Luis García Rodríguez

Deep Learning Concepts You Need to Know! 🤖✨ Unlock the key building blocks of AI—neural networks, transformers, GANs, and more. Stay informed, stay ahead! 🚀 💥 Bonus: We’re offering a FREE Course on Deep Learning to help you master these concepts! 🔗 https://lnkd.in/g8Td6m9b #AnalyticsVidhya #DeepLearning #FreeCourse

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Automated Multimodal Machine Learning with Large Language Models 📌 This paper introduces AutoM3L, an Automated Multimodal Machine Learning framework that leverages Large Language Models (LLMs) to streamline the construction and optimization of machine learning pipelines for multimodal data. The framework addresses the complexities and manual effort involved in handling diverse data modalities, offering a more automated and user-friendly solution. Article link: https://lnkd.in/gsxSKQKK 🔹 The primary problem is the complexity and manual effort involved in building machine learning models for multimodal data, which integrates various data types such as text, images, and tabular data. Traditional AutoML frameworks are not fully equipped to handle this complexity, often requiring extensive manual feature engineering and hyperparameter tuning. 🔹 AutoM3L introduces an innovative framework that utilizes LLMs as controllers to automatically construct and optimize multimodal machine learning pipelines. This framework simplifies user engagement by eliminating the need for manual feature engineering and hyperparameter optimization. The significant contribution of AutoM3L lies in its ability to provide a more automated, interactive, and customizable solution compared to existing rule-based AutoML approaches. 🔹 The evaluation of AutoM3L on six diverse multimodal datasets demonstrates its superior performance over traditional rule-based AutoML methods. It achieves competitive or better results in classification, regression, and retrieval tasks. Additionally, a user study highlights the framework's enhanced usability and user-friendliness. 🔹 The paper concludes that AutoM3L marks a significant advancement in automated multimodal machine learning, offering a scalable and adaptable solution. Future challenges include extending the framework to support a wider range of data modalities, such as graph, audio, and point clouds, to further enhance its applicability and performance. 📑 Luo, D., Feng, C., Nong, Y., Shen, Y. (2024). AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language Models. DOI: 10.48550/arXiv.2408.00665

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5 Free Resources for Understanding Neural Networks https://zurl.co/4WtL

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ML Interview Question 26: 𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐒𝐞𝐥𝐟-𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐌𝐚𝐬𝐤𝐞𝐝 𝐒𝐞𝐥𝐟-𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐚𝐬 𝐮𝐬𝐞𝐝 𝐢𝐧 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 Answer link: https://lnkd.in/gyuSZcMt Attention mechanisms are a core component in modern deep learning architectures, particularly in sequence-to-sequence tasks and natural language processing (NLP) models like Transformers. Attention allows the model to weigh the importance of different parts of an input sequence when processing each element, which is essential for capturing long-range dependencies. Attention mechanism has been a true game changer in the ML world. This is a very important interview question for Machine Learning today. Let's go through this article to uncover the following: 👉 Different types of Attention mechanisms 👉 Step-by-Step explanation of the basic components of Self-Attention 👉 Why Self-Attention works? 👉 Pytorch implementation of Self-Attention - 30 lines of code 📺  Some amazing video explanations on the topic -- 🔗 If you are preparing for Machine Learning interviews, join us at AIML.com 🔗 Link to Top 100 ML Interview Questions: https://lnkd.in/gJrWZ2Dk 🌐 𝑨𝑰𝑴𝑳.𝒄𝒐𝒎 𝒊𝒔 𝒕𝒉𝒆 𝒘𝒐𝒓𝒍𝒅'𝒔 𝒍𝒂𝒓𝒈𝒆𝒔𝒕 𝒓𝒆𝒑𝒐𝒔𝒊𝒕𝒐𝒓𝒚 𝒐𝒇 𝑴𝑳 𝒊𝒏𝒕𝒆𝒓𝒗𝒊𝒆𝒘 𝒒𝒖𝒆𝒔𝒕𝒊𝒐𝒏𝒔 𝒂𝒏𝒅 𝒒𝒖𝒊𝒛𝒛𝒆𝒔. (𝑨𝒍𝒍 𝑭𝑹𝑬𝑬) #aiml_com #machinelearning #deeplearning #machinelearninginterview

