Advance from building a first program to building a neural network.
Source: Opensource
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This post presents 5 practical resources for getting a start in natural language processing, covering a wide array of topics and approaches.
Source: Kdnugget35253
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: Kdnugget16077
Convolution neural networks (CNNs) are a family of deep networks that can exploit the spatial structure of data (e.g. images) to learn about the data, so that the algorithm can output something useful.
Source: HOB6558
Open source platform like Python play an important role in the Machine learning market. Python has a large collection of libraries. The simplicity of python has attracted many developers to build libraries for Machine learning.
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Deep learning is an increasingly popular subset of machine learning. Deep learning models are built using neural networks. A neural network takes in inputs, which are then processed in hidden layers using weights that are adjusted during training.
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Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go, a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.
Source: HOB8292
Deep learning seems to be leading as in data science as it is more researched. There are many applications which can help you build a save future as predicted by the data scientist. Deep learning looks hard and intimidating. Applications like TensorFlow, Keras, GPU based computing might scare you but in reality, it is not too hard and its take time effort and time to follow, and applying these applications in regular problems is easy.
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This may be the golden age of deep learning but a lot can be learned by looking at where deep neural nets aren't working yet. This can be a guide to calming the hype. It can also be a roadmap to future opportunities once these barriers are behind us.
Source: HOB5376
If you plan to start learning machine-learning models then you'll need a reasonably deep knowledge of math, spanning linear algebra, calculus, and statistics. But for beginners in the field, learning the basics of programming and getting to grips with a language like Python, which is commonly used for machine-learning tasks.
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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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In this crash course, you will discover how you can confidently get better performance from your deep learning models in seven days.
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This article is supposed to make you familiar you some of the most useful books on Computer Vision which will give you a thorough understanding of the field and will imbibe deeper understanding in you of the various concepts.
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Here are 10 machine learning courses to help with your spring learning season. Courses range from introductory machine learning to deep learning to natural language processing and beyond.
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This article will take you to the best books that you can find of Deep Learning.
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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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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.
Source: HOB1296
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