Everything You Need to Learn and Build
in Machine Learning
Learn, create, and deploy powerful machine learning models
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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.
AutoML
Build and deploy machine learning models with automated workflows. Perfect for tabular data analysis and predictions.
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Free comprehensive courses to master Machine Learning and Data Science fundamentals.
Explore LearnMLLangML
Fine-tune and deploy custom language models for your specific use cases. Launching soon.
More InfoBlog
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.
Feb 8, 2025
Complete guide to system requirements for running DeepSeek models on Mac, including both full and quantized models. We provide detailed recommendations on compatible Apple Silicon devices like MacBook Pro, Mac Studio, and iMac.
Feb 7, 2025
Top LLMs for coding in 2025, ranked based on accuracy, integration, cost, and speed. Learn which AI tools work best and why Claude 3.5 Sonnet and OpenAI's models stand out.
Feb 2, 2025
GPU system requirements for Qwen models with detailed guidance on VRAM needs, recommended GPUs for both full and quantized versions, and best practices for efficient AI deployment.
Jan 26, 2025
Comparison of the training costs of two cutting-edge language models, DeepSeek V3 (671B parameters) and Llama 3.1 (405B parameters)
Jan 26, 2025
Hugging Face simplifies machine learning and natural language processing with pre-trained models, tools, and an active community. Learn how it fits into your AI learning journey.
Jan 22, 2025
GPU system requirements to run DeepSeek-R1 and its distilled models effectively, along with recommendations for choosing the right hardware for your needs.
Jan 22, 2025
Learn how to implement the FizzBuzz problem using a deep learning approach with PyTorch. This step-by-step guide highlights the coding process, training, and testing to achieve impressive results.