Go Programming

Go Programming

1592320560

Concurnas: the New Language on the JVM for Concurrent and GPU Computing

Concurnas is a new open source JVM programming language designed for building concurrent and distributed systems. Concurnas is a statically typed language with object oriented, functional, and reactive programming constructs.

With a concise syntax that hides multithreaded complexity, and native support for GPU computing, vectorization, and data structures like matrices, Concurnas allows for building machine learning applications and high performance parallel applications. In addition, Concurnas provides interoperability with other JVM languages like Java and Scala. Concurnas supports Oracle JDK and OpenJDK versions 1.8 through to the latest GA release 14.

InfoQ spoke to Jason Tatton, creator of Concurnas and the founder of Concurnas Ltd., about the language, some of its design decisions, and features.

#machine learning #jvm languages #development #architecture & design

What is GEEK

Buddha Community

Concurnas: the New Language on the JVM for Concurrent and GPU Computing
Go Programming

Go Programming

1592320560

Concurnas: the New Language on the JVM for Concurrent and GPU Computing

Concurnas is a new open source JVM programming language designed for building concurrent and distributed systems. Concurnas is a statically typed language with object oriented, functional, and reactive programming constructs.

With a concise syntax that hides multithreaded complexity, and native support for GPU computing, vectorization, and data structures like matrices, Concurnas allows for building machine learning applications and high performance parallel applications. In addition, Concurnas provides interoperability with other JVM languages like Java and Scala. Concurnas supports Oracle JDK and OpenJDK versions 1.8 through to the latest GA release 14.

InfoQ spoke to Jason Tatton, creator of Concurnas and the founder of Concurnas Ltd., about the language, some of its design decisions, and features.

#machine learning #jvm languages #development #architecture & design

Agnes  Sauer

Agnes Sauer

1596955500

Staying home? Why not build a GPU Box!

After working with Machine Learning applications for a while, I was excited to try some Deep Leaning models. Probably the biggest obstable that I faced in getting started was the workflow, specifically, finding an accessible, fluid GPU-powered environment to work in. My Mac Pro (affectionately called the trash can model) was not going to help. I began to use cloud services, but missed having my own place to store data and run code without having the meter running. So I decided to build my own GPU box. At first it was pretty Intimidating (capital I), but things went fairly smoothly, and the end result was extremely gratifying. In case my adventure is interesting or helpful to you, here it is!

Image for post

Motherboard

ASUS ROG Strix B360-I

I’ll start with the mobo. Let me say upfront that this build won’t apply without modification to your situation if you want to use multiple GPUs. I decided to build a mini-ITX form factor (i.e. small) machine with a single GPU because this was my first build and I thought it would be challenging enough. Also, I like my house to have a decorated and put-together feel, including my office. I wasn’t trying to create a man cave gaming paradise with a large neon-glowing tower. But can I say, I love this board! For the gamers, this was the only RGB item I bought for the system, and it’s perfect. But in practical building terms, the real bonus is the built-in I/O shield. As one of the helpful youtube gaming computer assembly gentlemen pointed out, installing the shield can be the most challenging part of the build. You’re already a hero for building your own rig; you can catch a break without feeling guilty.

Case

Thermaltake Core V1 Snow Edition

I decided on the case early in the process, because it determined the sizes that would be possible for some of the other components. It’s also pretty fun, because there are a lot of options. I chose the Core V1 because, while it’s certainly a mini, it isn’t super small, so there’s still plenty of room for cables, etc. Some things to watch out for are: 1) the CPU cooler height, 2) the length of the GPU, and 3) the length of the power supply (PSU). This case is incredibly well designed. I like the fact that the board sits upright instead of sideways, so your components aren’t ‘hanging on for dear life’ as one reviewer pointed out.

CPU

Intel Core i7–8700K

I think I may have over-bought on the processor. I had to have generation 8 to fit the motherboard, but the ‘K’ in the model number means that the processor is unlocked and so can be ‘overclocked.’ This is gamer language for pushing the processor to go faster than normal; however, my board doesn’t support this functionality anyhow (it’s a B instead of a Z series).

CPU cooler / heatsink

Noctua NH-U9S with NF-A9 92mm Fan

The cooler might be one place on a small machine where you pay a bit more, but this is the most highly rated item (reviewer stars) on my list. It’s still less expensive than many liquid coolers (although not as lovely). Plus, who wants to burn things up ’cause they were too cheap to spend an extra $30. You will learn to love this baby.

