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Results for Machine Learning

Is the cloud the key to democratizing AI?

July 03, 2018
These days, AI is expected to drive worldwide revenues from nearly $8 billion in 2016 to more than $47 billion in 2020, across a broad range of industries. There is no doubt that AI has changed the way we run business.


Take a case of Makoto Koike as an example. It takes his mother 8 hours each day to sort cucumbers from the family farm into different categories at the peak of the Japanese harvest. This is such a boring, time-wasting task that he finally made a decision to using automation. Makoto started off with TensorFlow, a popular open-source machine learning framework Despite not being a machine learning expert. Sorting by size, shape and other attributes, the system has an accuracy rate of around 75 %. This proved how AI could transform even the smallest family-run business sooner or later.

Large companies like Google, Apple are well-aware of this transformative power. Most Fortune 500 companies also have dedicated AI teams in place. However, for smaller and medium enterprises in the same situation as Makoto, they have faced difficulties in exploring how AI can hone their business because of the lack of expertise.

Those companies are in need of preparing huge data sets and spend putting their sources into computing power to analyze them, although having  ability to employ AI experts However, the big cloud providers come up with solutions to these issues.

As a service or cloud AI, Machine learning is now a major part of cloud platforms like Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and IBM Cloud. To add AI to business applications, these companies supply their customers with access to pretrained deep learning models—for image recognition. Simplifying the process of building, training and deploying, tools customized models on the cloud.

From Chris Nicholson’s judgment - CEO of Skymind, there are tools data scientists knowing how to code and for software developers not knowing how to properly tune algorithms but knowing to create apps if receiving an API to code against. In addition to this, there are  tools for clickers basically relating through GUIs, which covers the enormous number of people in all over the world.

While the likes of Amazon Rekognition and Google Translation are APIs created around pretrained models, Microsoft Azure ML Studio, Amazon SageMaker, and Google Cloud ML Engine are broadly similar platforms sitting more toward the data scientist end of the spectrum, helping deep learning experts to train, tune, and deploy their models at scale.

To solve a specific business problem, which pretrained models may cannot tackle, deep learning is typically used. This is the issue of the latter approach. Put it in another way, if you want it to identify different types of cucumbers, using an API to recognize different breeds of kittens is bad.

Now you can use a bunch of data on which we trained a model to make predictions about images but that’s a false solution in the sense that it’s still just as hard and just as necessary to train a model on your own data if wanting to customize that solution.

To bridge the gap between highly customized neural networks and the more basic one-size-fits-all pretrained models, launched by Google, Cloud AutoML uses customer data to automatically create a custom deep learning model. Allowing users  to built custom machine learning models for image recognition via a drag-and-drop interface, Cloud AutoML Vision is the first release from the new service.

Disney is one of large companies using the tool  and  it helps Disney’s customers to search its merchandise for particular Disney characters, even the product with no the character’s name. However, preparing their own data for the AutoML service is still a difficulty for some enterprises.
Link:
Machine learning experience for computer engineers
This is the time to deep learning in the cloud
Machine Learning and Deep Learning In The Cloud: Are You Ready?
There could be a challenge for companies to gather data since in the first place, many of them don’t take control over their data well. A great deal of data is specific to an organization, like how they handle invoices or how they do customer lead checking.

To attract more customers, the company is definitely trying to leverage its AI expertise as Google’s cloud business lies a distant third behind AWS and Microsoft Azure. But given the computational resources needed to train and deploy deep learning models, all the major cloud vendors stand to make considerable sums from renting chips for this purpose.

The proportion of  commercial enterprise using AI will be 75% by 2021 according to IDC. There is no doubt that Machine learning tools will become an essential component of any cloud computing service. To make the most of AI, companies need to offer these kinds of capabilities.

Determining about whether a business case is clear for using deep learning tools is a needed task enterprises should do at first. People are much less likely to succeed If not defining the specific problem they want to solve. People are using it in a very specific way to cope with a very specific problem.
Is the cloud the key to democratizing AI? Is the cloud the key to democratizing AI? Reviewed by thanhcongabc on July 03, 2018 Rating: 5

How does machine learning work? All you need to know

July 03, 2018
Many of us may hear about machine learning before. However, you might not have the full understandings of the way it works. Today we will discuss this topic in more details.


Machine learning can be the modern science which gets computers the ability to act, but they can be explicitly programmed. Previously, it gave us a throughout understanding of our human genome, self-driving cars, effective web search, and practical speech recognition. Nowadays, machine learning can be so pervasive that you can use it several times per day without having full knowledge about it. Also, many researchers think this can be the critical way for the human to make the significant progress towards AI at the human level.

