Developer Insights

When it comes to understanding computing processes, especially in today's front end and backend development world, most of the times everything revolves heavily around analyzing the algorithmic architecture in tools, applications, or more complex pieces of software.

In fact, a thorough analysis of what concerns the algorithmic side of things within the computing processing industry has led to a common conclusion— algorithmic functions are moving with architectural rendering languages to build much more complex tools.

Let's analyze some of these.

Algorithmic Retargeting in R and Python

The biggest Python application currently available for the mass market is the one related to front-end tools installed on enterprise sites.

This includes tools related to the web personalization industry, retargeting, remarketing, and Big Data manipulation, which are, in fact, a massive part of this statement.

The way these tools work is by restructuring a catalog onto specific user preferences.

This is done with the combination of Python features and R-rendering algorithms.

Python scripts are gathering big data from specific landing pages, which are then stored into a Javascript (generally) container.

After this is done, R algorithms are set up to render automatically the data, via (generally) AngularJS-coded scripts.

In this particular case, R functions are simply acting as a processing functionality.

Which Rendering Languages are Used

The above-mentioned process (gathering via Python, processed in R, and then exported in JS) is pretty common in a variety of architecture and, depending on the usage, the only variable for what concerns which programming languages are used is related to the "export" side of the matter.

To better explain this, let's analyze the most common programming languages— JavaScript and C#.

JavaScript exports are common within CMS-based tools (the ones, to reference, installed on architecture like WordPress, Magento, Shopify, etc) given the easiness of its application to these very portals.

C#, on the other hand, is used when the tool (or software) is native and, therefore, the rendering langue used to print the pieces of information must be tailored onto the building architecture.

Why is this Considered AI?

Although for many, the matter could sound a bit dark and complicated, the combination of R algorithms to rendering languages (and computing power in general) could be aggregated within the AI sphere.

This is possible because, technically, those features (data gathering, processing, and printing) are related to AI as a whole.

Artificial pieces of intelligence in 2019 have moved, in fact, to this very matter: fast processing, personalization, and projections tailored onto Big Data, automatically gathered without any human input.

Futuristic projections of AI controlling our lives still live in science fiction and sometimes, given how they're covered in many technology blogs/newspapers, these statements are extremely downgrading for an industry that is moving massively for what concerns both development and business awareness.

The Market Value

Pieces of software that are combining R algorithms and rendering languages as well as data automation have been covered by a variety of industry analysts.

These industry analysts have pointed out how they are building a futuristic architecture that is very likely to dominate the way we perceive data processing.

On top of everything that was said above, there is a significant part of the mobile market which is approaching the matter.

As we know, mobile has definitely become quite important, both from a development point of view (with new applications) and a purely business-related one (with many investors and new startups becoming enterprises).

Any app developers who have pointed out how algorithmic features within complex builds (especially on iOS) are now being embraced in the UK, which was recently selected as the European technological powerhouse.

We can safely say that this will become the industry standard in the near future. Take a free tutorial to Python & Machine learning programming for better understanding.

Digital technologies development has been on a constant rise in the past several years. Technologies such as AR and VR were mere whispers at the beginning of the 21^st century. Today, we see these and other technologies all around us thanks to increased computing power and visionary inventors.These bots are smart and are getting smarter by the day. Their data storage expands with each query they process which in time ensures more logical and useful responses. Implementing machine-learning algorithms into your business and website can elevate the way you communicate with the public.

Blockchain technology

Blockchain started as a way to track cryptocurrency trade on the deep web. The currency (namely Bitcoin) became so destabilized that it required a centralized ledger of information and trade to work effectively.

Since then, blockchain has been repurposed into a business-centric technology. It allows companies to easily manage data, books, transactions and any information too valuable to lose.

Recent data shows that 90% of banks across North America and Europe continue to experiment with blockchain implementation. The same logic can be applied to individual business’ finances and records, especially with the estimated 33% drop in operational costs due to blockchain.

