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Analyticsindiamag
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NVIDIA is Hiring AI Engineers in India
- NVIDIA is hiring experienced AI engineers in India to join its partner companies.
- The roles are based in Bangalore and New Delhi and focus on the position of Deep Learning Solutions Architect.
- Requirements for the role include a degree in Engineering (BE/B.Tech/MS/MTech), preferably in CS/IT/Electrical/Electronics or equivalent.
- The company is seeking candidates with a proven track record of 2-5 years in writing code in Python, PyTorch, TensorFlow.
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Medium
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Transformer Neural Networks: Game changer in Natural Language Processing
- Transformer neural networks are a game changer in natural language processing.
- They revolutionize sequential data processing with self-attention mechanism.
- Transformers excel at contextual understanding tasks like translation and sentiment analysis.
- They have applications beyond NLP, such as computer vision and speech recognition.
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Medium
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Unravelling the Power of Knowledge Graphs in Data Analytics
- Knowledge graphs are a powerful tool in data analytics, providing a sophisticated model of data organization.
- They integrate entities, attributes, and relationships to emphasize connections and context.
- Knowledge graphs enhance tasks like semantic searches, recommendation systems, and natural language processing.
- They enable advanced analytics, knowledge discovery, and improved data-driven decision-making.
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Analyticsindiamag
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Zebra Brings Generative AI to the Frontlines with Google, Android, and Qualcomm
- Zebra Technologies partners with Google Cloud, Android, and Qualcomm to bring generative AI capabilities to frontline workers.
- The collaboration integrates Zebra's expertise, Google Cloud's AI, Qualcomm's hardware, and Android's software.
- The new capabilities aim to assist frontline workers by providing a chat experience on their handheld devices.
- The partnership enables workers to access information, retrieve answers, and improve decision-making in real-time.
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Medium
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Where to start learning Data Science
- Learning data science can be challenging, similar to learning a new language.
- Once committed to data science, continuous learning is essential.
- Data science offers numerous opportunities in the long term, given the rapid technological advancements and digital transformation.
- Implementing effective data processing methods can provide valuable insights for business strategies and future trends.
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Medium
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Introduction to LLMs: A Component Overview
- LLMs, or Large Language Models, play a pivotal role in AI-human interaction.
- LLMs can remember past conversations and adapt their suggestions accordingly.
- Different LLM models yield varied results and require specific coding styles.
- Tuning hyperparameters is essential for effective LLM usage.
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Medium
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PLATFORM for MACHINE LEARNING
- A platform for machine learning refers to a software framework or tool that allows users to develop, train, deploy, and manage machine learning models efficiently.
- These platforms offer features such as data preprocessing, algorithm selection, model evaluation, and visualization tools to support the end-to-end machine learning workflow.
- They facilitate collaboration among team members, offer scalability for large datasets, and integrate with various data sources for seamless model deployment.
- Machine learning platforms streamline the development of predictive models, making it easier for organizations to leverage artificial intelligence.
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Medium
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Machine Learning Boot Camp
- A machine learning boot camp is an intensive training program designed to teach participants the fundamentals of machine learning, including algorithms, model building, and data analysis.
- Participants gain hands-on experience through practical projects and exercises, helping them gain critical skills in data science and machine learning.
- The boot camp covers topics such as supervised and unsupervised learning, neural networks, and deep learning techniques.
- Led by industry experts and experienced instructors, the program provides guidance, mentorship, and access to career support services.
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Medium
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Exploring Sentiment Analysis and Word Embeddings with Ryanair Reviews
- This project explores sentiment analysis and word embeddings using Ryanair reviews dataset.
- The project objectives include sentiment analysis, word embeddings with Vader model, and correlation analysis.
- Methodology involves data preprocessing, sentiment analysis, word embeddings, analyzing proportion of sentiments, and correlation analysis.
- Key findings include majority of negative reviews, identified patterns in sentiment expressions, and correlations between sentiment polarity and ratings.
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Medium
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The Building Blocks of LLMs: Vectors, Tokens and Embeddings
- Large language models (LLMs) are revolutionizing the way machines understand, generate, and manipulate human language.