#computers #custom-built-computers #deep-learning #gpu-computing #gpu #deep learning

Ananya Gupta

Ananya Gupta

1594464365

Advantage of C Language Certification Online Training in 2020

C language is a procedural programming language. C language is the general purpose and object oriented programming language. C language is mainly used for developing different types of operating systems and other programming languages. C language is basically run in hardware and operating systems. C language is used many software applications such as internet browser, MYSQL and Microsoft Office.
**
Advantage of doing C Language Training in 2020 are:**

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  6. Rich Function Library: The another main Advantage of doing C language training in 2020 is rich function library. C language has rich function of libraries as compared to other programming languages. The libraries help to build the analytical skills.

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    The demand of C language is high in IT sector and increasing rapidly.

C Language Online Training is for individuals and professionals.
C Language Online Training helps to develop an application, build operating systems, games and applications, work on the accessibility of files and memory and many more.

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Is C Language Training Worth Learning for You! and is providing the basic understanding of create C applications, apply the real time programming, write high quality code, computer programming, C functions, variables, datatypes, operators, loops, statements, groups, arrays, strings, etc.

The companies which are using C language are Amazon, Martin, Apple, Samsung, Google, Oracle, Nokia, IBM, Intel, Novell, Microsoft, Facebook, Bloomberg, VM Ware, etc.
C language is used in different domains like banking, IT, Insurance, Education, Gaming, Networking, Firmware, Telecommunication, Graphics, Management, Embedded, Application Development, Driver level Development, Banking, etc.

The job opportunities after completing the C Language Online certificationAre Data Scientists, Back End Developer, Embedded Developer, C Analyst, Software Developer, Junior Programmer, Database Developer, Embedded Engineer, Programming Architect, Game Programmer, Quality Analyst, Senior Programmer, Full Stack Developer, DevOps Specialist, Front End Web Developer, App Developer, Java Software Engineer, Software Developer and many more.

#c language online training #c language online course #c language certification online #c language certification #c language certification course #c language certification training

Ananya Gupta

Ananya Gupta

1599550659

Benefits Of C Language Over Other Programming Languages

C may be a middle-level programing language developed by Dennis Ritchie during the first 1970s while performing at AT&T Bell Labs within the USA. the target of its development was within the context of the re-design of the UNIX OS to enable it to be used on multiple computers.

Earlier the language B was now used for improving the UNIX. Being an application-oriented language, B allowed a much faster production of code than in programming language. Still, B suffered from drawbacks because it didn’t understand data-types and didn’t provide the utilization of “structures”.

These drawbacks became the drive for Ritchie for the development of a replacement programing language called C. He kept most of the language B’s syntax and added data-types and lots of other required changes. Eventually, C was developed during 1971-73, containing both high-level functionality and therefore the detailed features required to program an OS. Hence, many of the UNIX components including the UNIX kernel itself were eventually rewritten in C.

Benefits of C language

As a middle-level language, C combines the features of both high-level and low-level languages. It is often used for low-level programmings, like scripting for it also supports functions of high-level C programming languages, like scripting for software applications, etc.
C may be a structured programing language that allows a posh program to be broken into simpler programs called functions. It also allows free movement of knowledge across these functions.

Various features of C including direct access to machine level hardware APIs, the presence of C compilers, deterministic resource use, and dynamic memory allocation make C language an optimum choice for scripting applications and drivers of embedded systems.

C language is case-sensitive which suggests lowercase and uppercase letters are treated differently.
C is very portable and is employed for scripting system applications which form a serious a part of Windows, UNIX, and Linux OS.

C may be a general-purpose programing language and may efficiently work on enterprise applications, games, graphics, and applications requiring calculations, etc.
C language features a rich library that provides a variety of built-in functions. It also offers dynamic memory allocation.

C implements algorithms and data structures swiftly, facilitating faster computations in programs. This has enabled the utilization of C in applications requiring higher degrees of calculations like MATLAB and Mathematica.

Riding on these advantages, C became dominant and spread quickly beyond Bell Labs replacing many well-known languages of that point, like ALGOL, B, PL/I, FORTRAN, etc. C language has become available on a really wide selection of platforms, from embedded microcontrollers to supercomputers.

#c language online training #c language training #c language course #c language online course #c language certification course

How to Predict Housing Prices with Linear Regression?

How-to-Predict-Housing-Prices-with-Linear-Regression

The final objective is to estimate the cost of a certain house in a Boston suburb. In 1970, the Boston Standard Metropolitan Statistical Area provided the information. To examine and modify the data, we will use several techniques such as data pre-processing and feature engineering. After that, we'll apply a statistical model like regression model to anticipate and monitor the real estate market.