Marie Fincher, head of Trust My Paper content department had this to say about blockchain: “I fully support and advocate for blockchain implementation in online businesses. The sheer volume of potential implementations blockchain allows is astonishing to me, even after so many years in the online writing industry.”

Companies that implement blockchain will have an easier time when it comes to the management of their precious data, not to mention the client and project information involved in it.

Cloud-based outsourcing

Local servers, personal computer software and other forms of non-networked development are a thing of the past. Very few growth-focused businesses operate without relying on cloud-based computing and outsourcing services.It’s very easy and affordable to get storage space, computing power and even third-party support through cloud-based platforms. One notable example is the process of content creation for digital marketing. Data shows that 78% of global companies are happy with their outsourcing agencies, opting for third-party marketing instead of in-house expenditure.As with any other technology, marketing is a “make or break” feature which informs potential clients of your products and/or services. It can also be beneficial for businesses to invest into translation and localization of their digital content. With the recent shifts in the corporate marketplace, specialized outsourcing firms are a much better solution as opposed to internal teams which bleed revenue.

Recurring customer’s journey

The customer’s journey from introduction to brand advocacy is an important part of digital marketing. Today’s technology allows brands and companies to advertise their products and services in as many ways and platforms as they can muster.However, the customer’s journey is just as relevant as it ever was – if not more in 2018. The reason for this is the appearance of the recurring customer’s journey in which a customer is encouraged to reintroduce themselves to your brand. Only 37% of buyers have expressed that their brands know what they want as consumers. This means that one-sided marketing campaigns with little-to-no customer experience design will soon go extinct.You can achieve this by sending personalized email, small loyalty gifts and other purchase incentives. In doing so, you will effectively reignite the interest of old customers into your brand’s lineup.Studies have shown that 55% of customers are willing and ready to pay more for guaranteed quality of service. This number is only poised to grow with the ever-growing selection of brands and services as opposed to the customers’ limited budgets.They will effectively start the journey again and make a purchase, ending it with brand advocacy and word of mouth. Customer retention is a serious issue in the corporate sphere, so much so that innovations such as these are mandatory for the survival of a brand as a whole.

Marketing personalization

With so many link building agencies, SEO agencies, and digital marketing providers out there, people have grown tired of being treated as a “crowd”. Individualized messages and email marketing have become relevant again thanks to the surge in popularity of smartphones.

Short, direct messages aimed at specific customers can make a greater impact than a generalized banner ad placed somewhere on a website. Marketing personalization has veered its head in social media platforms and sites such as Google and YouTube as well.

Recent surveys have shown that 58% of marketing experts agree about the importance of original, personalized content. This means that you should pay close attention to the copywriting aspect of your marketing campaigns before you delegate that content.

Automated personalized marketing using email marketing software is more relevant than ever thanks to meticulously gathered data from web users around the globe. In 2018, this trend is poised to transform the way we perceive marketing and customer targeting – which is what makes this transformation even more relevant.

The future is bright (conclusion)

The above-mentioned points about digital transformation are but a taste of what the future has to offer beyond 2018. Estimates show that more than 50% of US ad spending will be directed at digital content by 2021. Companies and customers alike have a lot to look forward to when it comes to quality of life improvements of their digital content.

Pay attention to the trends and changes in your own industry to stay ahead of the curve. Don’t be afraid to implement new ideas and innovations into your workflow just because someone says you shouldn’t.

Companies which pioneer certain digital technologies are bound to get an increase in popularity and revenue as a result – don’t pass on the opportunity if it comes your way.

Businesses that use Artificial Intelligence (AI) and related technology to reveal new insights “will steal $1.2 trillion per annum from their less informed peers by 2020.” predicts Forrester Research. Although AI has been around since the 1950s, it is only recently that the technology has begun to find real-world applications (such as Apple’s Siri). The investment in AI by both tech giants as well as start-ups has increased 3 folds to $40 Billion as of 2017. Recent advances in AI have been helped by three factors:

1. Access to big data generated from e-commerce, businesses, governments, science, wearables, and social media
2. Improvement in machine learning (ML) algorithms—due to the availability of large amounts of data
3. Greater computing power and the rise of cloud-based services—which helps run sophisticated machine learning algorithms.