How does machine learning work?

To obtain the most valuable result from methods of machine learning, we might need to know the way to pair its best algorithms and let them fit the right processes and tools. SAS comprises sophisticated, rich heritage in term of statistics as well as data mining which comes along with new advances regarding the architecture to ensure the models run at its fastest speed – even in the huge and complex enterprise environments.

In term of algorithms, the SAS graphical interfaces for users may assist you much in building the models of machine learning and implementing the iterative process of machine learning. You do not have to know everything about statistics. Our comprehensive algorithms selection of this field may help you get value quickly from the big data. Such components can be included in a full range in different SAS products.

Following we will discuss SAS algorithms of machine learning include:
  • Sequential covering building of rules
  • Neural networks
  • Gaussian mixture models
  • Decision trees
  • Singular value decomposition
  • Random forests
  • Principal analysis of components
  • Sequence and associations discovery
  • Gradient boosting and bagging
  • Nearest-neighbor mapping
  • Support vector machines
  • K-means clustering
  • Kernel density estimation
  • Self-organizing maps
  • Bayesian networks
  • Optimization for local search techniques (such as genetic algorithms)
  • Multivariate adaptive splines of the regression
  • Expectation maximization

Processes and Tools

Up to now, we may know that it is not only the algorithms created the whole circumstance. Ultimately, we may have a chance to diagnose the secret of getting the valuable things from the big data when pairing the algorithms for all handy tasks with:
  • An end-to-end, integrated platform for all dedicated automation process of the decision comprising from all necessary data;
  • Comprehensive data management and quality;
  • GUIs designed for process flows and building models; 
  • Easy deployment of models so you may get reliable, repeatable results quickly;
  • Interactive exploration of data and model results visualization; 
  • Automated ensemble evaluation of models to identify and recognize the perfect performers;
  • Comparisons of various different models of machine learning to identify the most suitable one quickly. 

A training set of machine learning

If you want to learn much about the effective techniques of machine learning, you may start by gaining and implementing them in practice before getting them directly to work, especially for yourself. However, the more important thing is that you may understand about the theoretical learning underpinnings as well as gain the know-how practice needed to powerfully and quickly apply such techniques to solve new problems. Then, you will learn about the best practices of Silicon Valley in innovation provided that it pertains well to Al and machine learning.

The full course can provide you a broad and comprehensive introduction to statistical pattern recognition, machine learning, and datamining. Topics you may consider include:
  • Best practices of machine learning (variance /bias theory; the innovation process of Al and machine learning);
  • Unsupervised learning (deep learning, clustering, recommender systems, and dimensionality reduction);
  • Supervised learning (non-parametric/ parametric algorithms, support vector machines, neural networks, and kernels).
If you complete this course, you may draw your own lesson from numerous applications and case studies. In addition, you can also learn the way to apply all learning algorithms smoothly to building all smart robots (control, perception), database mining, text understanding (anti-spam, web search), computer vision, audio, medical informatics, and so on.

An application of machine learning - Natural-sounding robotic voices

The “robotic voice” term can be defined clearly soon with the blooming text-to-speech system performance. Today, we may think of the speech synthesis which is an example of complement. Occasionally, this application of machine learning is indeed a strong competitor to all human voice-over announcers and talents.

The publications mentioning DeepVoice, WaveNet, and Tacotron are considered as important milestones for us to continue to build the acoustic forms passing of all Turing tests. However, training the speech synthesizer can be a resource-intensive, time-consuming task which is outright frustrating sometimes. The demos and issues published on the Github repositories can focus more on replicating research results. They are indeed a truthful testimony to the above fact.

Adversely, all platforms of cloud computing covered in the series —  IBM Watson, Amazon Web Services, Microsoft Azure, and Google Cloud — create the available text-to-speech conversion at the service call. This fact opens up many exciting opportunities which can develop rapidly to engage the conversational applications directly with increasingly natural-sounding and flexible voices.

Conclusion

In short, this article did provide you helpful information regarding how machine learning works, its processes and tools, a training set, and an application of machine learning. In the case that you need basic guidance stated on which algorithm of machine learning to utilize for, you may want to read additional articles in our website.
How does machine learning work? All you need to know How does machine learning work? All you need to know Reviewed by thanhcongabc on July 03, 2018 Rating: 5

What are some popular machine learning methods?

June 29, 2018
Meta Description: there are several machine learning methods. Of which, unsupervised learning and supervised learning are two widely adopted methods we may all know. Let’s look at the overview of some popular machine learning methods.

All you need to know about machine learning methods

What is supervised learning?

