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Medium
5d
373
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A Headset-to-Headset Clash!
- Sentiment analysis involves analyzing a word, sentence, paragraph or even a whole story to determine its overall sentiment.
- In this project, we rank comments on a scale of 1 to 5 using the NLP model BERT.
- BERT or Bidirectional Encoder Representations from Transformers is a large-scale Natural Language Processing Model developed by Google in 2018.
- We can use this on our extracted comment files to provide sentiment scores for each comment.
- Using Python and external libraries such as Seaborn, Matplotlib, and Wordcloud, we can plot word clouds and visualize sentiment distribution.
- The average sentiment for all comments does not have a very high difference, and both devices failed to have an overall positive sentiment.
- We see a pattern in the comment section that people talk about the same few aspects.
- The high price point of the Vision Pro has been mentioned negatively in the comment section for both devices.
- Adding this to the fact that the Meta Quest 3 has a higher Field of Vision (FoV) than the Apple Vision Pro, we can confidently say that the Quest 3 has the upper hand in this segment.
- Overall, after analyzing over 12000 comments, we can see that the Quest 3 has a higher average sentiment than the Apple Vision Pro.
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Medium
5d
262
How Fair representation learning works part4(Machine Learning)
- Algorithmic fairness is an important machine learning problem in mission-critical Web applications.
- DualFair is a self-supervised model that debiases sensitive attributes like gender and race from learned representations.
- This model jointly optimizes for group fairness and counterfactual fairness to make fair predictions at both group and individual levels.
- It uses contrastive loss and self-knowledge distillation to generate indistinguishable embeddings for protected groups and maintain representation quality.
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Medium
5d
168
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Exploratory Data Analysis for TikTok Claim Classification
- TikTok is developing a machine learning model to classify videos based on whether they contain claims or opinions.
- The aim is to streamline the user report process, reduce backlog, and enhance content moderation tasks.
- The project is in its initial stages of claims classification, focusing on conducting extensive Exploratory Data Analysis (EDA).
- The dataset contains a mix of data types, and the main objective is to develop a machine learning model that categorizes videos as claims or opinions.
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Medium
5d
246
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Game of Codes: The Chronicles of King Python and the Digital Kingdoms.
- King Python reigns supreme in the digital kingdom of Jupyter, with an array of programs as his diligent citizens and a council of minister-libraries to manage the kingdom.
- Other neighboring kingdoms include Google Colab, Visual Studio, PyCharm, and Replit, each with its own unique strengths and weaknesses.
- King Python envisioned a digital realm of collaboration where each kingdom could share strengths and resources, resulting in the formation of the United IDEs alliance.
- Under the United IDEs banner, each kingdom contributed its strengths, with King Colab offering clouds for storage, Queen Studio providing armor for defense, King Charm illuminating paths with his IntelliSense, and Prince Replit ensuring rapid deployment and diversity.
- In this alliance, the kingdoms faced challenges together, tackling monstrous bugs and stealthy threats, resulting in a golden age of technological advancement.
- The United IDEs story serves as a reminder to adopt a collaborative spirit, and to contribute to a thriving tech world.
- Actionable insights for coders include enhancing their own coding skills while contributing to a thriving interconnected tech world.
- In Jupyter, King Python's council of minister-libraries managed the kingdom, with NumPy managing the kingdom’s numbers, Pandas keeping records, Matplotlib painting charts, and SciPy solving any complex riddle thrown his way.
- In PyCharm, every alley and avenue was lit with IntelliSense, with the watchful overseer King Charm ensuring that no citizen every lost their way in code.
- The story of the United IDEs alliance teaches the power of collaboration and the strength found in diversity.
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Medium
5d
41
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Learn Python for Data Science through Stories.
- Jupyter is a kingdom of lush landscapes flowing with data and towering stacks of code. King Python, its ruler of unmatched wisdom, leads the kingdom with his council of minister-libraries NumPy, Pandas, Matplotlib, and SciPy.
- Beyond the borders of Jupyter are other kingdoms such as Google Colab, Visual Studio, PyCharm, and Replit, each unique in its ways and ruled by equally wise leaders.
- King Python envisions a grand alliance to unite all the kingdoms under the banner of United IDEs, where all can share their strengths, compensate for their weaknesses, and multiply their capabilities.
- During their journey, King Python and his ministers traveled to every kingdom and quickly proved the worth of their libraries.