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📝 In natural language processing (NLP), Tokenization is one of the simplest fundamental steps in text preprocessing. 🔠 Discover how to effortlessly preprocess and tokenize your text data using PyTorch's cutting-edge tokenizer. Follow this free guide here: https://lnkd.in/dJfzh6xT #pytorch #tokenization #nlp #textanalysis #datascience #learnwithme #deeplearning #llms #machinelearning

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5 Free Resources for Understanding Neural Networks https://zurl.co/TP6F

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Integrating Large Language Models with Graph Machine Learning: A Comprehensive Review Quick read: https://lnkd.in/gEuAfS8e Paper: https://lnkd.in/gsdVWcM8 #datascience #machinelearning #ai

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🚀Research Paper Highlights: "On Speeding Up Language Model Evaluation" by Jin Peng Zhou et al. 🚀This paper explores methods to enhance the efficiency of evaluating large language models (LLMs) by combining multi-armed bandit algorithms with low-rank factorization techniques. This approach addresses the high costs and resource demands of traditional evaluation methods. 🚀Methods and Techniques 🔗Multi-Armed Bandit Framework: Balances exploration and exploitation to select model-example pairs for efficient evaluation. 🔗Low-Rank Factorisation: Reduces complexity by approximating the evaluation matrix with lower-rank representations. 🚀Key Algorithms: 🔗UCB-E: Uses UCB1 to dynamically allocate resources for evaluating methods. 🔗UCB-E-LRF: Extends UCB-E with Low-Rank Factorization for more efficient evaluations. 🚀Evaluation The proposed techniques were tested against standard evaluation methods: 🔗Efficiency: The multi-armed bandit framework combined with low-rank factorization reduced the resource requirements for LLM evaluation by 85-95%. 🔗Effectiveness: Successfully identified top-performing LLM configurations with significantly fewer resources compared to exhaustive evaluation methods. 🚀Results and Comparisons 🔗Resource Reduction: Achieved a 85-95% reduction in the computational and financial costs of LLM evaluation. 🔗Performance Improvement: The methods identified high-performing models with only 5-15% of the typical evaluation resources. 🚀Limitations Generalization: Further research is needed to refine these techniques for broader application and more diverse evaluation scenarios. Further Reading: https://lnkd.in/dMHHrmm7 Stay tuned for more updates on innovative methods and advancements in language model evaluation! #generativeai #innovation #research

