Mention machine learning, and many common frameworks pop into mind, from "old" stalwarts like Scikit-learn to juggernauts like Google's TensorFlow. But the field is large and diverse, and useful innovations are bubbling up across the landscape.
Source: Info World
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Learn how to create a simple neural network, and a more accurate convolutional neural network, with the PyTorch deep learning library
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We rank 23 open-source deep learning libraries that are useful for Data Science. The ranking is based on equally weighing its three components: Github and Stack Overflow activity, as well as Google search results.
Source: Kdnugget22806
Deep Learning is a collection of algorithms used in machine learning. This is an approach used for building and training neural network. There are many frameworks like Tensorflow, Theono, Caffe, Torch and PyTorch.
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Scholarship recipients will learn the Machine Learning, Deep Learning framework PyTorch for Artificial Intelligence Research.
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Speech recognition is the task of detecting spoken words. There are many techniques to do Speech Recognition. In this post, we will go through some background required for Speech Recognition and use a basic technique to build a speech recognition model. The code is available on GitHub. For the techniques mentioned in this post, check this Jupyter Notebook.
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GitHub has identified three features that make a programming language popular in 2018.
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We will now talk about what are some steps that you can do if you want to deploy your recently trained model as an API. The discussion on how to do this with Fast.ai is currently ongoing (more) and will most likely continue until PyTorch releases their official 1.0 version. You can find more information in the Fast.ai Forums, PyTorch Documentation/Forums, and their respective GitHub repositories.
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More than 1,300 people mainly working in the tech, finance, and healthcare revealed which machine-learning technologies they use at their firms, in a new O'Reilly survey.
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Using a Deep Learning Framework Deep Learning models are easily developed by the professionals without requiring to develop the models from starting.
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Well-architected API on top of either Tensorflow or Theano and potentially extensible as a shim over other deep learning engines as well.
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Natural Language Processing (NLP) is a very important field in the present time. The present article is an amalgamation of some of the most useful books with some of the amazing YouTube videos available online which you can easily refer for NLP.
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Facebook's commitment to the wider dev community remains as strong as ever if recent developments are any indication.
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Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. Here are some courses which will make you aware of everything about this technology.
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Each chapter focuses on a different area of deep learning. Chapters start with a refresher on the core principles, before sharing the code you need to implement them in PyTorch.
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Machine Learning is invading this world gradually and gradually. If we can develop a driverless car then it is Machine Learning.
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In this article, you will get to know some of the best frameworks to get you started with AI development.
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Keras, TensorFlow, and PyTorch are among the top three frameworks that are preferred by Data Scientists as well as beginners in the field of Deep Learning.
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Accelerate your deep learning with PyTorch covering all the fundamentals of deep learning with a python-first framework.
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Fortunately, some brilliant minds have created and generously open-sourced several deep learning frameworks that can be easily integrated by people with little to no knowledge of machine learning technology.
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PyTorch's key features will be explained and compare it to the current most popular deep learning framework in the world (Tensorflow).
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Here are some resources to learn deep learning frameworks.
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Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Here are some famous machine learning tools to learn from some famous books.
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Build neural network models in text, vision and advanced analytics using PyTorch.
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Some Deep Learning Framework tutorials to which will boost your knowledge in this field.
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Data science in its very brief form is the science of drawing out insights and information out of raw data using a mixture of various tools, algorithms, and machine learning principles.
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AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone especially your non-technical colleagues to take.
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Learn how to use Python and its popular libraries such as NumPy and Pandas, as well as the PyTorch Deep Learning library.
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You will learn about the different deep learning models and build your first deep learning model using the Keras library.
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Keras, TensorFlow, and PyTorch are among the top three frameworks that are preferred by Data Scientists as well as beginners in the field of Deep Learning.
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Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities.
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Learn how to use Python and its popular libraries such as NumPy and Pandas, as well as the PyTorch Deep Learning library.
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