Top 5 Machine Learning Frameworks[2020]

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• Session 1

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• 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

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• Supervised- It helps in predicting data based on
  previous experiences. For example, filtering
  spam.
• Unsupervised-ML framework deciphers hidden
  patterns and recognizes facial features in
  pictures and friend lists to make
  recommendations.
• Reinforcement- It is concerned with boosting
  efficacy.
• 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

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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
  environments.
• 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

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• 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
  PDB
• It works dynamically with an updated graph
• It is widely used for natural language Processing

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• 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
  computation.
• Benefits
• Good for beginners
• Promotes faster experimentation
• It helps in writing readable and precise code

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• 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.

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• 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.

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Conclusion

• 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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