Applications of AI

AI is important because it can help solve immensely difficult issues in various industries, such as entertainment, education, health, commerce, transport, and utilities. In fields like software testing, AI enables more efficient and accurate identification of bugs, enhances test automation processes, and contributes to the development of innovative solution, such as an AI companion app. AI applications can be grouped into five categories:

• Reasoning: The ability to solve problems through logical deduction. e.g. financial asset management, legal assessment, financial application processing, autonomous weapons systems, games
• Knowledge: The ability to present knowledge about the world. e.g. financial market trading, purchase prediction, fraud prevention, drug creation, medical diagnosis, media recommendation
• Planning: The ability to set and achieve goals. e.g. inventory management, demand forecasting, predictive maintenance, physical and digital network optimization, navigation, scheduling, logistics
• Communication: The ability to understand spoken and written language. e.g. real-time translation of spoken and written languages, Ai voice generation, real-time transcription, intelligent assistants, voice control
• Perception: The ability to infer things about the world via sounds, images, and other sensory inputs. e.g. medical diagnosis, autonomous vehicles, surveillance

Here is an infographic by Mckinsey that shows the extent to which AI can be used end-to-end in the retail industry from identifying customers to personalizing promotion to inventory management.

Source: McKinsey

AI trends in various sectors

1. Healthcare

AI and ML technology has been particularly useful in the healthcare industry because it generates massive amounts of data to train with and enables algorithms to spot patterns faster than human analysts.

• Medecision developed an algorithm that detects 8 variables in diabetes patients to determine if hospitalization is required.
• An app called BiliScreen utilizes a smartphone camera, ML tools, and computer vision algorithms to detect increased levels of bilirubin in the sclera (white portion) of a person’s eye, which is used to screen people for pancreatic cancer. This cancer has no telltale symptoms, hence it has one of the worst prognoses of all cancers.
• NuMedii, a biopharma company, has developed a platform called Artificial Intelligence for Drug Discovery (AIDD), which uses big data and AI to detect the link between diseases and drugs at the systems level.
• GNS Healthcare uses ML algorithms to match patients with the most effective treatments for them.

2. Entertainment

A familiar application of AI in everyday life is seen with services like Netflix or Amazon, wherein ML algorithms analyze the user’s activity and compare it with that of other users to determine which shows or products to recommend. The algorithms are becoming intelligent with time—to the extent of understanding that a user may want to buy a product as a gift and not for himself/herself, or that different family members have different watching preferences.

3. Finance

• Investment companies like Aidya and Nomura Securities use AI algorithms to conduct trading autonomously and robo-traders to conduct high-frequency trading for greater profits, respectively.
• Fintech firms like Kensho and ForwardLane use AI-powered B2C robo-advisors to augment rebalancing decisions and portfolio management performed by human analysts. Wealthfront uses AI algorithms to track account activity and help financial advisors customize their advice.
• Chatbots, powered by natural language processing, can serve banking customers quickly and efficiently by answering common queries and providing information promptly.
• Fraud detection is an important application of AI in financial services. For example, Mastercard uses Decision Intelligence technology to analyze various data points to detect fraudulent transactions, improve real-time approval accuracy, and reduce false declines.

4. Data security

Cyber attacks are becoming a growing reality with the move to a digital world. There are also concerns about AI programs themselves turning against systems.