- The kingdoms witnessed the benefits of collaboration and agreed to join the United IDEs. Together, they forged a realm of unprecedented efficiency and power, safeguarding against common enemies like bugs and errors, under the wise leadership of King Python and the protection of his ministers.
- The United IDEs shared knowledge, tools, and resources, making sure that every coder in the digital world had access to the best of all kingdoms
- The story of the United IDEs serves as a beacon of collaboration and unity, teaching the power of sharing and the strength found in diversity.
- Adopt the strategies of the United IDEs to contribute to a thriving, interconnected tech world, and enhance your coding prowess.
- In our world, just as in Jupyter, unity, and cooperation forge the strongest alliances and the most enduring successes.
- The United IDEs serve as a clear representation of the benefits collaboration and cooperation bring in a community. In an interconnected tech world, unity can lead to stronger innovation.
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Medium
6d
217
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Unleashing the Power of Python for Data Analysis
- Python's simplicity, readability, and versatility make it a popular choice among data analysts.
- NumPy provides support for large arrays and high-performance mathematical functions.
- Pandas simplifies data manipulation and analysis with its data structures and tools.
- Matplotlib and Seaborn offer powerful visualization capabilities in Python.
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13 Likes
Medium
6d
320
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Financial Data Visualization for Finance Professionals and Data Analysts
- Financial data visualization is critical for finance professionals and data analysts.
- It helps reveal patterns, trends, and correlations in financial data.
- Platforms like Tableau, Power BI, and RapidMiner offer powerful tools for financial data visualization.
- With a clear objective, choosing the right platform, and continuous learning, professionals can enhance their data visualization skills.
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Medium
6d
201
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Demystifying Neural Networks: From Theoretical Foundations to Practical Applications
- Neural networks consist of neurons that are interconnected in layers for processing and transmitting information. The input layer receives data, hidden layers perform actual processing, and the output layer provides the final decision. Neurons sum up weighted inputs, pass them through activation functions to introduce non-linearity, and learn complex patterns.
- Sigmoid is a popular activation function because it is continuous, differentiable, and outputs values in the range (0, 1). Overcoming the limitations of binary outputs, sigmoid handles probabilities and works well in instances where binary outputs are insufficient. One limitation of the sigmoid is that its output slows during negative input.
- A perceptron is a single neuron in a neural network, and it has adjustable parameters. It calculates the weighted sum of inputs and outputs using activation functions to produce an output. Multilayered perceptrons (MLP) consist of more than one layer of neurons, which enhances the network's ability to model complex relationships.
- One of the challenges with neural network training is overfitting, where the model performs well on training data but poorly on unseen data. Techniques to mitigate overfitting include Regularization and Dropout.
- To demonstrate the practical application of neural networks, the author developed a project using Fast.ai to identify broken headphones from images. The project lifecycle includes data preparation, where images load from a directory, training the pre-trained ResNet34 model, and fine-tuning it. The model is then trained and optimized.
- Neural networks have boundless opportunities in various domains, such as personalized medicine, advanced robotics, and natural language processing. Whether it is through improving consumer products, enhancing medical diagnostics, or revolutionizing data processing, neural networks are paving the way for an era where artificial intelligence is not just a helper, but a transformative force across industries.
- The journey through the realms of neural networks is highly challenging but rewarding. Data scientists and other enthusiasts who understand their fundamental workings and explore hands-on applications can leverage these models to solve complex problems and drive innovation across various domains.
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Medium
6d
350
Working with Neural Link Predictors part4(Machine Learning 2024)
- In adversarial training, a set of models learn together by pursuing competing goals, usually defined on single data instances.
- However, in relational learning and other non-i.i.d domains, goals can also be defined over sets of instances.
- A link predictor for the is-a relation needs to be consistent with the transitivity property.
- Experiments on link prediction benchmarks indicate that given suitable prior knowledge, our method can significantly improve neural link predictors on all relevant metrics.
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Medium
6d
226
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SAS — The Bare Minimum: Part 2 “PROC and Roll”
- Today, we’ll get started on the basic tools for turning that data into forms that actually mean something to humans.
- SASHELP library includes many standard libraries for data science applications, such as the familiar “Iris” and “Cars” datasets.
- PROC procedures are quite similar to what we might see as built-in functions in something like R.
- PROC steps apart from DATA steps, and a few useful procedures.
- PROC step on the other hand, is mainly used for analyzing data. The result is typically a report, rather than a dataset.
- PROC Report is… well, it’s kind of boring, isn’t it?
- PROC Means is our one-stop shop for descriptive statistics.
