The workshop will have a limited amount of mathematics – instead the focus will be on both the idea behind PGAs and practical coding skills. Starting with the PyGAD library its uses and limitations will be discussed and presented with easy-to-understand examples. Later key aspects of parallel programming will be introduced such as recognizing CPU- and I/O-bound operations and the use of processes and threads respectively. Therefore a basic understanding and implementation of locks barriers flags and shared memory in general will be achieved.
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.
Before joining Innovatrics, Jakub worked at Google, contributing to the development of Google Assistant. He holds a PhD from Brno University of Technology, specializing in computer science and artificial intelligence. Technical analysis involves the use of patterns, volume of shares, and charts to predict future fluctuations in the BankNifty. Indicators like moving averages and the Relative Strength Index are some of the well-known indicators that help identify potential trends and entry levels.
This new approach allows us to obtain both an overall sum representation as well as the inner 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.
Nifty Bank Target for Tomorrow – Using EMA Indicator
Bank Nifty, which was established by the National Stock Exchange (NSE) in 2003, is a standard by which to measure the performance of banking firms on the stock exchange. Large and extremely liquid stocks from India’s banking industry make up its members. According to the NSE, Bank Nifty is calculated using the free float capitalization approach. It indicates that the market capitalization of banks that are included in the Bank Nifty is determined solely by the shares that are open for public trade. In this talk, we will explore the field of topic modeling for text documents, focusing on its challenges and practical applications. I will highlight various methods for clustering text documents, enhancing clustering quality, validating results, and integrating solutions into users’ daily workflows.
Iris Hotel Eden – Czech Leading Hotels
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.
Q19. How can one stay updated on BankNifty news and analysis?
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.
Real-Time Anomaly Detection and Alerting in Financial Markets Using Stream Processing
It is very easy for investors to trade the BankNifty futures and options and invest in the BankNifty index either through index funds or through BankNifty-based exchange-traded funds (ETFs). These products help you get connected with the banking industry’s operations. In the modern trading landscape, technology plays a pivotal role in Bank Nifty forecasting. GOC Technology Bank Nifty, with its innovative approach, has become a game-changer for traders seeking accurate predictions. Technical indicators, such as relative strength index (RSI) and moving average convergence divergence (MACD), are essential components of Bank Nifty analysis. These indicators offer insights into momentum, the strength of Bank Nifty trend analysis, and potential entry or exit points.
Prague appeals to different types of visitor – both party…
I emphasize continuous learning, open collaboration, and a balance of strategic oversight with hands-on involvement, ensuring practical solutions and tangible results. I am a long time industry expert having worked for SKF, Digital Equiment, Compaq and Hewlett Packard Enterprise in various roles including R&D Engineer, Application Engineer, CAE and HPC Consultant, and Pre-Sales solutions architect. Prof. Dr. Alexander nifty bank tomorrow prediction Jesser holds the diploma degree in Computer Engineering from the University of Paderborn, Germany and the Ph.D. in computer engineering from the Johann-Wolfgang Goethe University of Frankfurt a.M. Since 2013 he is a full professor for embedded systems and communications engineering at the University of Applied Sciences Heilbronn, Germany. Since 2021 he is the head of the Institute of Intelligent Cyber-Physical Systems ICPS at the University Heilbronn, Germany. He is conducting research in the field of Cyber-Physical Systems, Signal- Image-, and Voice Processing in industrial and medical technology applications.
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.
How the calculation of bank nifty is done?
Secondly, I will show how we can use multimodal large language models to (1) detect misinformation based on visual content and (2) provide strong alternative explanations for the visual content. Bank Nifty witnessed a notable recovery last week, supported by the Reserve Bank of India’s recent 25 basis point rate cut to 6% and its shift to a more accommodative policy stance. These developments provided support to banking stocks, which outperformed the broader market undercurrents.
- Cedric Clyburn (@cedricclyburn), Senior Developer Advocate at Red Hat, is an enthusiastic software technologist with a background in Kubernetes, DevOps, and container tools.
- 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.
- Current solutions while effective have limitations in terms of coverage maintenance and precision.
- Philipp Wendland is a Senior Consultant in the Deloitte AI Institute, Germany.
- Karel Piwko is a Senior Principal Software Engineer with extensive experience in both management and technical roles within the software industry.
It is important to note that predicting the Nifty Bank Index or any financial index with absolute certainty is challenging, as it depends on numerous unpredictable factors. Market participants and analysts use these methodologies to make informed investment decisions, but there is always an inherent degree of uncertainty involved. We will also livestream the talks for all those participants who prefer to attend the conference online.
- 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.
- A decisive close above this could pave the way for further gains, but until then, upside potential may remain limited.
- The first involves solving a labyrinth demonstrating how a parallel genetic algorithm can efficiently navigate complex search spaces and de facto interact with an environment.
- A decline might be witnessed in case the performance of the BankNifty is underwhelming while a solid earnings report may lead to a rise in the indices.
The BB84 protocol will be the protocol in question explained without the quantum mechanics’ mathematical rigor and the essentials of the protocol will already be implemented. The third and last case study will be a neural network in which hyperparameters will be optimized by a PGA in a Genetically Reinforced Learning scheme. Knowing how the PGA may interact with an environment and work on even very complicated functions this optimization task will be an easy step for those who have already seen the neural network. By the end of the workshop participants will have a solid understanding of how to implement and apply parallel genetic algorithms in Python with practical insights into their strengths and limitations. They will be equipped with the knowledge to extend these techniques to other domains fostering innovation in computational problem-solving.
I’m an AI Engineering Manager and Technology Consultant with extensive experience in ML Engineering. My work bridges Machine Learning, Data Science, Product Management, and Strategic Communication. I prioritize aligning cutting-edge AI with business objectives, delivering scalable solutions in Recommendation Systems, Generative AI, Computer Vision, and Advanced Data Analysis.