Project Outline:

  • EDA
  • Feature Engineering
  • Pick and Train a Model
  • Interpret
  • Conclusion

EDA

Before using a statistical model, the EDA is a good step to go through in order to:

  • Recognize the data set
  • Check to see if any information is missing.
  • Find some outliers.
  • To get more out of the data, add, alter, or eliminate some features.

Importing the Libraries

  • Recognize the data set
  • Check to see if any information is missing.
  • Find some outliers.
  • To get more out of the data, add, alter, or eliminate some features.

# Import the libraries #Dataframe/Numerical libraries import pandas as pd import numpy as np #Data visualization import plotly.express as px import matplotlib import matplotlib.pyplot as plt import seaborn as sns #Machine learning model from sklearn.linear_model import LinearRegression

Reading the Dataset with Pandas

#Reading the data path='./housing.csv' housing_df=pd.read_csv(path,header=None,delim_whitespace=True)

 CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
00.0063218.02.3100.5386.57565.24.09001296.015.3396.904.9824.0
10.027310.07.0700.4696.42178.94.96712242.017.8396.909.1421.6
20.027290.07.0700.4697.18561.14.96712242.017.8392.834.0334.7
30.032370.02.1800.4586.99845.86.06223222.018.7394.632.9433.4
40.069050.02.1800.4587.14754.26.06223222.018.7396.905.3336.2
.............................................
5010.062630.011.9300.5736.59369.12.47861273.021.0391.999.6722.4
5020.045270.011.9300.5736.12076.72.28751273.021.0396.909.0820.6
5030.060760.011.9300.5736.97691.02.16751273.021.0396.905.6423.9
5040.109590.011.9300.5736.79489.32.38891273.021.0393.456.4822.0
5050.047410.011.9300.5736.03080.82.50501273.021.0396.907.8811.9

Have a Look at the Columns

Crime: It refers to a town's per capita crime rate.

ZN: It is the percentage of residential land allocated for 25,000 square feet.

Indus: The amount of non-retail business lands per town is referred to as the indus.

CHAS: CHAS denotes whether or not the land is surrounded by a river.

NOX: The NOX stands for nitric oxide content (part per 10m)

RM: The average number of rooms per home is referred to as RM.

AGE: The percentage of owner-occupied housing built before 1940 is referred to as AGE.

DIS: Weighted distance to five Boston employment centers are referred to as dis.

RAD: Accessibility to radial highways index

TAX: The TAX columns denote the rate of full-value property taxes per $10,000 dollars.

B: B=1000(Bk — 0.63)2 is the outcome of the equation, where Bk is the proportion of blacks in each town.

PTRATIO: It refers to the student-to-teacher ratio in each community.

LSTAT: It refers to the population's lower socioeconomic status.

MEDV: It refers to the 1000-dollar median value of owner-occupied residences.

Data Preprocessing

# Check if there is any missing values. housing_df.isna().sum() CRIM       0 ZN         0 INDUS      0 CHAS       0 NOX        0 RM         0 AGE        0 DIS        0 RAD        0 TAX        0 PTRATIO    0 B          0 LSTAT      0 MEDV       0 dtype: int64

No missing values are found

We examine our data's mean, standard deviation, and percentiles.

housing_df.describe()

Graph Data

 CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
count506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000
mean3.61352411.36363611.1367790.0691700.5546956.28463468.5749013.7950439.549407408.23715418.455534356.67403212.65306322.532806
std8.60154523.3224536.8603530.2539940.1158780.70261728.1488612.1057108.707259168.5371162.16494691.2948647.1410629.197104
min0.0063200.0000000.4600000.0000000.3850003.5610002.9000001.1296001.000000187.00000012.6000000.3200001.7300005.000000
25%0.0820450.0000005.1900000.0000000.4490005.88550045.0250002.1001754.000000279.00000017.400000375.3775006.95000017.025000
50%0.2565100.0000009.6900000.0000000.5380006.20850077.5000003.2074505.000000330.00000019.050000391.44000011.36000021.200000
75%3.67708312.50000018.1000000.0000000.6240006.62350094.0750005.18842524.000000666.00000020.200000396.22500016.95500025.000000
max88.976200100.00000027.7400001.0000000.8710008.780000100.00000012.12650024.000000711.00000022.000000396.90000037.97000050.000000

The crime, area, sector, nitric oxides, 'B' appear to have multiple outliers at first look because the minimum and maximum values are so far apart. In the Age columns, the mean and the Q2(50 percentile) do not match.

We might double-check it by examining the distribution of each column.