As far as you may know, algorithms of supervised learning are educated using some labeled examples, like an input without knowing its desired output. In more details, a piece of equipment may have many data points with the “R” (runs) or “F” (failed) labels. This learning algorithm may receive all inputs came along with its corresponding outputs. In this way, such algorithm learns a lot by comparing the actual output of this method with all correct outputs in order to find as many errors as possible. Then, it modifies the machine learning model accordingly.

There are a variety of methods for your choice, such as classification, regression, gradient boosting, and prediction. In practice, supervised learning usually uses specific patterns to assist you in predicting the label values on additional data which is unlabeled. This method of machine learning can be commonly and widely used in some applications that historical data may predict events likely happened in future. Also, it can surely anticipate when insurance customer tends to file his/her claim or transactions related to our credit cards tend to be fraudulent.

What is unsupervised learning?

It is normally used against information and data that do not have any historical label. There is no right answer for the system. The algorithm here must be figured out what can be shown. The suggested goal can be to explore all the data as well as find the necessary structure within. By this way, unsupervised learning may work effectively on the transactional data. In details, it identifies customer segments throughout with the same attributes that can be treated in the same way for your marketing campaigns.

Otherwise, it may search for the primary attributes which separate segments of customers. Popular techniques can be listed here include the decomposition of singular value, nearest-neighbor mapping, self-organizing maps, and k-means clustering. These algorithms can be also utilized to segment all recommend items, text topics as well as identify outliers of data.

What is between them?

We want to discuss a method in unsupervised learning and supervised learning. Yes, we are talking about semisupervised learning. This method is used widely for the similar applications as the first one, supervised learning. However, it can handle both unlabeled and labeled data for educating and training. The portion should typically be a small labeled data amount and a large unlabeled data amount. The reason for this circumstance is that unlabeled data cannot take as much effort as acquired and require fewer expenses.

This learning type is normally used with some methods like prediction, classification, and regression. Semisupervised learning can be useful when your labeling costs are a bit high to allow you to have a training process of fully labeled data. Early examples include identifying the face of people when using webcams.

Reinforcement learning

This method is usually used for navigation, robotics, and gaming. With the purpose of reinforcement learning, such algorithm discovers all the facts through error and trial which actions the yield with the great rewards. Reinforcement learning owns three main components: the actions (what an agent is able to do), the agent (a decision maker or a learner), and the environment (with which an agent interacts).

Your objective here is to let the agent have the opportunity to choose the necessary actions which maximize all expected reward toward a given time amount. The agent can reach its goal faster by pursuing a dedicated policy. And to learn your best strategy is the learning goal in reinforcement aspect. Provided that humans can create maximum three models per week; they can have thousands of them a week by using machine learning.

Does data mining differ to deep learning and machine learning?

Although these methods own the similar goal which is to extract relationships, insights, and patterns that are used to decide – they will have different abilities and approaches.

Data Mining

This method comprises many methods which can extract insights directly from data. Data mining might involve the machine learning and traditional statistical methods. It applies several techniques from different areas in order to identify patterns which are unknown previously from data. Therefore, data mining may include all analytics areas, such as analysis of time series, statistical algorithms, text analytics, machine learning, and so on. Also, it is a complete set of the practice and study of data manipulation and data storage.

Machine Learning

You should be aware that the primary difference that machine learning brought to us is indeed similar to statistical models. Its goal can be to understand its data structure – match theoretical distributions dedicated to the well-understood data. So, there is one statistical model theory behind that. Such theory can be mathematically proven. However, machine learning requires that the data needs to meet certain assumptions.

Deep learning

It combines computing power advances as well as special kinds of several neural networks so you can learn all complicated patterns with the large data amounts. Techniques of deep learning may be currently the art state for identifying and determining objects in words and images in sounds. According to the recent studies, researchers currently want to apply the successes in the pattern recognition. Therefore, it may perform much more complex tasks like medical diagnoses, automatic translation of the language, and numerous different important business and social problems.

Verdict

In short, machine learning methods have developed mainly based on our computer using the ability in order to prove their data for a comprehensive structure. This situation can be overcome even if there is no theory stated of what a new structure can be described. The applicable test dedicated to a model of machine learning is the validation error which can be used on the new data. It may not stop as only one theoretical test which proves the null hypothesis. In fact, machine learning methods often utilize the iterative approach in order to learn directly from data, so such learning is easily automated. Then, passes may be run through them until finding the robust pattern.
What are some popular machine learning methods? What are some popular machine learning methods? Reviewed by thanhcongabc on June 29, 2018 Rating: 5

An Ultimate Guide to Machine Learning

June 27, 2018
Meta Description: With the computing technologies, there are more and more people know about machine learning today. Its iterative aspect is indeed vital as models can be exposed directly to new information and data before adapting independently.

