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🚀 Understanding Gradient Descent: Batch vs. Stochastic 🚀 When training machine learning models, optimizing the cost function is key, two popular methods to achieve this are: 🔹 Batch Gradient Descent: - Updates model parameters using the entire dataset. - Computes gradient for all training examples before making single update. - Pros: Stable convergence, smooth updates. - Cons: Can be slow and computationally expensive for large datasets. 🔹 Stochastic Gradient Descent (SGD): - Updates parameters for each individual training example. - Makes frequent updates, leading to faster convergence. - Pros: Efficient for large datasets, can escape local minima. - Cons: Noisy updates, may oscillate around the minimum. Both methods have their strengths and are suited for different scenarios. Understanding when to use each can significantly impact the performance of your models. #MachineLearning #DataScience #GradientDescent #AI #Optimization #LinkedInLearning

An epoch in machine learning represents one complete cycle of training data through a learning algorithm. It involves both forward and backward passes and allows the model to update its internal parameters. The number of epochs, a crucial hyperparameter, defines how many times the dataset cycles through the algorithm. Typically, machine learning models require hundreds or thousands of epochs to minimize errors effectively. Monitoring the learning curve, which plots epochs against model performance, helps determine if the model is underfit, overfit, or just right. Join our inner circle to dive deeper into machine learning concepts. Click the link in our bio to join us. #Tekdlin #MachineLearning #AI #DataScience #TechEducation #JoinTheWaitlist

How to Generate Synthetic Dataset for RAG? 🧠 Retrieval-augmented generation enhances the reliability and accuracy of generative AI by combining LLMs with external data sources. Learn how to generate synthetic datasets for RAG, a crucial step for evaluating and optimizing RAG models. Understand the benefits of synthetic data, such as improving contextual relevance, reducing hallucinations, and increasing the scalability and cost-efficiency of RAG systems. 𝐋𝐞𝐚𝐫𝐧 𝐌𝐨𝐫𝐞 👉 https://lnkd.in/ecKXrdBf #RAG #SyntheticData #MachineLearning #AI #LLMs #DataScience #TechInnovation #MLTraining #AIResearch #GenerativeAI

🌟 Day 23: Learning about Outlier Detection with Percentiles! 🌟 Today, I dove into the percentile method for spotting outliers. It’s super simple! Percentiles split data into parts—like the 1st or 99th percentile. If a data point falls below the 1st percentile or above the 99th, it could be an outlier. This helps to clean the dataset and improve the accuracy of machine learning models! 💡 Percentile method = Quick + Effective! 🚀 #DataScience #OutlierDetection #MachineLearning #Percentiles #LearningJourney #AI

Why we should read AI Research Papers? In today's fast-paced AI landscape, reading research papers is crucial for staying updated with the latest advancements. The rapid evolution of AI, driven by breakthroughs like GPT-4 and its competitors, underscores the need to understand the underlying research behind these developments. By reading research papers, individuals can gain insights into the latest techniques, models, and evaluation methodologies, enabling them to make informed decisions about which foundational models to utilize and how to effectively apply them in real-world applications. Additionally, #research papers often introduce new datasets and benchmarks, which are essential for evaluating the performance of AI models and ensuring their reliability. In the Video below from DeepLearning.AI, Presenter's Intorduce different papers in the AI/ML field and their use cases. Here are some of the key points from the video: - Reading research papers can be challenging, but it's essential for understanding the field and its advancements. - Different types of research papers exist, each serving a distinct purpose. - Survey papers provide a broad overview of a specific research area. - Benchmark papers establish evaluation standards for AI models. - Breakthrough papers introduce novel ideas and techniques that push the boundaries of the field. - Critical thinking is vital when evaluating research findings. - Hands-on experience with #AI tools and libraries can complement #paper reading. - Practice and persistence are key to developing the ability to read and understand research papers effectively. I hope this summary is helpful for you. You can find the mentioned video in the link below: https://lnkd.in/df4mivS2

"Just completed an insightful workshop on AI tools with BE10X! Excited to enhance my skills in artificial intelligence and apply them to real-world projects. #AI #Workshop #Certificate #BE10X"

Excited to announce that I’ve completed an Artificial Intelligence course! 🤖💡 This course has strengthened my understanding of AI principles, algorithms, and applications, equipping me to build smarter, data-driven solutions. I’m eager to apply this knowledge in future AI-driven projects and innovations! 🚀#ArtificialIntelligence #AI #MachineLearning #TechInnovation #AIAlgorithms #DataScience #AIInAction #CareerGrowth #SmartSolutions

🚀 Model Evaluation Results 🚀 I recently evaluated my machine learning model on a validation dataset, and I'm excited to share the results! Validation Loss: 0.0946 Validation Accuracy: 96.86% These metrics indicate that the model is performing well, achieving high accuracy and low loss. This suggests it can effectively classify the target classes while generalizing to unseen data. I'm looking forward to exploring further improvements and applications for this model! #MachineLearning #DataScience #AI #ModelEvaluation #DeepLearning

Think optimization is useless? Think again.... Discover in this article how mathematical optimization can outperform machine learning, AI, and data analytics. With rigorous mathematical models, optimization guarantees the most efficient and reliable results. Don't agree? Let's talk! A special thank you to Justine Broihan for putting together this outstanding article and simplifying the optimization concept. https://lnkd.in/gFzQuSBF #Optimization #AI #MachineLearning #DataAnalytics #BusinessEfficiency #Innovation #TechTalk #Mathematics #BusinessOptimization #ProcessImprovement #GAMS #OperationalExcellence