Inferences

  1. The rate of crime is rather low. The majority of values are in the range of 0 to 25. With a huge value and a value of zero.
  2. The majority of residential land is zoned for less than 25,000 square feet. Land zones larger than 25,000 square feet represent a small portion of the dataset.
  3. The percentage of non-retial commercial acres is mostly split between two ranges: 0-13 and 13-23.
  4. The majority of the properties are bordered by the river, although a tiny portion of the data is not.
  5. The content of nitrite dioxide has been trending lower from.3 to.7, with a little bump towards.8. It is permissible to leave a value in the range of 0.1–1.
  6. The number of rooms tends to cluster around the average.
  7. With time, the proportion of owner-occupied units rises.
  8. As the number of weights grows, the weight distance between 5 employment centers reduces. It could indicate that individuals choose to live in new high-employment areas.
  9. People choose to live in places with limited access to roadways (0-10). We have a 30th percentile outlier.
  10. The majority of dwelling taxes are in the range of $200-450, with large outliers around $700,000.
  11. The percentage of people with lower status tends to cluster around the median. The majority of persons are of lower social standing.

Because the model is overly generic, removing all outliers will underfit it. Keeping all outliers causes the model to overfit and become excessively accurate. The data's noise will be learned.

The approach is to establish a happy medium that prevents the model from becoming overly precise. When faced with a new set of data, however, they generalise well.

We'll keep numbers below 600 because there's a huge anomaly in the TAX column around 600.

new_df=housing_df[housing_df['TAX']<600]

Looking at the Distribution

Looking-at-the-Distribution

The overall distribution, particularly the TAX, PTRATIO, and RAD, has improved slightly.

Correlation

Correlation

Perfect correlation is denoted by the clear values. The medium correlation between the columns is represented by the reds, while the negative correlation is represented by the black.

With a value of 0.89, we can see that 'MEDV', which is the medium price we wish to anticipate, is substantially connected with the number of rooms 'RM'. The proportion of black people in area 'B' with a value of 0.19 is followed by the residential land 'ZN' with a value of 0.32 and the percentage of black people in area 'ZN' with a value of 0.32.

The metrics that are most connected with price will be plotted.

The-metrics-that-are-most-connected

Feature Engineering

Feature Scaling

Gradient descent is aided by feature scaling, which ensures that all features are on the same scale. It makes locating the local optimum much easier.

Mean standardization is one strategy to employ. It substitutes (target-mean) for the target to ensure that the feature has a mean of nearly zero.

def standard(X):    '''Standard makes the feature 'X' have a zero mean'''    mu=np.mean(X) #mean    std=np.std(X) #standard deviation    sta=(X-mu)/std # mean normalization    return mu,std,sta     mu,std,sta=standard(X) X=sta X

 CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
0-0.6091290.092792-1.019125-0.2809760.2586700.2791350.162095-0.167660-2.105767-0.235130-1.1368630.401318-0.933659
1-0.575698-0.598153-0.225291-0.280976-0.4237950.0492520.6482660.250975-1.496334-1.032339-0.0041750.401318-0.219350
2-0.575730-0.598153-0.225291-0.280976-0.4237951.1897080.0165990.250975-1.496334-1.032339-0.0041750.298315-1.096782
3-0.567639-0.598153-1.040806-0.280976-0.5325940.910565-0.5263500.773661-0.886900-1.3276010.4035930.343869-1.283945
4-0.509220-0.598153-1.040806-0.280976-0.5325941.132984-0.2282610.773661-0.886900-1.3276010.4035930.401318-0.873561
..........................................
501-0.519445-0.5981530.585220-0.2809760.6048480.3060040.300494-0.936773-2.105767-0.5746821.4456660.277056-0.128344
502-0.547094-0.5981530.585220-0.2809760.604848-0.4000630.570195-1.027984-2.105767-0.5746821.4456660.401318-0.229652
503-0.522423-0.5981530.585220-0.2809760.6048480.8777251.077657-1.085260-2.105767-0.5746821.4456660.401318-0.820331
504-0.444652-0.5981530.585220-0.2809760.6048480.6060461.017329-0.979587-2.105767-0.5746821.4456660.314006-0.676095
505-0.543685-0.5981530.585220-0.2809760.604848-0.5344100.715691-0.924173-2.105767-0.5746821.4456660.401318-0.435703

Choose and Train the Model

For the sake of the project, we'll apply linear regression.

Typically, we run numerous models and select the best one based on a particular criterion.

Linear regression is a sort of supervised learning model in which the response is continuous, as it relates to machine learning.

