// CATEGORY: DEEP LEARNING

Learn how to use Tunix, a JAX-native library for LLM post-training and specialization.

Learn the essentials of JAX for optimizing and diagnosing LLM training workflows in Tunix.

Learn how to build, compile, and train your first neural network using Keras in TensorFlow.

Explore the critical data fusion strategies—early, late, and hybrid—that enable multimodal AI systems to combine text, image, audio, and video inputs …

Learn how to implement custom training loops and use callbacks in TensorFlow for more control over the training process.

Learn how Tunix, built on JAX and Flax NNX, handles model architectures and state management for effective post-training.

Step-by-step guide to building a CNN for CIFAR-10 image classification using TensorFlow and Keras.

Learn how to implement RNNs, LSTMs, and GRUs for processing sequential data using PyTorch.

Build a character-level text generation model using TensorFlow and LSTM to generate creative text based on Shakespeare's works.

Dive into the critical world of real-time multimodal AI, learning how to optimize systems for speed and low latency across text, image, audio, and …

Learn how to customize Tunix with custom loss functions, optimizers, and callbacks for advanced LLM post-training.

Explore the integration of vision and language in AI, learning about multimodal models and their applications.

Learn the practical aspects of model training workflows and optimization techniques in machine learning.

Learn how to scale deep learning models using distributed training with PyTorch.

Learn how to fine-tune a Large Language Model for a specific task using Parameter-Efficient Fine-Tuning techniques like LoRA.

An in-depth exploration of AI model quantization, bridging theoretical model development with practical application.

A comprehensive guide to building intelligent AI agents using LangChain and LangGraph, covering foundational concepts, tool integration, memory …

An in-depth exploration of Large Language Model architectures, focusing on the Transformer mechanism.

A comprehensive guide to Large Language Model (LLM) quantization, covering its principles, various techniques (4-bit, 8-bit, GGUF), practical …

A comprehensive guide to deploying and serving Large Language Models (LLMs) locally, focusing on Ollama for running pre-packaged models, and …

A comprehensive guide to Deep Learning with PyTorch, covering foundational tensor operations, neural network construction, custom training, and …

A comprehensive guide to Large Language Model (LLM) pre-training and fine-tuning concepts, covering Supervised Fine-tuning (SFT), Parameter-Efficient …

A comprehensive guide to Natural Language Processing fundamentals, including text preprocessing, word embeddings, and an in-depth exploration of …

A comprehensive and practical guide to Retrieval-Augmented Generation (RAG), covering its core components, document loading, chunking, embedding, …