- The fundamental building blocks of LLMs are vectors, tokens, and embeddings, and they play a crucial role in the models' architecture.
- Vectors are the mathematical representation of words, phrases, or even entire documents.
- Tokens are the basic units of language that carry meaning and help in understanding the structure of text.
- Word embeddings are dense vector representations that map words to a continuous vector space, where semantically similar words are closer to each other.
- Pre-trained embeddings like Word2Vec and GloVe play a crucial role in enhancing the LLMs' performance.
- LLMs employ embeddings to tackle real-world NLP tasks like machine translation, sentiment analysis, text classification, and named entity recognition.
- Contextual embeddings and subword tokenization are recent innovations in the realm of NLP.
- Understanding vectors, tokens, and embeddings is crucial for anyone venturing into the world of NLP and LLMs.
- The field of NLP is constantly evolving, and new techniques and architectures are emerging to address the challenges and limitations of traditional approaches.
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Towards Data Science
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How does temperature impact next token prediction in LLMs?
- The temperature parameter in LLMs affects the next token prediction.
- At a temperature of 1, the probabilities are the same as the standard softmax function.
- Increasing the temperature broadens the range of potential candidates for next token prediction.
- Decreasing the temperature boosts the model's confidence and reduces uncertainty.
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Medium
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Bresenham’s Circle Algorithm
- Bresenham’s Circle Algorithm is a method for drawing circles using simple arithmetic and symmetry.
- The algorithm takes advantage of the circle's symmetry and allows the calculation of one point on the circle to be extrapolated to find 8 symmetrical points.
- It is similar to Bresenham’s Line Algorithm and is fast and accurate enough for most purposes.
- Bresenham’s Circle Algorithm is a useful tool in raster graphics for drawing circles efficiently.
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Medium
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From Text to Insights: Essential Techniques for Handling Text Data in ML
- Text data refers to any form of data represented in textual format, such as articles, emails, social media posts, or customer reviews.
- The significance of text data lies in its richness, providing valuable insights into market trends, customer behavior, and even historical events.
- Working with text data comes with its own set of hurdles such as inconsistencies, irrelevant information, and noises.
- Text preprocessing is a crucial initial step in text data analysis, aimed at transforming raw textual data into a structured format.
- Tokenization, Lowercasing, Removing Punctuation, Removing Stop Words, and Stemming and Lemmatization are some of the most common techniques used in text preprocessing.
- Vectorization is a fundamental process in natural language processing (NLP) that transforms textual data into numerical vectors, which can be understood and processed by machine learning algorithms.
- Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) are some commonly used techniques for vectorization.
- N-grams help us capture the relationships and context between words, which can be crucial for tasks like sentiment analysis or topic modeling.
- Text preprocessing and transformations shine in various Natural Language Processing (NLP) tasks, including Sentiment Analysis.
- Text preprocessing and transformation techniques have emerged as powerful tools, transforming raw text into a format that empowers machine learning models to extract meaning and insights.
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Medium
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Best Research on Geometric Deep Learning part2
- New technology for energy storage is necessary for the large-scale adoption of renewable energy sources like wind and solar.
- The Open Catalyst Project aims to apply advances in graph neural networks (GNNs) to accelerate progress in catalyst discovery.
- This study evaluates lightweight approaches for catalyst discovery, making it more approachable for smaller teams and individuals from diverse backgrounds.
- By implementing robust design patterns, a GNN model was trained with high accuracy in predicting per-atom forces of adsorbate-surface interactions.
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Medium
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Best Research on Geometric Deep Learning part1
- Detecting and segmenting moving objects from a moving monocular camera is challenging in the presence of unknown camera motion, diverse object motions and complex scene structures.
- Most existing methods rely on a single motion cue to perform motion segmentation, which is usually insufficient when facing different complex environments.
- A novel monocular dense segmentation method is proposed, combining the strengths of deep learning and geometric model fusion methods.
- The method achieves state-of-the-art motion segmentation results in a zero-shot manner, surpassing some supervised methods without training on any data.
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