Form of Linear Regression

y= θX+θ1 or y= θ1+X1θ2 +X2θ3 + X3θ4

y is the target you will be predicting

0 is the coefficient

x is the input

We will Sklearn to develop and train the model

#Import the libraries to train the model from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression

Allow us to utilise the train/test method to learn a part of the data on one set and predict using another set using the train/test approach.

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) [7.22218258] 24.66379606613584

In this example, you will learn the model using below hypothesis:

Price= 24.85 + 7.18* Room

It is interpreted as:

For a decided price of a house:

A 7.18-unit increase in the price is connected with a growth in the number of rooms.

As a side note, this is an association, not a cause!

Interpretation

You will need a metric to determine whether our hypothesis was right. The RMSE approach will be used.

Root Means Square Error (RMSE) is defined as the square root of the mean of square error. The difference between the true and anticipated numbers called the error. It's popular because it can be expressed in y-units, which is the median price of a home in our scenario.

def rmse(predict,actual):    return np.sqrt(np.mean(np.square(predict - actual))) # Split the Data into train and test set X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) loss=rmse(predictions_test,y_test) print('loss: ',loss) print(model.score(X_test,y_test)) #accuracy [7.43327725] 24.912055881970886 loss: 3.9673165450580714 0.7552661033654667 Loss will be 3.96

This means that y-units refer to the median value of occupied homes with 1000 dollars.

This will be less by 3960 dollars.

While learning the model you will have a high variance when you divide the data. Coefficient and intercept will vary. It's because when we utilized the train/test approach, we choose a set of data at random to place in either the train or test set. As a result, our theory will change each time the dataset is divided.

This problem can be solved using a technique called cross-validation.

Improvisation in the Model

With 'Forward Selection,' we'll iterate through each parameter to assist us choose the numbers characteristics to include in our model.

Forward Selection

  1. Choose the most appropriate variable (in our case based on high correlation)
  2. Add the next best variable to the model
  3. Some predetermined conditions must meet.

We'll use a random state of 1 so that each iteration yields the same outcome.

cols=[] los=[] los_train=[] scor=[] i=0 while i < len(high_corr_var):    cols.append(high_corr_var[i])        # Select inputs variables    X=new_df[cols]        #mean normalization    mu,std,sta=standard(X)    X=sta        # Split the data into training and testing    X_train,X_test,y_train,y_test= train_test_split(X,y,random_state=1)        #fit the model to the training    lnreg=LinearRegression().fit(X_train,y_train)        #make prediction on the training test    prediction_train=lnreg.predict(X_train)        #make prediction on the testing test    prediction=lnreg.predict(X_test)        #compute the loss on train test    loss=rmse(prediction,y_test)    loss_train=rmse(prediction_train,y_train)    los_train.append(loss_train)    los.append(loss)        #compute the score    score=lnreg.score(X_test,y_test)    scor.append(score)        i+=1

We have a big 'loss' with a smaller collection of variables, yet our system will overgeneralize in this scenario. Although we have a reduced 'loss,' we have a large number of variables. However, if the model grows too precise, it may not generalize well to new data.

In order for our model to generalize well with another set of data, we might use 6 or 7 features. The characteristic chosen is descending based on how strong the price correlation is.

high_corr_var ['RM', 'ZN', 'B', 'CHAS', 'RAD', 'DIS', 'CRIM', 'NOX', 'AGE', 'TAX', 'INDUS', 'PTRATIO', 'LSTAT']

With 'RM' having a high price correlation and LSTAT having a negative price correlation.

# Create a list of features names feature_cols=['RM','ZN','B','CHAS','RAD','CRIM','DIS','NOX'] #Select inputs variables X=new_df[feature_cols] # Split the data into training and testing sets X_train,X_test,y_train,y_test= train_test_split(X,y, random_state=1) # feature engineering mu,std,sta=standard(X) X=sta # fit the model to the trainning data lnreg=LinearRegression().fit(X_train,y_train) # make prediction on the testing test prediction=lnreg.predict(X_test) # compute the loss loss=rmse(prediction,y_test) print('loss: ',loss) lnreg.score(X_test,y_test) loss: 3.212659865936143 0.8582338376696363

The test set yielded a loss of 3.21 and an accuracy of 85%.

Other factors, such as alpha, the learning rate at which our model learns, could still be tweaked to improve our model. Alternatively, return to the preprocessing section and working to increase the parameter distribution.

For more details regarding scraping real estate data you can contact Scraping Intelligence today

https://www.websitescraper.com/how-to-predict-housing-prices-with-linear-regression.php