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Excellent job! Congratulations!!!

Chegg is worst!!Their management is worst!!Their AI cant compete with gpt!!Their experts use chat gpt to give answer to students!!(You can confirm the same by looking throught trustpilot reviews). Chegg india support team is sleeping, i work as chegg expert who give solution to students. Colleague experts use chat gpt to answer questions,chegg quality team has given the solution reviewing work to experts only!!So experts are revewing their own solutions. Experts take account from other people and use gpt to reciew even solutioms. Chegg quality team Is doing no work,they are not even checking what sort of useless gpt answers being thrown to the students. We habe raised multiple tickets to stop all this,but support team ,doesnt do anything other thsn sending automated reply. Chegg has 2 way authentication only,they dont use otp for student subscription through credit card. Multiple people do carding fraud and chegg has to refund full amoint,chegg has lost million of amoint owing to this,many more things are there which are being doen via scams

Hey there Anna and Lovedeep... I would love to get guidance from you as to how to start a career in data science, followed by GenAI.

Nice introduction, any more details about tehcnical aspects of that case, instructions, tips on how to implement this kind of solution?

🎯 Key Takeaways for quick navigation: 00:31 *🔄 Gretel AI specializes in creating synthetic data for generative AI programs within enterprises, aiming to aggregate different use cases and implementation strategies.* 02:06 *📊 Synthetic data mirrors real-world data's statistical properties, enabling simulation of real-world situations without needing original data, covering various data modalities like tabular, natural language, and time series.* 03:02 *🚀 Gretel's platform allows for flexible deployment, offering cloud and hybrid models to experiment quickly or deploy at scale within an enterprise's environment.* 03:46 *📈 Use cases include creating synthetic versions of machine learning training datasets to safely share across the enterprise, enhancing data privacy and fidelity.* 05:24 *⏳ Time series synthetic data fills in gaps for expensive-to-collect physical sensor data, supporting simulations and augmenting sparse datasets.* 06:47 *🗣️ Natural language synthetic data supports internal GPT model development, enabling training for bots and detecting toxic language, spam, and fraud.* 07:14 *🖼️ Synthetic images generated through stable diffusion techniques can create costly or hard-to-acquire images for ML training, like in insurance or medtech.* 08:12 *🛠️ Example workflows illustrate how synthetic data can address ML challenges, such as imbalances in training data, by generating additional data samples.* 11:06 *🌐 For enterprises new to ML, synthetic data allows exploration of production databases for ML training set creation, overcoming privacy and access barriers.* 14:21 *📝 Deployment considerations for synthetic data solutions range from open source with various licenses to vendor-hosted or hybrid cloud models, highlighting the importance of understanding license restrictions and deployment needs.* Made with HARPA AI

Incredible conversation! Thanks, folks

i liked the way she explained everything - very clear and well-spoken

Marvelous video to learn the big data analysis!!!

great and informative

Excellent! Well presented. Thanks for sharing.

Explained very well. Im a BIG COM SCI GUY

*dark pools and naked shorts have entered the chat* *supply & demand economics has left the chat*

ጸ😊?ጻ❤🎉

🎯 Key Takeaways for quick navigation: 00:02 🎙️ *Introduction and Background* - Erin Stanton's introduction and her role in trading analytics. - Historical evolution of trading analytics from Excel to machine learning. 01:15 📊 *Concepts in Trading Analytics* - Explanation of Andre Pearl's concept of implementation shortfall. - The impact of time delays on trading outcomes. - The influence of supply and demand in economics on stock prices. 04:00 📈 *Challenges in Trading Analytics* - Challenges faced in trading analytics: lack of trust in models and data, data availability, skill set, and access to technical resources. - The importance of starting with a small, well-defined goal in machine learning projects. 07:44 🛠️ *Tackling Challenges in Machine Learning* - Approach to machine learning: starting with small, well-contained problems and involving stakeholders early. - The importance of transparency in model development, including data, features, and algorithms. - The value of building a community and sharing knowledge within the organization. 10:44 🔍 *Feature Selection and Transparency* - A detailed explanation of feature selection and its importance. - Examples of categorical, numerical, and excluded features in a model. - The role of transparency in feature selection and model building. 14:25 🌲 *Random Forest Feature Importance* - The use of Random Forest feature importance in explaining model outcomes. - How feature importance aids in building trust with traders. - The presentation of feature importance to make it understandable to non-ML practitioners. 17:44 🔄 *Simplifying Model Complexity* - Challenges faced in building a complex, all-encompassing model for trading analytics. - The decision to focus on a specific type of trade (algo wheel) and why it simplifies model development. - Explanations of data quality calls and the exclusion of certain data points. 21:53 📊 *Ongoing Model Development* - A continuation of the feature selection process for a more complex model. - The iterative nature of machine learning model development. - Transparency and openness in the model-building process. 23:02 📊 *Model Development and Transparency* - Model development process and transparency with clients. - Peer review process for model evaluation and challenging assumptions. - Exclusion of features based on endogeneity and visual model representation. 25:00 🧩 *Semi-Supervised Learning and Reports* - Use of semi-supervised learning for populating partially tagged data. - Splitting commission rate reports into execution and research components. - Successful outcomes with semi-supervised learning and execution-only reports. 26:29 🤖 *Trading Strategy Recommendation Engine* - Ongoing efforts to develop a versatile trading strategy recommendation engine. - Learnings from previous failures and breakthroughs in the last 18 months. - Expanding machine learning applications to various asset classes beyond stocks. 27:52 🐦 *Twitter Sentiment Analysis Experiment* - Experimentation with Twitter sentiment analysis for stock trading insights. - Challenges encountered in processing visceral language on Twitter. - Classifying this experiment as part of the "failure column." 28:34 🚀 *Confidence in ML Adoption in Trading Analytics* - Anticipation of 2023 as the year for ML adoption in trading analytics. - Building transparent models and addressing data set gaps. - Gaining trust, enhancing skills, and preparing for ML adoption in trading analytics. Made with HARPA AI

nice talk.

Can you share the code, please? tyty

fantastic talk ty!

I really enjoyed this presentation, especially the way that you got very granular with the case studies. Thank you!

Thanks for this, I am still a beginner in the field.

what is the type of optimizer used , the audio is not very clear

Is it possible to get the python code and Tableau file to implement the same in my usecase

The Company faces tremendous losses ~97% since last year and is dues to Remote work so this is normal.I am not surprised. But the returns are less than more than 3 y ago. Does this new R&D ML architecture results in ROI increase, RnD in unreliable in measurement for many reasons of course but is there an estimate?

Very good presentation Jayeeta, thanks!

19:34 Amazing potential of transfer learning🙂

Voice quality is bad

So would profit be an input feature of the bayesian optimiser? As in, you multiply the predicted sales by the gross margin and use that as feature to optimise the price.

This video has nothing about Finance except 1 sentence on 1 slide. I suggest the better title would be "The Machine Learning Blueprint". Nothing new or specific about Finance, just common and general practice for any ML project.

Thank you for this nice representation! What are the use cases of ML in finance?

Great material, great perspective, well presented. Thanks

Your video is very beautiful. I like it a lot. I have been trying to talk to you for a long time. I am waiting for your response. Please give me a chance to talk to you..

Nice presentation. I was also hoping to understand 1. Types of visualisations available 2. Which visualisation works in a particular situation and how to arrive there

Very insightful video, thank you

This is great live session

Thanks for sharing this video; it is helpful

Very nice and informative!

Engaging conversation. Useful insights on sustainability with Blockchain technology and NFT use-cases with digital ownership of physical products.

Can you share the github repo for this algorithm please?