How LLMs transform tabular data with model.fit()

Machine Learning (ML) algorithms are computational methods that enable systems to learn patterns from data and make predictions or decisions without being explicitly programmed. They can be used for tasks like classification, regression, clustering, and dimensionality reduction, adapting their behavior based on input data.

Machine Learning: Preprocessing o Handling NA values, outlier treatment, data normalization o One hot encoding, label encoding o Feature engineering o Train test split o Cross validation • Machine Learning: Model Building o Types of ML: Supervised, Unsupervised o Supervised: Regression vs Classification o Linear models ▪ Linear regression, logistic regression ▪ Gradient descent o Nonlinear models (tree-based models) ▪ Decision tree ▪ Random forest ▪ XGBoost o Model evaluation ▪ Regression: Mean Squared Error, Mean Absolute Error, MAPE ▪ Classification: Accuracy, Precision-Recall, F1 Score, ROC Curve, Confusion matrix o Hyperparameter tunning: GridSearchCV, RandomSearchCV codebasics.io 9 o Unsupervised: K means, Hierarchical clustering, Dimensionality reduction (PCA)

Considering the price ratio (1 : 14.3) and the TDP ratio (estimated in 1 : 20) for M1 max vs workstations with H100, these results seem promising and make me believe I will see ubiquitous robotics in my life span.

How does MLX on Metal perform in handling machine learning tasks? Yi Wang and I conducted a set of benchmarks using M1 Max, M2 Ultra with MLX, A100, and H100 with PyTorch to compare the performance of two fundamental operations, SDPA and Linear Projection. A surprising revelation is the close performance between the M2 Ultra and A100, underscoring the impressive potential of on-device machine learning. The benchmark also reveals distinct performance trends. Linear Projection shows a linear increase in latency with larger input sizes, while SDPA exhibits exponential latency growth due to its higher complexity. Interestingly, the performance disparity in SDPA is much less pronounced than in Linear Projection. For instance, Linear Projection demonstrates a nearly 100x performance difference between the M1 Max and H100, whereas SDPA shows only 25x difference on the same set of hardwares. These findings highlight the significant potential of on-device machine learning, and we look forward to further enhancements in performance, particularly with advancements in Metal.

Diagnosing Overfitting in Machine Learning Models: Early Signs and Prevention 💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇 👉 https://lnkd.in/dFWhtGjU Machine learning models can sometimes perform exceptionally well on training data but fail to generalize to new, unseen data. This condition is known as overfitting. In this discussion, we dive into the causes of overfitting and explore early warning signs and preventive measures. Overfitting occurs when a model learns the training data too well, including the noise in the data. This leads to poor performance in real-world scenarios. Some common reasons for overfitting include: 1. High complexity models in relation to the amount of available training data 2. Insufficient data for adequate model training and generalization 3. Dominant noise in the training data that the model captures instead of the underlying patterns Detecting overfitting early can save time and resources. Some early signs of this condition include: 1. High training accuracy: When the model performs significantly better on the training data than on the validation or test data. 2. Low validation accuracy: The model performs poorly on the validation data while doing well on the training data. 3. Variable importance: Significant differences between feature importance in training and validation sets. Preventing overfitting involves: 1. Reducing model complexity: Using simpler models or regularization techniques to reduce overfitting. 2. Data augmentation: Increasing the available data to cover more variations. 3. Dropout: Implementing data masking during training to randomize feature availability, keeping the model from relying too heavily on any one feature. 4. Early stopping: Allowing the model to be under-fit for the sake of providing proper generalization to new data. Throughout the machine learning development process, keep monitoring performance measures closely to detect and address overfitting proactively. Additional Resources: 1. [Understanding theml.ai - Overfitting](https://lnkd.in/d4cWum8g) 2. [Stanford CS221: Convolutional Neural Networks for Visual Recognition](https://lnkd.in/d93VzpVi) #STEM #Programming #Technology #Tutorial #diagnosing #overfitting #machine #learning #models #early #signs #prevention Find this and all other slideshows for free on our website: https://lnkd.in/dFWhtGjU #STEM #Programming #Technology #Tutorial #diagnosing #overfitting #machine #learning #models #early #signs #prevention https://lnkd.in/d2rQkfQv

🚀 **Understanding the Time Complexity of Popular ML Algorithms** 📊 As machine learning continues to evolve, it's crucial for practitioners to grasp the efficiency of various algorithms. Here's a concise overview of the time complexity for 10 popular ML algorithms, which can guide our choices based on the size and complexity of our datasets. 🔍 **Key Takeaways:** 1. **Linear Regression**: Efficient for small datasets with a complexity of O(nm² + m³) for training, and O(nm) for inference. 2. **Logistic Regression**: Similar efficiency with O(nm) for both training and inference. 3. **Decision Trees**: A versatile choice, but beware of overfitting—complexity can vary. 4. **Support Vector Machines (SVMs)**: Powerful for classification tasks, but can become computationally expensive with larger datasets. 5. **K-Means Clustering**: A go-to for unsupervised learning, but its complexity can vary based on iterations and clusters. Understanding these complexities helps us make informed decisions, optimizing our models for performance and scalability. 💡 #TimeComplexity #Algorithms #LakkiData #LearningSteps

A project on FEATURE ENCODING The project explains the use case of the following types of Encoders : 1. 'OneHotEncoder' 2. 'LabelEncoder' 3. 'OrdinalEncoder' 4. 'BinaryEncoder' 5. 'CountEncoder' Feature encoding in machine learning refers to the process of transforming categorical data into a numerical format that can be used by machine learning algorithms. Most algorithms require numerical input, so categorical variables (such as labels or text) need to be encoded into numbers to be used effectively.

Read our latest community article on reusing and optimizing machine learning models in .NET. Enhance your ML projects with these practical insights🌟: https://lnkd.in/g5DsfJbt #MachineLearning #dotNET #DataScience #ABPCommunity