• Automatic exploit generation (AEG) is a bot that can determine whether a software bug, which may cause security issues, is exploitable. If a vulnerability is found, the bot automatically secures it. AEG systems help develop automated signature generation algorithms that can predict the likelihood of cyberattacks.
• PatternEx and MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed an AI platform called AI2 which claims to predict cyber attacks better than existing systems. The platform uses Active Contextual Modeling, a continuous feedback loop between a human analyst and the AI system, to provide an attack detection rate that is better than ML-only solutions by a factor of 10.
• Deep Instinct, an institutional intelligence company, says that malware code varies between 2%-10% in every iteration and that its AI model is able to handle the variations and accurately predict which files are malware.

5. Manufacturing

• Landing.ai claims to have created machine-vision tools to find microscopic defects in objects like circuit boards using an ML algorithm trained using tiny volumes of sample images. In the future, self-driving robots may be created which can move finished goods around without endangering anyone or anything around.
• Robots in factories are often stationary but are still in danger of crashing into objects around it. A new concept called collaborative robots or “cobots, enabled by AI, can take instructions from humans, including instructions that the robot has not been previously exposed to, and work productively with them.
• AI algorithms can influence the manufacturing supply chain by detecting the patterns of demand for products across geographies, socioeconomic segments, and time, and predicting market demand. This, in turn, will affect inventory, raw material sourcing, financing decisions, human staffing, energy consumption, and maintenance of equipment.
• AI tools help in predicting malfunctions and breakdown of equipment and taking or recommending preemptive actions as well as tracking operating conditions and performance of factory tooling.

6. Automotive industry

• Tesla introduced TeslaBot, an intelligent virtual assistant integrated with Tesla models S and X, allows users to interact with their car from their phone or desktop.
• Uber AI Labs is working on developing self-driven cars with the help of the best engineers and scientists. Uber has already tested a batch of self-driving cars in 2016.
• Nvidia has partnered with Volkswagen to develop “intelligent co-pilot systems” in cars that will enable safety warnings, gesture control, and voice and facial recognition.
• Ericsson predicts that 5G technology will improve vehicle-to-vehicle communication wherein sensors will be implanted in airport runways, railways, and roads.

Conclusion

Jack Ma, the founder of Alibaba, warned the audience at the World Economic Forum 2018 at Davos that AI and big data were a threat to humans and would disable people instead of empowering them. However, given the sweeping real-world applications of AI and ML and the constant advancements in the field, it is more likely that the technology will transform the way we work—enabling faster, more informed decisions, increasing operational efficiency, and innovating new products and services.

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<!—– Copy and Paste This Code Into Your Post —-></p><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /> <h3>Applications of Artificial Intelligence (AI) in business </h3><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /> <p><a href=”https://www.hackerearth.com/blog/innovation-management/applications-of-artificial-intelligence/”><img src=”data:image/gif;base64,R0lGODdhAQABAPAAAP///wAAACwAAAAAAQABAEACAkQBADs=” data-lazy-src=”https://media.hackerearth.com/blog/wp-content/uploads/2018/07/applications-of-artificial-intelligence-in-business.jpg” alt=”Applications of Artificial Intelligence (AI) in business ” width=”700px” /><noscript><img src=”https://media.hackerearth.com/blog/wp-content/uploads/2018/07/applications-of-artificial-intelligence-in-business.jpg” alt=”Applications of Artificial Intelligence (AI) in business ” width=”700px” /></noscript></a></p><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /> <p><a href=”https://www.hackerearth.com”>Infographic by hackerearth.com</a></p><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /> <p>

Bonjour! Welcome to another part of the series on data visualization techniques. In the previous two articles, we discussed different data visualization techniques that can be applied to visualize and gather insights from categorical and continuous variables. You can check out the first two articles here:

• Data visualization for beginners – Part 1
• Data visualization for beginners – Part 2

In this article, we’ll go through the implementation and use of a bunch of data visualization techniques such as heat maps, surface plots, correlation plots, etc. We will also look at different techniques that can be used to visualize unstructured data such as images, text, etc.

