at the time of writing, a simple pip install Theano will give you a version that is too old.[16]
  52. Theano was written at the LISA lab with the intention of providing rapid development of efficient machine learning algorithms.[17]
  53. Theano can be installed on Windows, MacOS, and Linux.[17]
  54. Before you install Theano, you must install its dependencies.[17]
  55. To install Theano and its dependencies, you use pip from the command line as follows.[17]
  56. Theano is most commonly used to perform Deep Learning and has excellent GPU support and integration through PyCUDA.[18]
  57. As Theano and all its dependencies are written in Python.[18]
  58. To do this, create the .theanorc file in your HOME directory and set the fraction of GPU memory reserved by Theano.[18]
  59. The exact amount of energy may have to be hand-picked: if Theano asks for more memory that is currently available on the GPU, an error will be thrown during import of theano module.[18]
  60. Theano is a Python library that allows one to define and evaluate mathematical expressions involving multi-dimensional arrays efficiently.[19]
  61. This package contains Theano for Python 2.[19]
  62. This is where libraries like Theano can help.[20]
  63. Since Theano is still in development, it is not user friendly and not easy to install.[20]
  64. Run python and import theano.[20]
  65. Anaconda automatically installs two of the python libraries required to run Theano: scipy and numpy.[20]
  66. Theano is no longer being developed by MILA.[21]
  67. Tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.[21]
  68. Theano will unroll the loop into the graph structure and the whole unrolled loop is going to be compiled on the target architecture as the rest of the computation graph.[22]
  69. However, that graph will then be compiled into another Theano function that will be called repeatedly.[22]
  70. Looking for theano Keywords?[23]
  71. Looking for theano Answers?[23]
  72. Theano is a python library, which handles defining and evaluating symbolic expressions over tensor variables.[23]
  73. By default, Theano will detect if it can use cuDNN.[23]
  74. Theano is a Python library that enables you to evaluate, optimize, and define mathematical expressions that involve multi-dimensional arrays effectively.[24]
  75. Theano code is written in such as way that it takes the advantage of how a computer compiler functions.[24]
  76. Let’s have a look at an example where Theano supports functions having multiple outputs.[24]
  77. It is one of the most user-friendly libraries used for building neural networks and runs on top of Theano, Cognitive Toolkit, or TensorFlow.[24]
  78. Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).[25]
  79. Using Theano it is possible to attain speeds rivaling hand-crafted C implementations for problems involving large amounts of data.[25]
  80. Theano combines aspects of a computer algebra system (CAS) with aspects of an optimizing compiler.[25]
  81. Similarly, Theano serves as a base for many.[26]
  82. Theano can act as a building block for scientific computing.[26]
  83. PyLearn2 is a machine learning library with most functionality built on top of Theano.[26]
  84. Moreover, in this, we discussed PyTorch, TensorFlow, Keras, Theano etc.[26]
  85. However, we need to take a special attention when we write Theano code.[27]
  86. Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.[27]
  87. With Theano, you can achieve the rapid development of some of the most efficient machine learning algorithms.[28]
  88. Built on top of Theano are some of the well known deep learning libraries such as Keras, Blocks and Lasagne.[28]
  89. You can start an interactive Theano session on the login nodes by loading the corresponding modules and starting the Python interpreter.[29]
  90. Theano is a low-level library that specializes in efficient computation.[30]
  91. Use this if need the flexibility of Theano but don’t want to always write neural network layers from scratch.[30]
  92. At its core, Theano is a library for doing math using multi-dimensional arrays.[30]
  93. From our experience, you’ll rarely be writing Theano code directly.[30]
  94. With new frameworks coming up every month, TensorFlow and Theano have been there for a while and have gained a good amount of popularity as well.[31]
  95. Theano can be defined as a library for Scientific Computing.[31]
  96. Especially the single GPU Tasks run, way fast in Theano.[31]
  97. Theano computes the gradient when determining the Backpropagation error.[31]
  98. What makes Theano particularly attractive from a deep learning point of view is that it uses symbolic expressions.[32]
  99. Theano then takes this equation and figures out how best to run it in a manner completely transparent to the programmer.[32]
  100. The key point is that Theano allows you to write model specifications rather than the model implementations.[32]
  101. The reason was actually a problem installing Theano (which is a requirement for PyMC3).[32]
  102. Let me start by saying that Theano is beautiful.[33]
  103. Without Theano, we wouldn’t have anywhere near the amount of deep learning libraries (specifically in Python) that we do today.[33]
  104. At the very core, Theano is a Python library used to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays.[33]
  105. Similar to Theano, TensorFlow is an open source library for numerical computation using data flow graphs (which is all that a Neural Network really is).[33]
  106. Theano is deep learning library developed by the Université de Montréal in 2007.[34]


  1. Theano (python library)
  2. 2.0 2.1 2.2 2.3 Theano
  3. 3.0 3.1 Theano (software)
  4. 4.0 4.1 4.2 4.3 Theano Python Tutorial
  5. 5.0 5.1 5.2 5.3 Hands-On Theano: One of the Most Powerful Scientific Tools for Python
  6. 6.0 6.1 6.2 6.3 Introduction to the Python Deep Learning Library Theano
  7. Theano/Theano: Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficie
  8. 8.0 8.1 8.2 Quick Overview of Theano: A Popular New Python Library for Deep Learning
  9. 9.0 9.1 9.2 9.3 Theano in Python
  10. 10.0 10.1 10.2 Theano Release Notes :: NVIDIA Deep Learning Frameworks Documentation
  11. 11.0 11.1 11.2 11.3 An Overview of Python Deep Learning Frameworks
  12. 12.0 12.1 12.2 12.3 Deep Learning Software - Theano
  13. 13.0 13.1 13.2 13.3 Choosing an Open Source Machine Learning Library: TensorFlow, Theano, Torch, scikit-learn, Caffe
  14. 14.0 14.1 14.2 14.3 A beginner's guide to Theano for Deep Learning - RECODE || Blog
  15. How to install theano with python3.6?
  16. 16.0 16.1 16.2 16.3 Installation — Lasagne 0.2.dev1 documentation
  17. 17.0 17.1 17.2 17.3 Tutorialspoint
  18. 18.0 18.1 18.2 18.3 Theano — Sheffield HPC Documentation
  19. 19.0 19.1 Debian -- Details of package python-theano in sid
  20. 20.0 20.1 20.2 20.3 Installing Theano on Windows 10 using Python 3.5
  21. 21.0 21.1 numerical computation library for Python
  22. 22.0 22.1 Deep Learning with Theano
  23. 23.0 23.1 23.2 23.3 theano - Getting started with theano
  24. 24.0 24.1 24.2 24.3 Top Python Libraries for Machine Learning
  25. 25.0 25.1 25.2 Symmatrix
  26. 26.0 26.1 26.2 26.3 11 Deep Learning With Python Libraries and Frameworks
  27. 27.0 27.1 Deep Learning III : Theano, TensorFlow, and Keras
  28. 28.0 28.1 Best Python Libraries for Machine Learning and Deep Learning
  29. ScientificComputing
  30. 30.0 30.1 30.2 30.3 5 Genius Python Deep Learning Libraries
  31. 31.0 31.1 31.2 31.3 Theano vs TensorFlow : A Quick Comparision of Frameworks
  32. 32.0 32.1 32.2 32.3 Deep Learning with Theano - Part 1: Logistic Regression
  33. 33.0 33.1 33.2 33.3 My Top 9 Favorite Python Deep Learning Libraries
  34. TensorFlow vs Theano vs Torch vs Keras: Deep Learning Libraries