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⚙️ Optimizers in Deep Learning ⚙️ Optimizers are algorithms that adjust the weights and biases of a neural network to minimize the loss function, enabling the model to learn. Here's a detailed overview of common and specialized optimizers: 1️⃣ SGD (Stochastic Gradient Descent): Description: Updates weights using the gradient of the loss function for each training sample. Advantages: Simple and effective for many tasks, especially with large datasets. Limitations: Can be slow to converge, might get stuck in local minima. Use Case: Suitable for simpler, well-behaved loss landscapes. 2️⃣ Momentum: Description: Improves upon SGD by adding a fraction of the previous update to the current update. Advantages: Helps accelerate SGD in the relevant direction and dampens oscillations. Use Case: Useful in scenarios with high gradient variance. 3️⃣ Nesterov Accelerated Gradient (NAG): Description: A variant of momentum that looks ahead by applying the gradient correction based on the anticipated future position. Advantages: Provides a more accurate update direction, leading to faster convergence. Use Case: When faster convergence and reduced oscillation are needed. 4️⃣ Adagrad (Adaptive Gradient Algorithm): Description: Adapts the learning rate for each parameter based on its history of gradients. Advantages: Automatically adjusts learning rates, beneficial for sparse data. Limitations: Learning rate can become very small over time. Use Case: Ideal for dealing with sparse data and NLP tasks. 5️⃣ RMSprop: Description: Modifies Adagrad to maintain a moving average of squared gradients. Advantages: Adjusts learning rates dynamically, preventing the decay problem in Adagrad. Use Case: Particularly good for recurrent neural networks and online learning scenarios. 6️⃣ Adam (Adaptive Moment Estimation): Description: Combines the benefits of Adagrad and RMSprop, using both moving averages of the gradient and its square. Advantages: Efficient and effective, handles sparse gradients, and performs well in practice. Use Case: Widely used in various deep learning applications due to its robustness. 7️⃣ AdaDelta: Description: An extension of Adagrad that seeks to solve its diminishing learning rate problem. Advantages: Keeps the benefits of adaptive learning rates without accumulating past gradients. Use Case: Suitable for speech and image recognition tasks. 8️⃣ Adamax: Description: A variant of Adam based on the infinity norm, providing more stability. Advantages: Better suited for very large gradients. Use Case: Used in similar scenarios as Adam but offers more stability. 9️⃣ Nadam (Nesterov-accelerated Adaptive Moment Estimation): Description: Integrates Nesterov momentum into the Adam optimizer. Advantages: Combines fast convergence of NAG with the adaptive learning rate of Adam. Use Case: Preferred when training deep neural networks requiring fast convergence. #Optimizers #DeepLearning #AI #MachineLearning #EncephAI

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Recent Data Science Learning Community (DSLC.io) club meetings: 🟢 Practical Deep Learning for Coders: 17 "Initialization/normalization" https://lnkd.in/gb588uHr #PyData #DeepLearning #AI 🔵 ggplot2: Elegant Graphics for Data Analysis: Chapter 16 "Faceting" https://lnkd.in/g-jZFVPh #RStats #DataViz #ggplot2 From the aRchives: 🔵 "Bayes Rules! (Normal) Hierarchical Models with Predictors (bayes_rules01 17)" https://lnkd.in/ghzY2ZaD #RStats 🔵 "rlang docs: Operators, Functions, Attributes, S3, and Weak references (rlang01)" https://lnkd.in/g5Ak-AzX #RStats Subscribe at http://DSLC.video for hours of #DataScience videos every week!

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🚀 Decision Tree algorithm is one of the most powerful algorithms in Machine Learning and forms the basis for advanced Ensemble Learning models: https://lnkd.in/gRZ4bRC8 #analyticsvidhya #datascience #machinelearning

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A Comprehensive Review of Survey on Efficient Multimodal Large Language Models Quick read: https://lnkd.in/gV4_7ffR Paper: https://lnkd.in/gb_BYJfx #ai

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Dive into the world of Generative AI and discover how it’s reshaping the landscape of technology in this article by our co-CEO Matias Reina. From Large Language Models to AI tools, this Generative AI for Dummies article offers a comprehensive guide for anyone looking to understand the basics of generative AI to adopt this kind of technology in their business. 👇👇 https://lnkd.in/eFQ4TBzE #AI #GenAI

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New Data Curation Method for Self-Supervised Learning - Automatic Data Curation: Meta, Google, and partners introduce a technique using embedding models and clustering algorithms to curate balanced datasets without manual annotation. - Balanced SSL Datasets: Ensures diverse and balanced datasets by rebalancing less frequent concepts to avoid bias and improve model generalization. - Significant Performance Boost: Models trained on automatically curated datasets perform nearly as well as those on manually curated ones, enhancing efficiency and reducing costs. Subscribe to our daily newsletter here for more AI news https://lnkd.in/deGceMQG #Innovation #Technology #ArtificialIntelligence #BigData #Data #Analytics #Entrepreneurship #Futurism #DataScience #SoftwareEngineering #AI

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