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Hi everyone, I'm excited to release Chapter 4 of my blog series, *Full Stack ML Course*. I still recall the challenges I faced when training my first transformer model—not only understanding the concepts but also figuring out the implementation. In this chapter, I've expanded on the problem statement introduced in the previous chapter. My aim for this series is to guide readers through the entire ML pipeline using practical examples. This is designed for those working in data science who haven't yet taken their projects from research to deployment. I hope you find it helpful! What you will learn? 1. Learn to use a model from Hugging Face 2. Use Lightning AI studio and pytorch lightning to train the model 3. Use Weights & Biases to monitor the training Blog Link: https://lnkd.in/gN9-xtJ6 Studio Link: https://lnkd.in/grC57ahk #datascience #ml #deeplearning #pytorchlightning #pytorch #transformer

Hello everyone, hope you’re doing well. Where are Machine Learning lovers? Have you ever try the BART(Backward regression trimming) method to select your variables?In my POV, I never, I just found it while training myself. Leave in the comments what you know about it. I put what I learned on it in comments. Take a look and let me know if there is something you can add😉. Have a good day 😁. #BART #machinelearning #modelisation #variableselection

Implementing FLUX Pro image generation in Make wasn't a walk in the park but I did it. Used Replicate, super happy with the result, amazing quality. The model now powers image generation at Smasher with each business plan getting a unique image with prompting handled by GPT-4o mini. And here's a little placeholder image I made for plans that are still being generated. A 'how to implement Flux into Make' tutorial coming Monday.

In the final article of our three-part series on building our first #PredictiveMachineLearning model, discover the ins and outs of model interpretability (with interpretation techniques for advanced beginners💡): | https://bit.ly/3OybNoe |

🔍 Sharing the second post in my "I Learn, You Learn" series! This time, I dive into the Retrieval aspect of Retrieval-Augmented Generation (RAG), a critical component for efficient information retrieval in AI systems. 📚 Basics Of RAG (Retrieval Augmented Generation) — RETRIEVAL In this article, I explore how retrieval is powered by similarity search and the tools that make it happen:   1. Retrieval Powered via Similarity Search a. Similarity Search: Finding documents similar to a query by comparing their numerical vectors (embeddings) using methods like KNN. b. Cosine Similarity: Measuring the cosine of the angle between two vectors to determine their similarity. Higher cosine similarity means greater similarity. Think of it like searching for a book in a library by describing its content. The system finds books with similar content by comparing their numerical representations.   2. Vectorstores Implement This for You a. Storage: Specialized databases that store embeddings of all documents. b. Efficient Retrieval: Optimized for quickly retrieving the most similar vectors to a given query vector. It's like having a digital library where each book has a unique code, and the library can quickly find books with similar codes.   3. LangChain Has Many Integrations to Support This a. Integrations: LangChain offers integrations with various vectorstores and tools to support retrieval-augmented generation. b. Plug-and-Play: Allows developers to easily plug in different components, such as vectorstores or embedding models, without building everything from scratch. Think of LangChain as a toolkit that helps you connect various parts of your AI system, like linking your digital library to different search engines and machine learning models for a seamless retrieval and generation system.   Check out the full article here: https://lnkd.in/g2-rjwjF Stay tuned for more "I Learn, You Learn" series articles. Let's continue this learning journey together!   #AI #MachineLearning #RAG #ArtificialIntelligence #Indexing #DataScience #TechInnovation #MediumArticle #ILearnYouLearn #Retrieval #LangChain

Hey ML experts, is there an easy to use image to embedding generator that creates the vectors based on shape, color, background color similarity only ? I'm planning to redo a college project using ML. Please comment with a pointer. Thanks 🙂

💥💥💥 Is o1-preview reasoning? Dr. Tim Scarfe and Dr. Keith Duggar discuss OpenAI's new models and their capabilities. They critically analyse claims about AI reasoning, explore the limitations of current language models, and debate the nature of intelligence and computation. Throughout, they emphasize the importance of human oversight in AI applications and discuss potential future developments in the field. TOC: 00:00:00 1. Introduction and AI hype cycles 00:02:09 2. Computational limits of AI systems 00:03:57 3. Neural Networks vs. Turing Machines 00:11:55 4. Computational models in AI 00:13:03 5. What is Reasoning? 00:21:08 6. Chain-of-thought prompting 00:26:02 7. AI code generation and complexity 00:34:24 8. AI assistance vs. human problem-solving 00:35:04 9. Limitations of AI in reasoning and problem-solving 00:46:27 10. Knowledge acquisition and inference in AI 00:53:36 11. Comparing AI and human reasoning capabilities 00:58:58 12. LLMs as cognitive tools 01:00:32 13. Testing o1-preview on a logic puzzle 01:20:48 14. AI-assisted coding: strengths and limitations 👉 https://lnkd.in/dtBs_T37 #machinelearning

The newly published blog, "A guild to #supervisedlearning method for #regression and #classification " introduces one of the traditional #machinelearning algorithms for solving #classification and #regression problems. In the blog, you'll find an in-depth exploration of these topics, accompanied by code examples and clear explanations for each snippet. Additionally, there's a repository with supplementary questions and solutions for further practice. Stay tuned for an upcoming video tutorial on this topic! Follow Ridwan Ibidunni for more insights and tutorials on machine learning concepts.

Solutions for Overfitting in Machine Learning Introduction: Overfitting can be a significant hurdle in model performance. Fortunately, several techniques can help mitigate this issue. Let's explore effective solutions for handling overfitting. Key Concepts: Regularization Techniques: Regularization methods, like L1 (Lasso) and L2 (Ridge), penalize large coefficients, preventing the model from becoming too complex. This encourages a balance between accuracy and simplicity. Cross-Validation: Cross-validation, particularly k-fold cross-validation, provides a robust assessment of a model's performance. It helps detect overfitting by evaluating the model on different subsets of the data. Code Implementation: from sklearn.linear_model import Ridge from sklearn.model_selection import cross_val_score # Example: Ridge regularization ridge_model = Ridge(alpha=1.0) cross_val_scores = cross_val_score(ridge_model, X, y, cv=5) average_cross_val_score = np.mean(cross_val_scores) Practical Insight: In scenarios like image recognition, where the model must generalize well to various images, regularization techniques and cross-validation play a vital role in preventing overfitting. Stay tuned for posts on the cost function with regularization and regularized linear regression. Shubham Bansal & sahil yadav #MachineLearning #Overfitting #Regularization #CrossValidation #ModelPerformance #MLTechniques #DataScienceInsights #ModelEvaluation #TechInnovation #DataAnalysis

Check this out - #algorithm #graph Dynamically generating the layout of a skill tree: How would I go about automatically generating the layout of a skill tree? Skill tree nodes have at least one parent, but they can have more. Ideally I'd have a root node that recursively creates its branches. I've attached a basic sketch of the desired result (the white node is supposed to be the root node). Thanks in advance. - and consider kaedim3d.com for AI that turns images into 3d models in minutes