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: Kdnugget42726
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: Kdnugget22164
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.
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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.
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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.
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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.
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Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures.
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Here are some resources to learn deep learning frameworks.
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Facial recognition datasets are unfairly dominated by images of white men, so Google hired third-party contractors to go around recording people's faces by offering them vouchers.
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Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing.
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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
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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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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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Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms.
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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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Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell
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Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms.
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These books provide multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
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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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Over the past many years, the Artificial Intelligence revolution has provided a quality response to the different range of technologies.
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Many programming languages are used today, some are used, and some have gone obsolete. In the last few years, the programming scenario has changed drastically as developers and programmers are searching for more universal and approachable languages.
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