What is Machine learning?

Machine learning can be known as a data analysis method which automates analytical building model. It is also an artificial intelligence branch and was built from the basis that systems may learn much from data, then identify patterns before making decisions with the minimal intervention of the human.


This definition was first introduced from the theory that computers may be enabled to learn to perform different tasks without any accompanying programs and pattern recognition. For researchers of artificial intelligence, their main expectation is the desire to see whether computers can learn well from data. Such computers gain knowledge from their previous computations in order to produce repeatable, reliable results and decisions. It is not a new science, but it gained the fresh momentum.

What do you know about its basics

While many algorithms of machine learning have stayed healthy for a while, their ability to apply complex calculations of mathematics automatically to the big data faster and longer is a new development. Let’s look at a few publicized examples regarding applications of machine learning you may want to consider:

After understanding what machine learning is, following we will discuss why it becomes so popular. Also, this article will provide you with all necessary things about the SAS technology – what its functions are, how does it works as well as the way it affects how you do business.

Machine learning and its essence

An online recommendation may offer different benefits for famous Internet partner such as Netflix, Amazon. Otherwise, by knowing what your customers say about you and update their status on Twitter, applications of machine learning can be used for our everyday life. Furthermore, it may combine well with the creation of linguistic rules.

In statistics, you can call a target as one of the dependent variables. In this field, a target can be known as a label. So, one variable in statistics may be considered as a machine learning feature. A transformation of machine learning in statistics should be its creator.

Do you think that machine learning is important?

Resurging machine learning interest can let out from the similar factors which made Bayesian analysis and data mining extremely popular. It is much more comfortable and cheaper for you to deal with available data varieties, growing volumes, and computational processing than data storage with the affordable expenses.

All of the above things mean it is possible to automatically and quickly produce models which can analyze more complex, bigger data along with deliver more accurate, faster results on a large scale. By building a precise model, your organization has a good chance to identify profitable opportunities as well as avoids your unknown risks.

How to create a good system of machine learning

Nowadays, machine learning can help organizations to make more precise decisions without the intervention of the human. By using a lot of algorithms to start building models which uncover connections. For this reason, you may need to learn additional about the shaping technologies of our world, challenges, and opportunities for business machine learning. Then, we can implement these applications successfully in our organization. Let’s look at the ways to create a good system of machine learning as follows:
  • Scalability.
  • Capabilities of data preparation.
  • Iterative and automation processes.
  • Ensemble modeling.
  • Algorithms – basic & advanced.
  • Machine learning and its infographic.

Who is using machine learning?

Almost all industries working and coordinating with large data amounts recognized the technology value of using machine learning. Therefore, by gleaning its insights from the real-time data, organizations often gain a competitive advantage or work efficiently.

Financial services

Many financial businesses like banks use the technology of machine learning for two primary purposes: to prevent fraud, and identify important data insights. Such insights may define investment opportunities, as well as help investors to know the right time to trade. Besides, data mining also identifies your clients who have profiles with high-risks and use cyber-surveillance in order to pinpoint fraud warning signs.

Government

Secondly, government agencies like public utilities and safety have the particular requirement for this field since they own multiple data sources which may be mined detailed for insights. For example, analyzing a variety of sensor data, increasing efficiency ways and saving money. Therefore, machine learning also helps to minimize the identity theft and detect fraud.

Transportation

To identify trends and patterns, analyzing data can be the key point in the transportation industry that relies much on making efficient routes and predicts many potential issues to increase the profitability. The modeling aspects and data analysis of the machine learning field is important and critical tools to transportation organizations such as delivery companies and public transportation.

Sales and marketing

Items recommended by websites you like can be based on your previous purchases. Then, you may use machine learning in order to analyze the buying history and promote all of your interested items. Your data capturing ability can be analyzed and used to personalize your shopping experience. Therefore, your retail future can be implemented with a wonderful marketing campaign.

Healthcare

Thank much to the sensors and wearable devices which use different data to analyze and assess the health of a patient in practice, machine learning can be a trend which grows fast in the industry of health care. The technology also helps many medical experts to analyze data in order to identify red flags and trends that lead to enhanced treatment and improved diagnoses.

Gas and oil

Machine learning can assist you to find new sources of energy. What’s more? You can analyze ground minerals, predict refinery failure of sensors, and streamline the oil distribution so it can be more cost-effective and efficient. A large number of use cases of machine learning here still expands in the near future.

