2026.03.20
Representing Reality: From Raw Data to Embeddings
Unlock the secret behind multimodal AI: learn how raw text, image, audio, and video data are transformed into powerful numerical embeddings for AI …
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2026.01.17
Chapter 5: Model Training, Evaluation & Hyperparameter Tuning
Learn how to train, evaluate, and fine-tune machine learning models using PyTorch and TensorFlow Keras.
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Integrating with ML Frameworks (PyTorch/TensorFlow)
Learn how to integrate Meta AI's dataset library with PyTorch and TensorFlow for efficient model training.
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Chapter 8: Recurrent Neural Networks (RNNs) for Sequence Data
Learn how to implement RNNs, LSTMs, and GRUs for processing sequential data using PyTorch.
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Chapter 14: Model Training Workflows & Optimization Techniques
Learn the practical aspects of model training workflows and optimization techniques in machine learning.
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Chapter 17: Distributed Training & Scaling Deep Learning
Learn how to scale deep learning models using distributed training with PyTorch.
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Chapter 21: Project: Building a Custom Image Classifier
Learn to build a custom image classifier from scratch using PyTorch and transfer learning techniques.
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Chapter 23: Project: Fine-Tuning an LLM for a Specific Task
Learn how to fine-tune a Large Language Model for a specific task using Parameter-Efficient Fine-Tuning techniques like LoRA.
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UniFace Concepts: Face Biometrics
Embark on a comprehensive journey to master advanced face biometrics using UniFace concepts, from foundational principles to real-world applications …
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AI/ML Engineering: A Zero-to-Advanced Career Path
A comprehensive guide for aspiring AI/ML engineers, covering foundational concepts to advanced practical applications.
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LLM Quantization: Making Models Lean for Local Deployment
A comprehensive guide to Large Language Model (LLM) quantization, covering its principles, various techniques (4-bit, 8-bit, GGUF), practical …
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Mastering Deep Learning with PyTorch: From Tensors to Advanced Neural Networks for LLMs
A comprehensive guide to Deep Learning with PyTorch, covering foundational tensor operations, neural network construction, custom training, and …
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Mastering LLM Fine-tuning: Pre-training, SFT, and PEFT for Custom Models
A comprehensive guide to Large Language Model (LLM) pre-training and fine-tuning concepts, covering Supervised Fine-tuning (SFT), Parameter-Efficient …
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NLP Fundamentals: Mastering Attention and Transformers for Large Language Models
A comprehensive guide to Natural Language Processing fundamentals, including text preprocessing, word embeddings, and an in-depth exploration of …
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