 ### Importing the required libraries   
 import pandas as pd   
 import numpy as np  
 import seaborn as sns   
 import matplotlib.pyplot as plt   
 import plotly.plotly as py  
 import plotly.graph_objs as go  
 %matplotlib inline  

Heatmaps

A heat map(or heatmap) is a two-dimensional graphical representation of the data which uses colour to represent data points on the graph. It is useful in understanding underlying relationships between data values that would be much harder to understand if presented numerically in a table/ matrix.

### We can create a heatmap by simply using the seaborn library.   
 sample_data = np.random.rand(8, 12)  
 ax = sns.heatmap(sample_data)  

Let’s understand this using an example. We’ll be using the metadata from Deep Learning 3 challenge. Link to the dataset. Deep Learning 3 challenged the participants to predict the attributes of animals by looking at their images.

 ### Training metadata contains the name of the image and the corresponding attributes associated with the animal in the image.  
 train = pd.read_csv('meta-data/train.csv')  
 train.head()  

We will be analyzing how often an attribute occurs in relationship with the other attributes. To analyze this relationship, we will compute the co-occurrence matrix.

 ### Extracting the attributes  
 cols = list(train.columns)  
 cols.remove('Image_name')  
 attributes = np.array(train[cols])  
 print('There are {} attributes associated with {} images.'.format(attributes.shape[1],attributes.shape[0]))  

 Out: There are 85 attributes associated with 12,600 images.  

 # Compute the co-occurrence matrix  
 cooccurrence_matrix = np.dot(attributes.transpose(), attributes)  
 print('\n Co-occurrence matrix: \n', cooccurrence_matrix)  
 Out: Co-occurrence matrix:   
  [[5091 728 797 ... 3797 728 2024]  
  [ 728 1614  0 ... 669 1614 1003]  
  [ 797  0 1188 ... 1188  0 359]  
  ...  
  [3797 669 1188 ... 8305 743 3629]  
  [ 728 1614  0 ... 743 1933 1322]  
  [2024 1003 359 ... 3629 1322 6227]]  

 # Normalizing the co-occurrence matrix, by converting the values into a matrix  
 # Compute the co-occurrence matrix in percentage  
 #Reference:https://stackoverflow.com/questions/20574257/constructing-a-co-occurrence-matrix-in-python-pandas/20574460  
 cooccurrence_matrix_diagonal = np.diagonal(cooccurrence_matrix)  
 with np.errstate(divide = 'ignore', invalid='ignore'):  
   cooccurrence_matrix_percentage = np.nan_to_num(np.true_divide(cooccurrence_matrix, cooccurrence_matrix_diagonal))  
 print('\n Co-occurrence matrix percentage: \n', cooccurrence_matrix_percentage)  

We can see that the values in the co-occurrence matrix represent the occurrence of each attribute with the other attributes. Although the matrix contains all the information, it is visually hard to interpret and infer from the matrix. To counter this problem, we will use heat maps, which can help relate the co-occurrences graphically.

 fig = plt.figure(figsize=(10, 10))  
 sns.set(style='white')  
 # Draw the heatmap with the mask and correct aspect ratio   
 ax = sns.heatmap(cooccurrence_matrix_percentage, cmap='viridis', center=0, square=True, linewidths=0.15, cbar_kws={"shrink": 0.5, "label": "Co-occurrence frequency"}, )  
 ax.set_title('Heatmap of the attributes')  
 ax.set_xlabel('Attributes')  
 ax.set_ylabel('Attributes')  
 plt.show()  

Since the frequency of the co-occurrence is represented by a colour pallet, we can now easily interpret which attributes appear together the most. Thus, we can infer that these attributes are common to most of the animals.

Choropleth

Choropleths are a type of map that provides an easy way to show how some quantity varies across a geographical area or show the level of variability within a region. A heat map is similar but doesn’t include geographical boundaries. Choropleth maps are also appropriate for indicating differences in the distribution of the data over an area, like ownership or use of land or type of forest cover, density information, etc. We will be using the geopandas library to implement the choropleth graph.

We will be using choropleth graph to visualize the GDP across the globe. Link to the dataset.

