Deep Learning
Deep Learning is a technology that uses computational networks to reduce the amount of data required to perform an analysis.
23 Simple Things What About Deep Learning
- Deep Learning is not so new. <a style="color: #000000;" href=" removed link " target="true">Deep Learning has been around for a long time.
- DL systems use vast amounts of computer hardware and software resources ( GPUs, FPGAs, cloud services) to learn.
- DL is starting to get useful, and increasingly so.
- DL is a way of artificially generating natural language.
- DL is not making decisions, it is making the neural network read data to understand how to make a decision.
- DL is the science of building models to learn and understand the world as it exists.
- DL models are typically executed on GPU hardware to massively parallelize and achieve the scale required for AI capabilities.
- DL works in regions of the brain that do not need to work for something like processing speech, finger movements, or lip-reading.
- DL is an integral component of AI; it has to be. We can't just be adding in new categories as we build products, we need to learn through our products, and that takes learning in time.
- DL methods are nonlinear models of multiple data types.
- DL models are typically executed on GPU hardware to massively parallelize and achieve the scale required for AI capabilities.
- DL is applying the idea of generative models to artificial neural networks and creating artificial neural networks that learn.
- DL systems will be able to do pretty much everything a human-level AI can do today, and in a far cheaper and smaller size than a human brain.
- DL does not teach you how to run it, or how to design the models. It does not explain the algorithms and not even teach you how to measure them.
- DLMs are not on-ramps into the AI/ML revolution but instead provide an extension of our currently limited models of intelligence.
- DL is a new way of thinking about the world.
- DL predicts semantic representations and contains conditional probabilistic logic.
- DL has no limits to its capability.
- DL is too big of a risk to make yet. But what I think is real is that we are exploring a very powerful new technique that can potentially solve some of the biggest questions that humanity faces and that makes us understand better.
- DL is clearly impressive. DL techniques can, for example, break humans on a given task (see above) or outperform them in tasks that we have little idea about (see below).
- DL is a future word of the future when it comes to implementing deep learning on current architecture.
- DLs are going to change the way we design our systems, and not because we think they're a bad idea.
- DL intelligence and creativity is based on its power to understand data, unify and combine disparate sources and kinds of data.
Source : <a style="color: #000000;" title="Deep Learning Co UK" href=" removed link " target="true">Deep Learning Co Uk