It is important to note that while comparing it with the overall Nifty, the latter is volatile and sensitive to fluctuation in interest rates, regulatory changes, and the overall banking system. Additionally, LLM applications often extract web data to provide up-to-date context for answers using Retrieval Augmented Generation (RAG). I will emphasize the importance of preprocessing input documents to improve clustering quality.
Conference day 1
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Our presentation will share insights and lessons learned from building topic modeling inside a product that serves real customers, emphasizing the challenges of creating scalable systems that deliver real value. He focuses on large language models (LLMs), agentic systems, and machine reasoning and their practical implementation through model training and tuning. He uses his expertise as part of the Search and Recommendation team, where he scales up solutions to address complex user requirements. Jon McLoone is central to driving the company’s technical business strategy and leading the consulting solutions team.
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A particularly difficult task for Similarweb is the estimation of ad hoc cohorts of users, as clients require the freedom to query and compare their relevant online behavior metric for their specific use case. We briefly present an earlier approach we have adopted i.e. assigning each panel user a singular weight and applying simple rescaling within a given cohort. This singular weight is useful under strong constraints, but yields poor results on ad hoc cohorts as it cannot account for the non linear nature of interaction between users and cohorts of which they are a part. These methods range from traditional statistical formulas to sophisticated similarity algorithms leveraging sparse matrix structures.
These clusters are notoriously diverse in appearance, making it difficult to distinguish benign from malignant cases with precision. This diagnostic uncertainty often results in unnecessary biopsies, causing undue stress for patients and placing additional burdens on healthcare systems. To address this, we turned to the transformative potential of artificial intelligence (AI), specifically convolutional neural networks (CNNs), to refine the accuracy of breast cancer screening. Since joining the CIIRC RICAIP TESTBED in 2022, Artem has been applying his expertise in computer vision, machine learning, and deep learning to develop algorithms for a range of industrial applications. Martin is a Principal ML Engineer at King, Sweden, specializing in recommender systems, interpretable learning, contextual multi-armed bandits, and off-policy learning.
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We introduce a new multistep approach to create the context dependent weighing. First, we obtain a representative embedding of both common types of cohorts (websites, search terms, product views etc.) and a respective embedding representing users using the same encoder. Second, we train a recommendation-like neural network that learns the non linear interactions between users and their cohorts.
This new approach allows us to obtain both an overall sum representation as well as the inner nifty bank tomorrow prediction weight distribution for an ad hoc cohort. We demonstrate the usefulness of this new approach on several examples of ad hoc cohorts. It is interesting to note that as an additional byproduct of this training process, we can extract a useful intermediate from the network that embeds both users and cohorts under the constraint of actual data and panel biases. His current research focuses on the applications of computational intelligence, machine learning, and stochastic analysis in physics. He is particularly interested in QKD error correction algorithms, near-infrared spectroscopy, and modeling weather at sea. Philipp Wendland is a Senior Consultant in the Deloitte AI Institute, Germany.
- By combining generative AI with more traditional symbolic AI, reliability can be maintained, explainability improved and private knowledge and data injected.
- Ever more decisions are driven by advanced, nonlinear data analysis, where the validity, correctness, and fairness of the outcomes are often assumed but difficult to guarantee in practice.
- A banking stock’s free float factor is multiplied by its market capitalization in order to determine its inclusion in the Bank Nifty.
- He has experience speaking and organizing conferences including DevNexus, WeAreDevelopers, The Linux Foundation, KCD NYC, and more.
- Neural networks are ubiquitous yet they remain opaque for most of its users, who has very little understanding of how they store the knowledge and how the information propagates through.
The presentation explores the nature, cause and consequences of this “hallucination” issue and proposes a solution. His research includes work on classifying red blood cells using time-distributed convolutional neural networks from simulated videos, as well as developing curated datasets for red blood cell tracking in microfluidic devices. Alessandro worked as post-doctoral researcher at the French Institute for Research in Computer Science (INRIA), Technical School of Switzerland (ETH-Zurich), Italian Institute for Technology (IIT), and University Hospital of Zurich.
If this momentum carries forward into the new trading week, the Nifty could extend its recent recovery rally. Analysts see immediate resistance around the 23,000 level, while support is pegged near 22,700. A decisive move beyond these levels could determine the direction for the rest of the week. Navigating the stock market requires skill, patience, and the right insights.