 # Importing the required libraries  
 import geopandas as gpd   
 from shapely.geometry import Point  
 from matplotlib import cm  
 # GDP mapped to the corresponding country and their acronyms  
 df =pd.read_csv('GDP.csv')  
 df.head()  

	COUNTRY	GDP (BILLIONS)	CODE
0	Afghanistan	21.71	AFG
1	Albania	13.40	ALB
2	Algeria	227.80	DZA
3	American Samoa	0.75	ASM
4	Andorra	4.80	AND

### Importing the geometry locations of each country on the world map  
 geo = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))[['iso_a3', 'geometry']]  
 geo.columns = ['CODE', 'Geometry']  
 geo.head()  

# Mapping the country codes to the geometry locations  
 df = pd.merge(df, geo, left_on='CODE', right_on='CODE', how='inner')  
 #converting the dataframe to geo-dataframe  
 geometry = df['Geometry']  
 df.drop(['Geometry'], axis=1, inplace=True)  
 crs = {'init':'epsg:4326'}  
 geo_gdp = gpd.GeoDataFrame(df, crs=crs, geometry=geometry)  
 ## Plotting the choropleth  
 cpleth = geo_gdp.plot(column='GDP (BILLIONS)', cmap=cm.Spectral_r, legend=True, figsize=(8,8))  
 cpleth.set_title('Choropleth Graph - GDP of different countries')  

Surface plot

Surface plots are used for the three-dimensional representation of the data. Rather than showing individual data points, surface plots show a functional relationship between a dependent variable (Z) and two independent variables (X and Y).

It is useful in analyzing relationships between the dependent and the independent variables and thus helps in establishing desirable responses and operating conditions.

 from mpl_toolkits.mplot3d import Axes3D  
 from matplotlib.ticker import LinearLocator, FormatStrFormatter  
 # Creating a figure  
 # projection = '3d' enables the third dimension during plot  
 fig = plt.figure(figsize=(10,8))  
 ax = fig.gca(projection='3d')  
 # Initialize data   
 X = np.arange(-5,5,0.25)  
 Y = np.arange(-5,5,0.25)  
 # Creating a meshgrid  
 X, Y = np.meshgrid(X, Y)  
 R = np.sqrt(np.abs(X**2 - Y**2))  
 Z = np.exp(R)  
 # plot the surface   
 surf = ax.plot_surface(X, Y, Z, cmap=cm.GnBu, antialiased=False)  
 # Customize the z axis.  
 ax.zaxis.set_major_locator(LinearLocator(10))  
 ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))  
 ax.set_title('Surface Plot')  
 # Add a color bar which maps values to colors.  
 fig.colorbar(surf, shrink=0.5, aspect=5)  
 plt.show()  

One of the main applications of surface plots in machine learning or data science is the analysis of the loss function. From a surface plot, we can analyze how the hyperparameters affect the loss function and thus help prevent overfitting of the model.

Visualizing high-dimensional datasets

Dimensionality refers to the number of attributes present in the dataset. For example, consumer-retail datasets can have a vast amount of variables (e.g. sales, promos, products, open, etc.). As a result, visually exploring the dataset to find potential correlations between variables becomes extremely challenging.

Therefore, we use a technique called dimensionality reduction to visualize higher dimensional datasets. Here, we will focus on two such techniques :

• Principal Component Analysis (PCA)
• T-distributed Stochastic Neighbor Embedding (t-SNE)

Principal Component Analysis (PCA)

Before we jump into understanding PCA, let’s review some terms:

• Variance: Variance is simply the measure of the spread or extent of the data. Mathematically, it is the average squared deviation from the mean position.
• Covariance: Covariance is the measure of the extent to which corresponding elements from two sets of ordered data move in the same direction. It is the measure of how two random variables vary together. It is similar to variance, but where variance tells you the extent of one variable, covariance tells you the extent to which the two variables vary together. Mathematically, it is defined as:

A positive covariance means X and Y are positively related, i.e., if X increases, Y increases, while negative covariance means the opposite relation. However, zero variance means X and Y are not related.