Conclusion

In short, we hope this Insights Article will provide you necessary knowledge of machine learning. To emphasize its power, you may know that it makes credit scoring efficient. By this way, it can change our organization in some positive ways. Some partners did consider applying it to IoT and this field is used widely in order to achieve high-efficiency levels when utilizing it pn the Internet. Therefore, we hope to help you to explores our topic thoroughly.
An Ultimate Guide to Machine Learning An Ultimate Guide to Machine Learning Reviewed by thanhcongabc on June 27, 2018 Rating: 5

How to get started with AI – before it’s too late

June 21, 2018
These days, Artificial Intelligence is becoming more popular because of capability and influence at an astonishing rate across virtually every industry. Yet the speed and scale of AI is such that some long-standing norms are facing disruption. In this article, we would show you 5 ways  to explore what each of us can do to prepare for this bold new future and make the most of AI.

1. Make yourself  an AI expert 

Recently, many people believe that it will take too long to learn it yourself. But these are early days. Thanks to the development of the Internet, AI becomes the biggest opportunity; it’s just getting started. Relying on your computer science and math expertise, you’ll want to brush up on the following:
  1. Statistics
  2. Calculus
  3. Linear algebra
  4. Algorithms
  5. Convex optimization
  6. Graph theory
  7. Current programming tools and trends
  8. Data analysis
To begin with, you should grasp practical skills helping you understand machine learning at a low level. Here they are:
  1. Data wrangling
  2. Cross validation
  3. Distributed computing
  4. Data visualization
  5. Database management
  6. Feature engineering
In addition, if you are processing your big data set to choose features and explore its statistical properties, R would be a great tool to familiarize yourself with. An ideal way to get up to speed on these subjects are Data science bootcamps like The Data Incubator and Zipfian Academy. Also, it’s noticeable that the exact role and necessary skill set of a data scientist varies depended on the problem they’re attempting to solve.

2. Employ an AI expert

As the ‘sexiest job of the 21st century’, the demand for data scientists may be 60% greater than supply by 2018 in job market. Data scientists are the combination of  programming, math expertise and analytic skills, which makes them so attractive.

The data scientists are typically employed by large research universities or large tech companies like Google and Facebook. Although the prospect of building a self-driving car is likely more rewarding than creating an AI model to help a small company automate insurance forms, the use cases for AI are getting more intriguing. In addition, if you run a cool project, you may merely convince one of these mythical data scientist unicorns to join your team. You can buy up a university robotics department if you’ve got millions in the bank.

3. Open-source libraries and frameworks

In 2006, machine learning frameworks made outstanding progress. Over just the last  five months, Microsoft, Baidu and Amazon have all open-sourced their own ML libraries (CNTK, WarpCTC, and DSSTNE, respectively), OpenAI released OpenAI Gym and Google has continued to push major updates to Tensorflow.

Only by stitching together Tensorflow’ s ‘building blocks’, engineers can code an efficient neural network without too much time waste and deep math knowledge, and importantly declining rooms for error. ML libraries- a great reservoir for machine learning practitioners, provide engineers with much control over a model’s outcome as well as the ability to tweak and enhance upon it.

4. Statistical analysis tools

Many companies of all sizes have troubles with mountains of user data. If you have data with no the patterns, statistical analysis tools are a great resource to make your data work harder for you.

You can merely find a few services and tools taking varied approaches to this problem. A service like BigML or DataRobot can take all of your data, try different machine learning models, and choose the ideal fit to your specific business problem.

To find patterns in noisy data sets and to gain more information from your own data, these back boxes would be a great tool with no machine learning background required. If you’re not satisfied with the model, you’ll have to go back to the service and generate a new model.

5. APIs

Using API could be the fastest method to apply AI technology into your business. IBM and Google, alongside many new AI startups, have released APIs related to natural language processing, visual recognition, and semantic analysis.

It’s noticeable that each API has a mission to do just one certain thing. Take Google’s Cloud Vision API for example. Boasts of its ability to help applications see does very well at that for certain tasks. You would need to build your own neural network and train it on images of that anomaly so it can draw a lesson about how to identify it.

Currently, APIs are likely to determine emotions of tweets, translate languages, translate text to audio, recognize the emotion of human faces and analyze data. It’s a great way to boost the intelligence and productivity of your app fast if your problem falls into the wheelhouse of one of these APIs.

If you put these ways together, the rest should follow as you transition from the Information Age to the Insight Age.
How to get started with AI – before it’s too late How to get started with AI – before it’s too late Reviewed by thanhcongabc on June 21, 2018 Rating: 5

Machine learning experience for computer engineers

June 10, 2018
Robert Heinlein published a book called "A Door into summer" in the mid-1950. In his book, skilled mechanical engineer hooked up some "Thorsen hoses" for pattern matching memory and some "side circuits to include decision making" and generated an entire industry of intelligent robots. In other to make the story more imaginable, in 1970 it was set well into the future. These robots are involved in tasks like washing dish illustrated to them and then repeat the process entirely. 


