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Top 5 Machine Learning Frameworks[2020]


A Machine Learning Framework is an interface, library, or tool which allows developers to build machine learning models easily, without getting into the depth of the underlying algorithms. – PowerPoint PPT presentation

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Date added: 8 May 2020
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Title: Top 5 Machine Learning Frameworks[2020]

  • Session 1

  • As we advance in the world of digitization, there
    is going to be a growing demand for technologies
    that can simplify human life, and one such
    technology is machine learning. It is the subset
    of AI, which helps the business examine the data,
    learn, and adapt it. Based on its analysis of
    data, machine learning enables the devices to
    make a decision and work accordingly. Another
    aspect that you need to know about is deep
    learning, which is the subdomain of machine
    learning. It ensures efficiency and accuracy.
  • With the help of best frameworks for Machine
    learning and Deep Learning, you can easily
    construct ML and DL models, which are accurate
    and efficient. Moreover, these frameworks nullify
    the need to understand the details of ML
    algorithms. Machine learning is divided into the
    following types

  • Supervised- It helps in predicting data based on
    previous experiences. For example, filtering
  • Unsupervised-ML framework deciphers hidden
    patterns and recognizes facial features in
    pictures and friend lists to make
  • Reinforcement- It is concerned with boosting
  • Machine learning has emerged as a popular career
    option. In the times to come, we are going to
    witness a huge demand for Machine learning
    experts. If you are planning to learn machine
    learning, then you must also know about the
    popular machine learning frameworks. Here we are
    discussing the top 5 machine learning framework
    that you need to learn to become a machine
    learning expert

Top Machine Learning Frameworks
  • 1. TensorFlow- It is one of the most popular
    open-source platforms by Google. This framework
    is based on JavaScript, and this framework is
    equipped with different tools and community
    resources, which helps in easy deployment to the
    machine learning models. The core tool allows
    your deployment for the browsers, and the
    TensorFlow Lite is used to deploy models on
    mobile. You can also use the TensorFlow Extended
    for deploying ML models in large production
  • If you are planning to learn the TensorFlow
    framework, then you need to have expertise in
    Julia, Go, and Python. You would be required to
    do lengthy coding, and it works with a static
    computation graph.
  • Benefits
  • It is good for developing ML models
  • Used for data integration function, SQL Tables
    and images together

  • 2. PyTorch or Torch- Another popular framework
    for machine learning is PyTorch or Torch.
    Facebook developed it. This framework is based on
    the Torch library and was designed to advance the
    entire process from research prototyping to
    production deployment. It has a C frontend atop
    and a Python interface. PyTorch makes use of
    standard debuggers like PDB or PyCharm.
  • Benefits
  • You can use standard debuggers like PyCharm or
  • It works dynamically with an updated graph
  • It is widely used for natural language Processing

  • 3. Keras- This yet another open-source machine
    learning framework. It is much faster than the
    other frameworks. It comes with in-built support
    for data parallelism. Thus, it can handle a large
    volume of data while expediting the training
    time. This framework is written in Python and is
    easy-to-use. You can use this for high-level
  • Benefits
  • Good for beginners
  • Promotes faster experimentation
  • It helps in writing readable and precise code

  • 4. Sonnet- If you are looking for a high-end
    framework for machine learning, then you must
    consider learning Sonnet. It is used for building
    complex neural network structures in TensorFlow.
    It is a simple but powerful programming model. It
    is based on a single concept snt.Module.
  • Benefits
  • It is simple and easy to use the framework
  • You can write modules which can detect other
    submodels internally
  • It is easy to integrate models created with
    Sonnet with raw TF code and those written with
    high-level libraries.

  • 5. MXNet- The 5th entrant in our list of most
    preferred machine learning frameworks for Machine
    Learning experts. It is highly scalable and works
    very fast. One of the notable features of this
    framework is that it supports multiple
    programming languages and has a flexible
    programming model. Thus, making it an absolutely
    useful framework. Some of the programming
    languages that it supports are Matlab, Java,
    Python, Perl, Wolfram, Go, R, etc. It is portable
    and can scale to different GPUs.
  • Benefits
  • It supports different GPUs
  • It promotes faster context switching
  • It supports different programming languages
  • It allows the developers to choose their desired
    programming language for building machine
    learning models.

  • These are a few of the many machine learning
    frameworks that every machine learning expert
    needs to know.
  • Global Tech Council brings you a comprehensive
    curriculum having all the details about Machine
    Learning. Your entire study material is based on
    the demand for the industry so that you can pick
    up the practical aspect of this technology and
    learn how to implement the same. The machine
    learning certification program by Global Tech
    Council is a recognized one, and you can surely
    reap benefits from the same.

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