Ricardo Galante’s Post

New Post: Five Machine Learning Types to Know

Run a machine learning algorithm is to find the best set of weights corresponding to each feature and the bias. 😅

The success of a machine learning technique depends to a large extent on how well it can perform its task and if it has a meaningful embedding in the overall system. Learn the top uses and more in this blog post. https://lnkd.in/daRsMEbV

TIME Framework: A Novel Machine Learning Unifying Framework Breaking Down Temporal Model Merging

🚀 Kickstart or Refresh your AI skills with this fantastic resource! Machine Learning University (MLU) from Amazon offers a fantastic resource for anyone who wants to learn machine learning theory and practical application. 🌟 MLU-Explain is an incredible initiative that breaks down complex AI concepts through visual essays—making it fun, informative, and accessible to all learners! 💡📊 If you're looking for a great starting point or just want to brush up on your knowledge, this is definitely worth checking out. 🎓 https://lnkd.in/gmxPjKSP #MachineLearning #AI #Learning #MLU #ArtificialIntelligence #TechLearning #VisualLearning #Education

Learn how the perceived conflict between accurate predictions and interpretability is misleading and how both objectives can be aligned in Bruce Desmarais latest blog post, "In Machine Learning, Can Good Predictive Models also be Interpretable?" https://bit.ly/4dbqFE3

Mastering Hyperparameter Tuning for Optimized Machine Learning Models Hyperparameter tuning is the secret sauce that transforms a good machine learning model into a great one. By fine-tuning parameters like learning rate, tree depth, or number of layers, you can maximize performance and accuracy. Key Highlights from the Article: 1. What is Hyperparameter Tuning? A method to optimize non-learnable parameters in a machine learning model. Impacts training speed, convergence, and overall accuracy. 2. Techniques for Tuning: Grid Search: Systematic exploration of parameter combinations. Random Search: Random sampling of hyperparameters for efficiency. Bayesian Optimization: Intelligent exploration for fewer iterations. 3. Practical Steps with Code: Learn how to implement tuning using libraries like Scikit-learn, TensorFlow, or PyTorch. Understand real-world examples of hyperparameter tuning in action. 4. Challenges: Time-consuming process for large datasets. Risk of overfitting when tuning excessively. https://lnkd.in/gkBMQ4vc Additional Resources: Tools for Automation: Optuna, Ray Tune https://lnkd.in/gExMuTnF Code Examples: Explore hyperparameter optimization on GitHub. https://lnkd.in/g3n_SQbp #MachineLearning #HyperparameterTuning #AI #DataScience #MLModels #OptimizationTips

Machine Learning Fundamentals : A Dive into the Types of Machine Learning