I don’t think I have to inform you, but it didn't happen in the right way. It may have seemed believable in 1956, but in 1969 it was apparent that it wouldn't turn out in 1970. Every ten years the capability for an average engineer to build a synthetically intelligent machine seemed to be impossible as time passed. As technology gets better, the enormous complexity of the problem became evident as different layers of complexity were discovered.

The major setback was not that the machine learning wasn’t solving the problems, it was. For instance, in the mid-90s all credit card transactions were scanned using neural networks to avoid fraud. In the late 90’s Google was investigating the web for highly developed signals to support search. Unfortunately, your day to day computer engineer did not have the opportunity of building such system expect if they go back to school for a Ph.D. and meet a group of like-minded friends that would do the same thing. Machine learning was hard, and every new domain required breaking massive amount of new ground. Even the most outstanding researchers could not crack hard problems like image credit in the real world.

I am excited to say that this condition has changed significantly. I don’t imagine that anybody will want to found an auto-magical, Heinlein-style and all-robotic engineering company in the future. But it is easy for a software engineer without any advanced training to build systems that do remarkable things. The amazing part is not that computers could do all these things. But it has been identified since 1956 that this would be possible anytime! The most surprising thing is how far we have come in the last decade. The time taken to do a Ph.D. research for ten years is now a simple project for a weekend.

Machine learning is getting accessible (or more available)

In our latest book on “Machine Learning Logistics” Ellen Friedman, and I explained a system known as TensorChicken. It was built as a fun home project by our software engineer friend, Ian Downard. The problem to be solved was the blue jays that usually enter our friend’s chicken area to peck the eggs.  He decided to build an automated vision system that could detect a blue jay so that some necessary action could be taken to prevent the pecking.

After learning an in-depth learning presentation by Google engineers from the TensorFlow group, Ian followed the guidelines and built a similar system. He started with a partial model called Inception-v3 and training it to the task of blue jay detecting a thousand images taken by a webcam in his chicken area. The final result was deployed on Raspberry Pi, but plausibly quick response time requires a bit beefier like Intel Core i7 processor.

During the research, Ian was with different sorts of people, many of them are not qualified data scientists or building exceptional bots to all sorts of things. Also, many numbers of developers started working on a selection of different machine learning projects when they discovered that the learning machine and deep learning have become more available. Many developers have started to occupy different positions as data engineers in a "data operations" mode of work, where data-focused skills (data scientist, data engineering, and architect) are combined with a expand approach to build things like machine learning systems. 

It is interesting that a computer can easily be trained to detect a blue jay, using an image detection model. In some cases, regular folks meet and do this and a whole lot more besides. All that is required is a few pointers for proper technique, and a bit of rest in your frame of mind, especially if you are familiar with software development.

Building models are different from building regular software. It is data-driven rather than design-driven. You need to check the system from an empirical perspective and depend little on the experimental proofs of the function instead of the careful implementation of a good design that comes with unit and integration tests.  Also, always remember that the difficult domains where machine learning has become straightforward.

Right next door, are situations that are still hard and that usually require more complicated data science expertise with more calculations. So, you need to test and prototype your answer. Make sure you set up your farm or hen house when you know that the problem is not hard. Don't even expect the farm after it seems to work for the first time. You must be suspicious of good results just like any good data scientist.

Necessary data knowledge for machine learning beginners

The remaining article explained some of the tactics and abilities that developers require to use machine learning efficiently.

Let the data prove it

You can quickly think of a design, build your software and authenticate the correctness of your solution independently and directly in a sound software engineering. Most times, you can even show that your software is correct. The real world does intrude a bit, mainly when humans are involved, but if you have proper specifications, you can implement a right solution.

With the help of machine learning, you don't have a rigid specification. Some data represents the experience with a system, and you have to build a system that will work in the future. To detect if your system is working correctly, you have to measure performance in practical situations. In regards to the data-driven, the specification-poor approach of development can be complicated, but it is an essential step if you want to build systems with machine learning inside.

Learn to detect the better model

It is easy to compare two numbers. If the numbers are valid values, it is easier to spot the more significant value and you are done. When it involves the correctness of a machine learning model, it is not simple. There are many results for the models you are want to compare, and there is no precise answer. However, the most fundamental skill in building machine learning systems is the capability to check the history of the decision of two models that were made and determine the model that is better for your situation. This result requires basic techniques to check the values that have an entire cloud of values instead of a single value. Also, you need to be able to visualize data as well. Histograms scatter plots, and many other related techniques will be required.

