Now more and more people want to talk to your own virtual personal assistant, let Siri/Alexa/Rokid help you only take your pencil to complete the probe, ticket booking, set the alarm clock to the business, meeting also can remind you to take the medicine, so one does not need to pay warm padded jacket can not liked about it? Virtual Assistants are closer to the reality of personal assistants, and behind it is the depth of learning technology. Other than the virtual assistant, advanced learning technology will also be the future of computer vision, automatic driving core technology, speech recognition, and other fields. Depth study and practice the four key elements: computation, algorithms, data, and application scenarios, as guardians of the four animals to ensure that the depth of learning and practice, are indispensable.
Depth is not less than two hidden-layer neural network to nonlinear input transformation or learning technology by building deep neural network analysis of the activities carried out. Deep neural network consists of one input, a number of hidden layers, and an output layer. Each layer has a number of neurons and connections between neurons the weights. Each neuron simulation of biological neurons and connections between nodes simulate the connections between nerve cells. Summed up like this:
This flow chart is a special property of depth (depth): from an input to an output of the length of the longest path. Deep learning is not a new concept, but in 2006 by Hinton, who led a wave of outbreak. However in recent years, although a lot of people are talking about deep learning, but which is the practical application of this technology? Starting mature companies rely on deep learning what elements are needed? Here is our opinion.
| Computing power
First, the depth of complex neural network, data, and computing capacity. Depth of neurons in the neural network, number of connections between neurons are also pretty amazing. From a mathematical point of view, each neuron to include mathematical calculations (such as the Sigmoid, ReLU or Softmax function), the need to estimate the amount of parameters also have a great. Speech and image recognition application, neuron, tens of thousands of parameters of tens of millions of, model complexity leads to calculation. So computing power is the Foundation of the application deep learning.
Not only so, calculation capacity also is promoted depth learning of weapon, calculation capacity more strong, also time within accumulated of experience on more more, and Diego generation speed also more fast, Baidu Chief Scientist Wu Enda Dr think depth learning of frontier are transfer to high performance calculation (HPC), this is he currently in Baidu of work gravity one of, Wu Dr think depth learning Shang of many success thanks Yu active to pursuit available of calculation capacity, 2011 Jeff Dean (Google Tensorflow one of the designers of the second generation of artificial intelligence learning system) founded and led Google depth study group, using Google's cloud extending deep learning; it allowed deep learning to push the industrial sector. In 2013, Dr Coates, who established the first deep learning of HPC systems, scalability, improved the 1-2 of magnitude, caused a revolutionary advance in deep learning--computing power of that deep learning support and role are irreplaceable.
Currently this aspects technology in leading status of also is like Baidu, and Google such of large Internet company, certainly also some like horizon robot such of start-up company in the field quite achievements, by Baidu depth learning Institute head Yu Kai Dr founded of horizon robot company design of depth neural network chip compared traditional of CPU chip can support depth neural network in the image, and voice, and text, and control, aspects of task and not to do all things, such than in CPU Shang with software to efficient, 2-3 increase in orders of magnitude.
| Algorithm MCM case
In calculation capacity became increasingly cheap of today, depth learning tries to established big have more also complex have more of neural network, we can put algorithm understanding for depth learning of neural network or calculation thinking, this neural network more complex, capture to of signal on more precise, currently compared common of algorithm including depth faith network (Deep Belief Networks), and volume product neural network (ConvolutionalNeural Networks), and Restricted Boltzmann machine (Restricted BoltzmannMachine) and stacked automatically encoder (Stacked Auto-encoders), represented by the advanced Convolutional neural network learning method was by far the most used and most effective.
But currently problem is everyone put focused degrees put in has data and operation, because neural network itself differences not is big, and neural network of core algorithm upgrade up too difficult, still faced with like local optimal problem, and cost function and whole neural network system of design, problem, but this also to many venture company to new of thought, why opposite, avoid that contains with took of "bridge", if can will algorithm optimization, future is limitless of.
| Data
Deep learning is fast becoming a hot topic in the field of advanced data analysis, and the absolute amount of data is to facilitate deep learning tools and technological development one of the key factors. DanielMcDuff Affectiva, Chief Scientist and Research Director, emerging after the company has accumulated enough data, technology can play a better role. For those who the application deep learning and development needs a lot of time training, and improve not only in product promotion need more user data in real time, continuous iteration, update.
Study in depth, China still has a good chance in the competition, availability of data over the Internet, as well as low-cost labour, China will bring vast amounts of data and very low cost of data tagging. But the problem is that large amounts of data for domestic market are Internet giants such as BAT control, start-ups are very hard to get the data to improve, update, deep learning of neural networks, especially after the product launch, also could face malicious marginalization of a large company, get data more difficult, is not said to be surviving in the crevice about it. MCM iPhone 6 case
| Application scenarios
Depth learning technology currently application of scene not more, most general is most success of field is voice recognition and image processing this two application scene has, zhiqian mentioned of three big God beast--calculation capacity, and algorithm and data belongs to development end, application scene is belongs to consumption end level, with future depth learning technology of constantly development and user of needs upgrade, depth learning of application scene will increasingly more, like many intelligent phone built-in of people face recognition function to on photos for classification, has can reached quite of accurate rate ; PayPal financial tools, such as a face recognition is most likely to increase security ... ... Depth of the future study is not limited to speech recognition and image recognition in both areas, there are many more possibilities. For those startups, with Google, Facebook, Amazon, BAT has over ten years of precipitation data, such as large companies compete in this mature market, rather than to develop a piece of their own little world.
Now depth learning of hot degree not weak Yu any other of field, Internet giant are are in trying to points this block cake, actually wants to do depth learning calculation capacity, and algorithm, and data, and application scene this four Dharma God beast integral, and BAT, giant in these aspects are accounted for do resources Shang of advantage, for start-up company for hard four points both, especially data aspects, so using itself of compared advantage caught which one points for innovation, regardless of is calculation capacity, and algorithm also is application scene Shang, As long as innovation place, can help you seize the initiative in the market.
Lei feng's network (search for "Lei feng's network", public interest) Note: this text for micro-line letter public capital (public number: LinearVenture) and the authorized network of Lei feng. Please contact our authorized, and keep the source and author, no deletion of content. Linear capital official public platform, focused Pan-intelligent, Fintech and VR/AR three major areas of early-stage investing.
No comments:
Post a Comment