Alternatives to Theano logo

Alternatives to Theano

TensorFlow, MXNet, Keras, Torch, and Caffe are the most popular alternatives and competitors to Theano.
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What is Theano and what are its top alternatives?

Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
Theano is a tool in the Machine Learning Tools category of a tech stack.
Theano is an open source tool with 9.4K GitHub stars and 2.5K GitHub forks. Here鈥檚 a link to Theano's open source repository on GitHub

Top Alternatives to Theano

  • TensorFlow

    TensorFlow

    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...

  • MXNet

    MXNet

    A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. ...

  • Keras

    Keras

    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/ ...

  • Torch

    Torch

    It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. ...

  • Caffe

    Caffe

    It is a deep learning framework made with expression, speed, and modularity in mind. ...

  • PyTorch

    PyTorch

    PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc. ...

  • NumPy

    NumPy

    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. ...

  • Numba

    Numba

    It translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes. ...

Theano alternatives & related posts

TensorFlow logo

TensorFlow

2.5K
2.7K
76
Open Source Software Library for Machine Intelligence
2.5K
2.7K
+ 1
76
PROS OF TENSORFLOW
  • 24
    High Performance
  • 16
    Connect Research and Production
  • 13
    Deep Flexibility
  • 9
    Auto-Differentiation
  • 9
    True Portability
  • 2
    Easy to use
  • 2
    High level abstraction
  • 1
    Powerful
CONS OF TENSORFLOW
  • 8
    Hard
  • 5
    Hard to debug
  • 1
    Documentation not very helpful

related TensorFlow posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.2M views

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

!

See more
MXNet logo

MXNet

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62
1
A flexible and efficient library for deep learning
38
62
+ 1
1
PROS OF MXNET
  • 1
    User friendly
CONS OF MXNET
    Be the first to leave a con

    related MXNet posts

    Keras logo

    Keras

    870
    902
    12
    Deep Learning library for Theano and TensorFlow
    870
    902
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    12
    PROS OF KERAS
    • 5
      Quality Documentation
    • 4
      Easy and fast NN prototyping
    • 3
      Supports Tensorflow and Theano backends
    CONS OF KERAS
    • 3
      Hard to debug

    related Keras posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.2M views

    Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

    At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

    TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

    Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

    https://eng.uber.com/horovod/

    (Direct GitHub repo: https://github.com/uber/horovod)

    See more

    I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.

    See more
    Torch logo

    Torch

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    An open-source machine learning library and a script language based on the Lua programming language
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    PROS OF TORCH
      Be the first to leave a pro
      CONS OF TORCH
        Be the first to leave a con

        related Torch posts

        Caffe logo

        Caffe

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        A deep learning framework
        49
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        + 1
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        PROS OF CAFFE
          Be the first to leave a pro
          CONS OF CAFFE
            Be the first to leave a con

            related Caffe posts

            PyTorch logo

            PyTorch

            868
            963
            33
            A deep learning framework that puts Python first
            868
            963
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            33
            PROS OF PYTORCH
            • 11
              Easy to use
            • 9
              Developer Friendly
            • 8
              Easy to debug
            • 5
              Sometimes faster than TensorFlow
            CONS OF PYTORCH
            • 2
              Lots of code

            related PyTorch posts

            Server side

            We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

            • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

            • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

            • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

            Client side

            • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

            • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

            • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

            Cache

            • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

            Database

            • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

            Infrastructure

            • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

            Other Tools

            • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

            • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

            See more
            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.2M views

            Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

            At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

            TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

            Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

            https://eng.uber.com/horovod/

            (Direct GitHub repo: https://github.com/uber/horovod)

            See more
            NumPy logo

            NumPy

            801
            581
            6
            Fundamental package for scientific computing with Python
            801
            581
            + 1
            6
            PROS OF NUMPY
            • 6
              Great for data analysis
            CONS OF NUMPY
              Be the first to leave a con

              related NumPy posts

              Server side

              We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

              • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

              • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

              • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

              Client side

              • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

              • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

              • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

              Cache

              • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

              Database

              • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

              Infrastructure

              • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

              Other Tools

              • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

              • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

              See more
              Numba logo

              Numba

              7
              22
              0
              An open source JIT compiler that translates a subset of Python and NumPy code into fast machine code
              7
              22
              + 1
              0
              PROS OF NUMBA
                Be the first to leave a pro
                CONS OF NUMBA
                  Be the first to leave a con

                  related Numba posts