Be apprehensive of your results

Along with the ability to decide which variant of a system is doing a good job, it is important to be wary of your results. Are your conclusions a statistical fluke that will go against your data? Has the world changed ever since your assessment, thus varying the better system?

Building a system with machine learning means that you will need to monitor the system to ensure that it is doing what you thought it was doing, beginning with. This doubtful nature is needed when you are dealing with unclear comparisons in a changing world.

Build various models to throw away

It is a maxim in software development that you have to build one version of your system in other to throw away. The idea is that until you develop a working system, you will not recognize the problem adequately to build that system well. You can build one version to learn and then make use of that learning to design and build the real system.

With the machine learning process, the condition remains constant. Also, instead of building one throwaway system, you must be ready to create many of variants. Many of these variants can make use of different learning technologies or different settings for the learning engine. Many other variants might be entirely different restatements of the problem or the data that you make use to train the models. For example, you might detect a surrogate signal that you can use to train the models if the signal is not really what you desire or expect. This can give you ten times data to train with efficiently. Or you can restate the problem in a manner that will make it easier to solve.

The world can change as well. This is possible, for example, when you are building models to detect fraud. After creating a successful system, you will have to change in the nearest future. The hackers will identify your countermeasures, and they will modify their behavior. You will need to respond to new counteractions.

However, for successful machine learning, decide to build many models to throw away. Don't wait for a golden model that will provide the solution forever.

Don’t be frightened to change the condition

Firstly, the question that you try to solve with machine learning is typically not the right one. Often it is usually the wrong one. The answer of asking the wrong question can be a model that is not possible to train or training data that is difficult to gather. Or it might be a situation where a model that provides the best answer still has little or no value.

 Recasting the problem can give you a condition where a simple model will build higher value. I had difficulty once that was deals with the recommendation of sale items. Getting even small gains with some appealing heavy techniques was tough.

As it happens, the high-value problem was to check when good items went on sale. It becomes trivial when you identify the issue of which products to recommend because there are many good products to recommend. At the wrong times, there was nothing to recommend anyway. Modifying the question made the problem easier to access.

You can start small

It is valuable to be able to deploy your innovative system in case of any single sub-problem. This will allow you to stay focus on your effort, learn more within your problem domain and get support in your company as you build models.

After, start big 

Ensure that you get adequate training data. If it is possible, you can get ten times more than you need or think.

Domain skills is required

In machine learning, finding out how a model can easily decide or predict is needed. Also, if you have more understanding in the domain, you are likely to ask the right questions and be able to integrate machine learning into a workable product. Domain knowledge is essential to find out where a sense of judgment is required to be included and where it might reasonably be added.

Coding knowledge are important

There are many tools out there that will allow you to build machine learning models using simple drag-and-drop tools. The fact is, the majority of the work in making a machine learning system does not have anything to do with models or machine learning. All that is required is to gather training data and to build a system to make use of the model's output. This provides good coding skills that are extremely valuable. However, there is a different tactic to code that is written to manipulate data, but that is not difficult to pick up. So the fundamental skill of a developer is to make useful skills in many varieties of machine learning.

Most new techniques and tools are readily accessible, and it helps practically any software engineer to build systems that make use of machine learning to do some incredible things. Fundamental software engineering skills are precious in building these systems, but you have to supplement them with a little of data focus. The best method to choose these new skills is to begin today in building something exciting.
Machine learning experience for computer engineers Machine learning experience for computer engineers Reviewed by thanhcongabc on June 10, 2018 Rating: 5

What is machine learning ?

June 09, 2018
You must have heard about machine learning many times lately. It is also known as artificial intelligence; machine learning is a division of AI, both can be traced with their sources from MIT in the 1950s.


Machine learning is usually encountered every day, whether you believe it or not. The technology that maintains self-driving cars from crashing into things, Alexa and Siri voice assistants, Amazon and Netflix recommendations and Facebook’s and Microsoft’s facial detection all are results of advances in machine learning.

While it is not too complex for human brain, systems that depend on machine learning have accomplished some remarkable feats such as defeating human challengers at Jeopardy, Texas Hold ‘em, chess, and Go,

Neglected for decades as unrealistic and overhyped (the recognized”AI winter”), both machine learning and AI have enjoyed a great resurgence in the last few years. Thanks to many technological breakthroughs and the huge explosion in simple computing horsepower, including the price of machine learning models.

Self-trained software

What is machine learning? Let’s start by saying that machine learning is not human-programmed, conventional and hand-coded computing application.

