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in Machine Learning
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ApX brings together everything you need to explore and grow in machine learning.
Whether you're starting with the basics or refining advanced skills, it's all here in one place.
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Build and deploy machine learning models with automated workflows. Perfect for tabular data analysis and predictions.
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Comprehensive courses to master Machine Learning and Data Science fundamentals.
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Fine-tune and deploy custom language models for your specific use cases. Launching soon.
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Mar 13, 2025
Learn the recommended GPU specifications for running Google DeepMind's latest Gemma 3 models efficiently, including VRAM requirements for text and image-to-text tasks.
Mar 12, 2025
Learn how to generate videos using Wan2.1, an advanced open-source video generation model. This guide walks you through installation, setup, and running text-to-video generation on consumer and high-end GPUs.
Mar 6, 2025
QwQ-32B is a 32-billion-parameter reasoning model optimized with reinforcement learning, rivaling larger models like DeepSeek-R1. This guide covers installation and running methods, including Hugging Face and an easier alternative using Ollama.
Mar 5, 2025
Comparing NVIDIA GPUs with Apple's macOS Metal GPUs for machine learning workloads. Performance tests include a deep learning rig, MacBook M3 Pro, MacBook Air M1, and Google Colab's free tier.
Mar 5, 2025
Learn how to run PyTorch on a Mac's GPU using Apple’s Metal backend for accelerated deep learning. This guide covers installation, device selection, and running computations on MPS.
Feb 25, 2025
Learn how to integrate and use the Claude 3.7 API, Anthropic’s latest hybrid reasoning AI model. This guide covers authentication, making API requests with cURL, Python, and JavaScript, and key features like extended reasoning and Claude Code.
Feb 23, 2025
As PyTorch continues to gain traction, is TensorFlow still worth learning in 2025? A deep dive into their strengths, industry adoption, and what matters for building a machine learning career.
Feb 11, 2025
Essential strategies to efficiently scale RAG (Retrieval-Augmented Generation) for millions of documents, including vector database selection, indexing methods, reranking approaches, and optimized data ingestion pipelines.