Neo4J GDS: Node Embeddings Algorithms for Data Science and Machine Learning
Explore node embedding algorithms in Neo4J GDS for data science and machine learning, creating powerful graph-based representations.
Neo4J GDS - Topological Link Prediction for Data Science and Machine Learning
Explore topological link prediction algorithms in Neo4J using the Data Science library (GDS) for advanced graph analysis and machine learning applications.
Multimodal Prompts with Gemini Pro 1.5 in Google Vertex AI
Explore multimodal prompts with Gemini Pro 1.5 in Google Vertex AI using Python SDK. Learn to integrate text and visual inputs for advanced machine learning applications.
Fine-Tuning Gemini 1.0 Pro for Text Classification in Vertex AI
Fine-tune Gemini Pro 1.0 for text classification using Google Vertex AI's UI and Python SDK. Learn advanced machine learning techniques for improved model performance.
Optimizing Python Code Speed with Functools and NumPy Vectorize
Boost Python code performance using functools and numpy vectorization techniques for faster execution and improved efficiency.
Optimizing Python Code Speed with Numba JIT Compiler
Accelerate Python code execution using Numba's JIT compiler, leveraging jit and njit decorators for enhanced performance.
Pandas Parallel Transformations with Dask and Pandarallel
Optimize data processing with Dask and Pandarallel for faster Pandas transformations. Learn parallel computing techniques to enhance performance.
MLOps: Fine-tuning Mistral 7B with PEFT, QLora, and MLFlow
Fine-tune Mistral 7B using PEFT, QLora, and MLFlow techniques. Master advanced LLM optimization for improved performance and efficiency.
Review of Latest Multimodal Models: Gemini Pro 1.5 Flash vs OpenAI GPT-4o vs Claude 3 Opus
Explore and compare cutting-edge multimodal AI models: Gemini Pro 1.5 Flash, GPT-4o, and Claude 3 Opus. Gain insights into their capabilities and performance.
ML and MLOps: Databricks Secrets and Integration with Azure KeyVault
Explore Databricks Secrets and Azure KeyVault integration for secure ML & MLOps workflows in data science and machine learning projects.
Installing NVIDIA cuDNN and First Steps with NVCC in C++
Aprenda a instalar Cudnn y desarrolle aplicaciones de ML con soporte GPU en C++, utilizando la biblioteca de redes neuronales profundas de Nvidia.
LLMOps: Inference of Fine-Tuned ViT Classifier on CPU with C#
Perform CPU inference on a fine-tuned Vision Transformer using C# and ONNX format for efficient image classification in machine learning projects.
Microsoft AI: Visual Studio Code AI Toolkit Introduction for Data Science and Machine Learning
Explore Visual Studio Code AI Toolkit with hands-on examples in Python and C#, focusing on CPU inference using the Phi 3 4K Instruct model in ONNX format.
Blackbody Radiation and the Weyl Law in Disordered Media
Explore blackbody radiation in complex media, focusing on path length invariance, Weyl law, and connections to information theory and quantum vacuum forces.
The Long-Run Distribution of Stochastic Gradient Descent: A Large Deviations Approach
Explore the long-run behavior of stochastic gradient descent in non-convex problems, analyzing state space visitation patterns and energy level distributions.
Error Bounds for Mean-Payoff Markov Decision Processes
Explore optimal transport techniques for error bounds in reinforcement learning, focusing on mean-payoff Markov decision processes and stochastic fixed point iterations.
Proximal Gradient Methods for Nonsmooth Nonconvex Minimax - A Unified Convergence Analysis
Explore proximal gradient methods for nonsmooth nonconvex minimax problems, analyzing convergence and complexity through a unified framework for parallel and alternating schemes.
Nonconvex Optimization and the Curse of Local Minima - Lessons Learned from Orbital Tomography
Explore nonconvex optimization algorithms, focusing on quality of fixed points in orbital tomography. Gain insights into evaluating algorithm effectiveness beyond convergence speed.
A New Derivative-Free Method Using an Improved Under-Determined Quadratic Interpolation Model
Explore a novel derivative-free optimization method using improved under-determined quadratic interpolation, considering trust-region iteration properties and model optimality.
Stochastic Algorithms for Constrained Continuous Optimization
Explore stochastic-gradient-based algorithms for constrained continuous optimization, focusing on interior-point and sequential-quadratic-programming methods with convergence guarantees and practical applications.
Hessian-Aware Stochastic Differential Equation Modeling of SGD
Explore a novel SDE model for SGD that incorporates Hessian information, improving accuracy in capturing escaping behaviors and achieving exact recovery for quadratic objectives.