Unlike conventional software that follows instructions but not better at improvising, machine learning systems are coded themselves efficiently and developed their instructions by general examples.

The model example is image detection. This shows that machine learning system has enough photos of labeled dogs, including images of babies, trees, bananas, cats, or any other object apart from marked "not dogs." If the system is appropriately trained, it will undoubtedly get better at recognizing canines without human being detecting what a dog is supposed to look like.

The spam filter inside your email program is the best example of machine learning in action. After it has been exposed to many spam samples, including non-spam email, it knows to recognize the significant characteristics of those unwanted and malicious messages. It is not just right, but it’s pretty accurate.

Unsupervised and Supervised learning

This type of machine learning is known as supervised learning that means that someone can uncover the machine learning algorithm to a massive set of training data and look at its output. After constantly tweaked its settings until you get the expected result when displayed on data it had not seen before. This process is similar to clicking “not spam” push button in your inbox when the filter clicks on legitimate message mistakenly. The more you follow this process, the more accurate the filter ought to get better.

The most recognize supervised learning tasks involve prediction (i.e., “regression”) and classification. Predicting stock prices is a good example of a regression problem.

The second type of machine learning is known as unsupervised learning. The system pores over large amounts of data to learn how “normal” data looks like, to recognize hidden and anomalies patterns. Unsupervised machine learning is helpful when you do not know what you are looking for and it is not possible to train the system to find it.

Unsupervised machine learning systems can detect patterns in a large amount of data many times quicker than humans. That is the reason why banks make use of them to identify fraudulent transactions; security software used them to unfriendly flag activity on a network and marketers employ them to detect customers with similar attributes.

The two examples of unsupervised learning algorithms are clustering and association. Clustering is a unique tool for customer segmentation while association rule learning can be used for recommending engines.

Restrictions of machine learning

Since every machine learning system builds its connections, the way in which one works can involve a little black box. It is not possible to reverse engineering process to find out why your system can be distinguished between a Persian and Pekingese. It doesn’t matter, as long as the system is working properly.

A machine learning system is also good as the data that has been exposed. A typical example is “garbage in, garbage out.” When a system is exposed or poorly trained to a small data set, a machine learning algorithm can generate results discriminatory apart from providing wrong information.

In 2009, HP got it trouble when facial technology recognition built into the webcam on an HP MediaSmart notebook was unable to recognize the faces of African Americans. Also, in June 2015, flawed algorithms in the Google Photos app mistakenly labeled two black Americans as gorillas.

Another remarkable example: In March 2016, Microsoft’s unfortunate Taybot experimented to check if an AI system copy human conversations by learning from tweets. In less than 24 hours, suspicious Twitter trolls had changed Tay into a hate-speech-sending out chatbot from hell.

A machine learning glossary

Machine learning is the perfect tip of the AI berg. Many other terms closely related to machine learning are deep learning, cognitive computing, and neural networks.

Neural network: A computer structural designed to copy the structure of neurons in human brains, with all artificial neuron (microcircuit) linking other neurons inside the system. Neural networks are arranged in layers along with neurons. Each layer connects data to multiple neurons in the next layer until they get to the output layer. The final layer is the region where the neural network offers its best predictions to determine the dog-shaped object along with a confidence score.

There are many types of neural networks for solving multiple types of problems. Networks with a large number of layers are known as “deep neural networks.” Neural networks are one of the essential tools used in machine learning situations, but it is not the only method.

Deep learning: This is a type of machine learning through steroids with the use of multi-layered (deep) neural networks to achieve a result based on incomplete or “imperfect” information. DeepStack is the deep learning system that defeated specialized poker players through continuous computing its approach after each round of stakes.

Cognitive computing: This is the term used by creators of Watson, IBM, and the workstation that kicked humanity’s ass at risk in 2011. The variation between artificial intelligence and cognitive computing, in IBM’s point of view, instead of changing the human intellectual, cognitive computing is intended to supplement it. These processes enable medical doctors to diagnose patients more effectively, help financial executives to make good recommendations and lawyers to check law cases faster, etc.

This is an extremely superficial impression. Also, people that want to go deeply into machine learning and the intricacies of AI can begin with this semi-wonky lesson from the University of Washington’s Pedro Domingos or InfoWorld’s Martin Heller on “The meaning of deep learning” including series of Medium posts from Adam Geitgey.

Regardless of the hype about AI, it is not an exaggeration to say that machine learning including the related technologies in placed are transforming the world as we all know it. The best time to learn more about it is now before the machines become entirely self-aware.
What is machine learning ? What is machine learning ? Reviewed by thanhcongabc on June 09, 2018 Rating: 5
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