- SGPlot is our swiss-army knife for visualizing data.
- Finally, we can see what the real applications of SAS are! With only a few lines of code, it’s possible to generate descriptive statistics, report datasets, and generate visualizations.
- Next time, I’ll focus in more on how to wrangle and clean data in SAS.
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Medium
6d
82
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Excel Learning Series Part-17
- Excel allows you to import data from various external sources, including databases, text files, CSV files, XML files, and web pages.
- You can use Excel's built-in data import features to connect to external data sources and bring data directly into your Excel workbook.
- Excel also provides options for exporting data from your workbook to external files or formats such as CSV, text files, PDF files, and HTML files.
- These import and export features in Excel facilitate data exchange and integration with other systems, enabling you to work with data from diverse sources and share your analysis effectively.
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Medium
6d
107
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Title:Unveiling the Power of Influencer Marketing:
- Influencer marketing has become a fundamental pillar of modern marketing strategies.
- Influencers have cultivated authenticity and trust with their followers, making their endorsements highly compelling.
- Influencers have large and engaged followings, allowing brands to reach targeted audiences.
- Collaborating with influencers can benefit a brand's SEO efforts and improve search engine rankings.
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Cloudblog
6d
140
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Tencent: Building analytics culture for better game development
- Tencent, the leading internet and technology company, has focused on building an analytics culture at Funcom, a gaming company acquired by Tencent.
- To support their business decisions, Funcom collaborated with Google Cloud to develop a new architecture, including a data warehouse and dashboards for key stakeholders.
- They replaced their legacy technology stack with Google Cloud products like Cloud Storage and BigQuery, enabling access to new game data and optimizing cost.
- With the new architecture, Funcom automated their pipeline, connected gaming and marketing datasets, and increased their daily game data processing by twice while reducing costs by 70%.
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Cloudblog
6d
325
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How Trendyol solves cloud governance at scale with Looker
- Trendyol has found that combining mechanisms and principles developed with Looker allows them to achieve true cloud governance while maintaining flexibility. Looker's access control features and other capabilities provide easy ways to implement data governance.
- Cloud environments can make balancing innovation and risk mitigation challenging because various developers and governance entities consume the resources. At Trendyol, thousands of users leverage Looker for everyday business intelligence tasks, including processing data and delivering reports.
- Trendyol's Hub and Spoke model and cache optimizations utilize Looker's smart caching mechanisms to avoid redundant data fetching.
- Moreover, it optimizes cloud governance by executing only the required number of scheduled jobs, ensuring they are carried out fairly and efficiently, and eliminating unnecessary costs.
- Creating a formalized program for users to join and learn from one another, along with regular meetings, office hours, and other discussion forums, encourages better collaboration to ensure that process is streamlined.
- Trendyol's data warehousing team created a list of useful mechanisms and principles to achieve cloud governance by using Looker.
- Trendyol could centralize its current tools' critical metrics, providing easy access to daily work for its data analysts, business users, and other teams.
- Trendyol could execute over 260,000 queries a day during the peak periods with Looker's help while empowering more than 1,500 employees.
- Various internal, production, and non-production applications within cloud environments present challenges to achieving governance. The different governance entities must also be taken into account to solve the problem.
- Trendyol has set up efficient methods to deliver effective cloud governance by combining several strategies designed to coordinate and focus individual teams and policies.
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Cloudblog
6d
206
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Breaking barriers: How BigQuery data insights boosts the data exploration journey
- New data insights capabilities in BigQuery can accelerate analytics workflows by generating insightful queries about hidden patterns within a table.
- The data insights feature uses Google's Gemini models to generate queries based on a table's metadata and data attributes.
- The grounding process of generated queries ensures relevance and accuracy of insights with statistical summaries of metadata attributes.
- The data insights feature is tailored for both admins and data consumers to democratize data analysis and foster collaboration.
- Data consumers can review and execute generated queries, while admins generate relevant queries with the necessary permissions and access.
- Efficient data exploration enables exploration of new tables more efficiently and independently, streamlining the data exploration process.
- Time and resource savings enable data professionals to focus on more challenging projects, while fostering collaboration promotes a unified approach to data interpretation.
- Real-time insights automatically derive insights from continuously flowing business data, allowing data teams to respond to changing business conditions in real-time.
- BigQuery data insights is a powerful tool to help unlock valuable insights from your data.
- Explore BigQuery data insights to fast-track your data exploration process and streamline analytics workflows.
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