PCA is the orthogonal projection of data onto a lower-dimension linear space that maximizes variance (green line) of the projected data and minimizes the mean squared distance between the data point and the projects (blue line). The variance describes the direction of maximum information while the mean squared distance describes the information lost during projection of the data onto the lower dimension.

Thus, given a set of data points in a d-dimensional space, PCA projects these points onto a lower dimensional space while preserving as much information as possible.

In the figure, the component along the direction of maximum variance is defined as the first principal axis. Similarly, the component along the direction of second maximum variance is defined as the second principal component, and so on. These principal components are referred to the new dimensions carrying the maximum information.

 # We will use the breast cancer dataset as an example  
 # The dataset is a binary classification dataset  
 # Importing the dataset  
 from sklearn.datasets import load_breast_cancer  
 data = load_breast_cancer()  
 X = pd.DataFrame(data=data.data, columns=data.feature_names) # Features   
 y = data.target # Target variable   
 # Importing PCA function  
 from sklearn.decomposition import PCA  
 pca = PCA(n_components=2) # n_components = number of principal components to generate  
 # Generating pca components from the data  
 pca_result = pca.fit_transform(X)  
 print("Explained variance ratio : \n",pca.explained_variance_ratio_)  

 Out: Explained variance ratio :   
  [0.98204467 0.01617649]  

We can see that 98% (approx) variance of the data is along the first principal component, while the second component only expresses 1.6% (approx) of the data.

 # Creating a figure   
 fig = plt.figure(1, figsize=(10, 10))  
 # Enabling 3-dimensional projection   
 ax = fig.gca(projection='3d')  
 for i, name in enumerate(data.target_names):  
   ax.text3D(np.std(pca_result[:, 0][y==i])-i*500 ,np.std(pca_result[:, 1][y==i]),0,s=name, horizontalalignment='center', bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))  
 # Plotting the PCA components    
 ax.scatter(pca_result[:,0], pca_result[:, 1], c=y, cmap = plt.cm.Spectral,s=20, label=data.target_names)  
 plt.show()  

Thus, with the help of PCA, we can get a visual perception of how the labels are distributed across given data (see Figure).

T-distributed Stochastic Neighbour Embedding (t-SNE)

T-distributed Stochastic Neighbour Embeddings (t-SNE) is a non-linear dimensionality reduction technique that is well suited for visualization of high-dimensional data. It was developed by Laurens van der Maten and Geoffrey Hinton. In contrast to PCA, which is a mathematical technique, t-SNE adopts a probabilistic approach.

PCA can be used for capturing the global structure of the high-dimensional data but fails to describe the local structure within the data. Whereas, “t-SNE” is capable of capturing the local structure of the high-dimensional data very well while also revealing global structure such as the presence of clusters at several scales. t-SNE converts the similarity between data points to joint probabilities and tries to maximize the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embeddings and high-dimension data. In doing so, it preserves the original structure of the data.

 # We will be using the scikit learn library to implement t-SNE  
 # Importing the t-SNE library   
 from sklearn.manifold import TSNE  
 # We will be using the iris dataset for this example  
 from sklearn.datasets import load_iris  

 # Loading the iris dataset   
 data = load_iris()  
 # Extracting the features   
 X = data.data  
 # Extracting the labels   
 y = data.target  
 # There are four features in the iris dataset with three different labels.  
 print('Features in iris data:\n', data.feature_names)  
 print('Labels in iris data:\n', data.target_names)  

 Out: Features in iris data:  
  ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']  
 Labels in iris data:  
  ['setosa' 'versicolor' 'virginica']  