This includes extracting relevant elements from the text, such as entities, key phrases, or summaries. We will explore techniques to detect new clusters as they appear while maintaining the integrity of existing clusters. Karel Piwko is a Senior Principal Software Engineer with extensive experience in both management and technical roles within the software industry.
- These indicators offer insights into momentum, the strength of Bank Nifty trend analysis, and potential entry or exit points.
- They will present advanced practical talks, hands-on workshops, and other forms of interactive content to you.
- We will explore the architecture of an LLM-based anti-tracking system developing the data pipeline and exploring how these models can be fine-tuned to analyze network requests page content and user interactions in real-time.
This is a machine learning pipeline combining reservoir computing and directed graph analysis to model brain connectivity in stroke patients using MRI data. Effective connectivity is derived via reservoir computing, enabling the creation of directed graph representations. Explainable AI tools provide insights into disrupted brain networks, elucidating biomarkers for stroke classification and enhancing clinical interpretability. This approach highlights the potential of machine learning to improve patient stratification in stroke and other brain diseases.
He was previously a VP of Data and Data Engineer at high growth startups and has led cross-functional data teams in developing analytics platforms and data-intensive AI applications. In his spare time, Tun organises PyData Cornwall meetups, goes surfing, plays guitar and tends to his analogue cameras. I’m a researcher at CTU CIIRC Testbed for Industry 4.0, where I also interned during my studies. My focus is on the usage of computer vision and machine learning in industrial applications.
Dr Stanislav Fort is a prominent artificial intelligence researcher specializing in Large Language Models (LLMs), interpretability, and AI safety. His career includes the position of a language model lead at Stability AI, a contribution towards the Anthropic’s AI system Claude, and research roles at Google Brain and DeepMind. He is currently working on AI security on the Gemini team as a senior research scientist at Google DeepMind. He obtained his PhD in artificial intelligence at Stanford University in California, USA, and studied physics for his Bachelor’s and Master’s degrees at Cambridge University, eventually specializing in black holes.
As the banking sector in India continues to evolve and adapt to changing market conditions and regulatory frameworks, the Nifty Bank Index is likely to reflect these developments. Market participants will closely monitor factors such as economic reforms, interest rate changes, government policies, and industry trends to gauge the future performance of the index. These are the periods of rebalancing and they occur in March and September of the financial year in the case of BankNifty. Portfolio rebalancing entails a process of reviewing and adjusting the component stocks based on the market capitalization and liquidity levels in the banking sector. Platforms like TradingView offer an interactive way to visualize trends, patterns, and technical indicators. Additionally, resources such as Moneycontrol and Investing.com provide valuable data-driven insights that can inform trading decisions.
NIFTY Bank Total Returns Index and Bank Nifty TRI are two examples of its index variants. Also known as Nifty Bank, the Bank Nifty index refers to an index that is made up of the 12 highly liquid and large capitalized banking stocks of India. It gives investors a standard by which to measure the performance of Indian bank equities on the capital market. Bank Nifty predicted for Tomorrow is ₹52,425, According to the experts and the analysis of various technical indicators for Bank Nifty forecast tomorrow, Bank Nifty is likely to face a uptrend. The movement of tomorrow Nifty Bank Prediction It looks like NIFTY_BANK is traveling in a straight line. How come drones are still mainly human controlled and have such limited autonomy?
Among sectoral indices, FMCG stocks outperformed, while the realty sector remained under pressure. In global markets, U.S. bond yields spiked, with the 10-year Treasury yield rising by 11.55% to 4.462%, while the U.S. dollar index dropped below the 100-mark, declining 2.72%. Meanwhile, the Indian rupee weakened by 0.78% to Rs 86.18 per U.S. dollar amid concerns over the current account deficit and persistent foreign outflows. Foreign Institutional Investors (FIIs) offloaded Rs 20,911 crore during the week, while Domestic Institutional Investors (DIIs) absorbed some of the pressure with Rs 21,955 crore in net inflows. The India VIX, a measure of market volatility, surged 46%, highlighting growing investor nervousness. The Bank Nifty tomorrow prediction is an estimate of how the index may behave during the next trading session, helping investors anticipate market movements.