Shatrughan Sharma

For Optimization problem, getting a well behaved Cost-function, is like finding a right soul-mate. Smooth, Continuous, Differentiable, Bounded, Convex.. Cost-function ! The one, which is loyal to a single Global-Minima, and never gets trapped into shady local-minimas.. Sarv-gun sampan! Just thinking about it can dilate Mathematicians' pupils, secret oxytocin and increases dopamine-levels. #optimization #statistics #datascience #funatwork

72

1 Comment

Check out this highly recommended course for those interested in optimizing their models: - Introduction to Quantization - Symmetric and Asymmetric Quantization Modes - Hands-on Implementation from Scratch using PyTorch - covering Per Tensor, Per Channel (Axis), and Per Group techniques - Weights Packing: Exploring 2 or 4 Bits Quantization Methods https://lnkd.in/gMAuWXHm

43

Exciting developments are underway in the realm of data analytics and engineering as Microsoft integration into Tableau becomes a reality. This integration holds immense promise and is poised to revolutionize data visualization and analysis for professionals across various industries. The potential synergies between these two powerful platforms are particularly intriguing, especially considering scenarios where individuals within the same organization utilize them in disparate departments. As anticipation builds, we eagerly await further details on how this collaboration will unfold and the transformative impact it will have on data-driven decision-making processes. https://lnkd.in/dXuZutyE

15

Just completed the DeepLearning.AI Natural Language Processing Specialization. While I appreciate the review of foundational knowledge gained from exploring historical approaches like BOW, HMMs and LSTMs, I would rather spend more time on the cutting-edge advancements in NLP, particularly delving deeper into the capabilities of Hugging Face Transformers. All those certifications are fun, but I always feel let down because they are either too much from scratch, not professionalizing enough, or not focused on the current state-of-the-art. What are your thoughts? #LLM #NLP #deeplearning

15

3 Comments

The Buzz word every where is GenAI , LLM's and Multi models , lets discuss on use cases where its driven significant value and not just made life slightly easier for stake holders. Comment below and lets discuss. #genai #llm #datascience

6

6 Comments

I am honoured to have my book listed as 1st book about #genai by MachineHack Generative AI. Thanks, guys! Here's the Amazon link: https://lnkd.in/euCg-M8G

23

5 Comments

Follow up to the previous article about Aswhin - https://lnkd.in/gmaeXBSC #cricket #cricketdata #cricketanalytics #dataanalytics #sportsanalytics

11

1 Comment

Summary of the recently concluded test match. https://lnkd.in/g6hZNruW #cricketanalytics #cricketdata #cricket #sportsanalytics

13

During one of the Cricket match in ICC World Cup T20 Championship, Indian Cricket Team Captain Rohit Sharma applauded Indian Bowler Jasprit Bumrah as Genius. I decided to test it out using data available publicly. I formulated this problem as an Anomaly Detection Problem. For Anomaly Detection I have used AutoEncoders using PyTorch. In this Blog I have extracted publicly available T20 Player Statistics from cricketdata R package, define train and test datasets, train AutoEncoder model and extract insights from the Model. This is a fun experiment and was pleasantly surprised by the results. I have published a Blog in Analytics Vidhya. Please go through the blog to know the result of my experiment.(There is one more player in addition to Jasprit Bumrah who is flagged as Anomaly). https://lnkd.in/dhnJ-NfD I have also uploaded the dataset and code in Kaggle (Link Embedded in Blog). I would like to thank Vetrivel PS and Ashish Patel 🇮🇳 for their valuable Inputs. I would like to thank Analytics Vidhya for publishing my Blog. #pytorch #cricket

14

1 Comment

Bayesian regression has answers for so many problems in the marketing mix modelling space. But it is so very slow. So. . . conjugate priors, then? Normal distributions for everyone! #marketingscience #marketinganalytics

10

3 Comments

If you get a moment, please take this survey for this study on fairness in machine learning for my friend and colleague!

3

1 Comment

Good course on fundamentals of pretraining LLMs from DeepLearning.AI and Upstage! #learning #llms

25

Amped to make valuable contributions to the Data Science community on Analytics Vidhya. My article, "A Guide to Understanding Interaction Terms", was recognized in the top 2 articles for August through the Data Science Blogathon. #DataScience #AnalyticsVidhya #Blogathon #MachineLearning #InteractionTerms

10

Your quest for data quality is actually a cry for better information design. Check out my latest article https://lnkd.in/gFDppRd3 where I delve into this topic. Special thanks to Tejas Chopra and Dan Jones for their valuable feedback. #DataQuality #InformationDesign #DigitalTransformation #AIfriendlyDataDesign

27

3 Comments

🚀 Excited to share that I’ve completed the Kellogg’s certification for Data Strategy for GenAI! 🌟 This course, led by Birju Shah, a renowned expert in AI and product strategy, packed a punch with insights and hands-on learning. Doing this course right after Vin Vashishta's AI product management course was a great segue. A major takeaway was the AI product needs framework, perfectly aligned with the Double Diamond framework. I created a hypothetical company, Designify (think Canva but with a niche market share), to develop a GenAI product strategy, culminating in a comprehensive PRD. The course was action-packed, with assignments that were both challenging and engaging. From qualitative and quantitative research methods, instrumenting user journeys, key path scenarios, persona definition, workflow mapping, and data visualization, to Freytag storytelling, every aspect was covered, leading to creating an optimization equation for your GenAI product. One of the highlights was diving deep into a detailed Uber case study, exploring product metrics, and understanding the intricate workings behind their AI strategies. Feeling ready to apply these learnings to real-world AI and data strategies! Here’s to continuous learning and innovation! 💡🚀 #DataStrategy #GenAI #KelloggCertification #AI #ProductStrategy #Designify #ContinuousLearning #Innovation

31

3 Comments

Quantized embeddings offer significant advantages for NLP models, including reduced storage requirements, faster computations, lower energy consumption, and improved model interpretability. They are becoming increasingly important for enabling efficient and scalable NLP on resource-constrained devices and cloud-based applications. Read my latest article below

6

Ace the NCA-GENL exam! 🚀 Our new practice tests simulate the real deal, giving you the confidence to conquer Generative AI. 🧠 👉 Enroll now & get an EARLY BIRD DISCOUNT! Course link - https://zurl.co/sXWa Great News for Generative AI Package learners of Manifold AI Learning: You've been automatically upgraded to include these practice tests at no extra cost! #NCA-GENL #GenerativeAI #AI #NVIDIA #ExamPrep #MachineLearning

4

🤔 Interesting perspective from Infosys Chairman Nandan Nilekani: "Let Silicon Valley build LLMs; India should lead in AI use cases." At Meta's Build with AI summit, Nilekani suggested that India should focus on practical AI applications rather than competing in the LLM space. But is this the right approach? Key points to consider: • Silicon Valley giants are heavily invested in LLM development • India could focus on: - Synthetic data generation - Small language models - Real-world applications - Building robust data infrastructure The vision is for India to become the global "use case capital" for AI, emphasizing frugal innovation and practical solutions. What do you think? 💭 Should India stick to developing practical AI applications? 💭 Or should we also invest in fundamental AI research and LLM development? 💭 Could focusing solely on use cases limit our AI capabilities in the long run? Let's discuss! Share your thoughts in the comments below. 👇 #AI #DigitalIndia #LLM

30