 # Loading the TSNE model   
 # n_components = number of resultant components   
 # n_iter = Maximum number of iterations for the optimization.  
 tsne_model = TSNE(n_components=3, n_iter=2500, random_state=47)  
 # Generating new components   
 new_values = tsne_model.fit_transform(X)  
 labels = data.target_names  
 # Plotting the new dimensions/ components  
 fig = plt.figure(figsize=(5, 5))  
 ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)  
 for label, name in enumerate(labels):  
   ax.text3D(new_values[y==label, 0].mean(),  
        new_values[y==label, 1].mean() + 1.5,  
        new_values[y==label, 2].mean(), name,  
        horizontalalignment='center',  
        bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))  
 ax.scatter(new_values[:,0], new_values[:,1], new_values[:,2], c=y)  
 ax.set_title('High-Dimension data visualization using t-SNE', loc='right')  
 plt.show()  

Thus, by reducing the dimensions using t-SNE, we can visualize the distribution of the labels over the feature space. We can see that in the figure the labels are clustered in their own little group. So, if we’re to use a clustering algorithm to generate clusters using the new features/components, we can accurately assign new points to a label.

Conclusion

Let’s quickly summarize the topics we covered. We started with the generation of heatmaps using random numbers and extended its application to a real-world example. Next, we implemented choropleth graphs to visualize the data points with respect to geographical locations. We moved on to implement surface plots to get an idea of how we can visualize the data in a three-dimensional surface. Finally, we used two- dimensional reduction techniques, PCA and t-SNE, to visualize high-dimensional datasets.

I encourage you to implement the examples described in this article to get a hands-on experience. Hope you enjoyed the article. Do let me know if you have any feedback, suggestions, or thoughts on this article in the comments below!

Music is the universal language of mankind—a great uniter. It’s astonishing how music can connect souls, overcome barriers, and bring people closer. It is something that people who differ on anything and everything can have in common.

The World Music Hackathon is a festival of music, innovation, and creativity. We are pushing down the boundaries between “hacking” and “music” to bring the music and tech world together. There are no limits to what you can create; we encourage hacking of music in the broadest conceivable sense, for example, through instrument-building, data visualization, collaboration, improvisation, or any other way you can imagine.

There are craftsmen, researchers, and other music programmers who are doing great work in the field of music, however, they are not getting the consideration they merit for whatever reason.

This is your platform to change the future of the underserved music community and is by no means is limited to young and old, regional or cultural genres or gender identity. It is a platform that can induce diversity across backgrounds, perspectives, and abilities to drive personal growth through creation, collaboration, and communication.

Music is becoming more digital every day. What's more, the World Music Hackathon is the phase to explore different avenues regarding its progression and create thoughts for the future of music and music groups.

Your ideas can connect the artist with his or her audience, on- and offline, real-time or over time. Your ideas will interface the path in rethinking and re-engineering music for the digital age.

Here are the primary focus areas:

• Enabling music for the disabled: For people with disabilities, technology has the potential to unlock new possibilities. Technology can enable communication, navigation, and independence of disabled people while learning and creating music.
• Anti-piracy: According to Woolley, about 12.5 billion dollars are lost due to file sharing and music piracy, and 5 billion of that is profits lost from the music industry directly every year. Innovative technology can minimize and discourage music piracy.
• Improving music recommendations: With the advent of technology, the glory of Radio DJs has passed, replacing musical gatekeepers with personalized algorithms and unlimited streaming services. With listeners now interested in a very diverse genre of music, content recommendation is at the heart of most subscription-based streaming platforms to enhance user experience and increase user engagement.
• Ease of learning and playing music: New innovations provide fun and creative ways to enhance the learning experience. Apps and online tools can ease the more unsavory aspects of learning an instrument through gamification and progress tracking which help the learner stay motivated.
• Innovate (reengineering music for the digital age): Innovate solutions that can make a difference in the world of music. You are only limited by your own imagination of what you can create.

We wish to bring together creative developer, designers, musicians, and product visionaries to test ideas and create products with the potential to change the world of music. There’s a lot that can be done here, so let’s unpack those beautiful ideas.Also, in addition to being good for humanity, this also helps foster innovation.