#deep-learning (43)
- Gemma 4 QAT: Efficient AI for Edge Devices
Master Gemma 4 QAT models for efficient AI on mobile and laptops. Learn QAT from first principles, optimize model compression, and integrate new checkpoints with practical steps and benchmarks.
- Multimodal AI: Integrate Diverse Data for Intelligent Apps
Integrate diverse data types like text, images, audio, and video to build sophisticated multimodal AI systems for intelligent real-world applications.
- Architecting Multimodal Encoders for AI Perception
Understand how to design and implement multimodal encoders, enabling AI systems to process and unify diverse data types such as text, images, and audio.
- Creating Diverse Content with Generative Multimodal AI
Grasp the core principles and architectures of generative multimodal AI to create novel content by integrating text, images, audio, and video inputs.
- Multimodal LLMs: How AI Interprets and Generates Across Modalities
Learn how Multimodal Large Language Models integrate diverse data and extend AI to interpret and generate content across modalities.
- Hands-On Project: Building a Multimodal Search Assistant
Build a practical multimodal search assistant from scratch using Python, CLIP, and FAISS. Learn to index and query text and images in a shared embedding space.
- Build Multimodal RAG Systems with Diverse Data Sources
Learn to implement Multimodal RAG, integrating diverse data types to enhance AI knowledge bases and overcome Large Language Model limitations.
- Optimize Real-Time Multimodal AI for Speed and Latency
Learn to optimize multimodal AI systems for real-time speed and low latency across diverse data types, enabling instant responses in critical applications.
- 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 understanding.
- Understanding Multimodal AI Systems
Explore multimodal AI systems, their architecture, and how they integrate text, image, audio, and video. Discover pipelines and real-world applications like voice assistants and vision AI.
- Understanding Multimodal AI and Combining Data for Perception
You will learn why combining text, image, audio, and video inputs is crucial for creating more intelligent and human-like AI systems.
- UniFace Concepts: Face Biometrics
Embark on a comprehensive journey to master advanced face biometrics using UniFace concepts, from foundational principles to real-world applications and ethical considerations.
- Introduction to Face Biometrics and UniFace Concepts
Dive into the world of face biometrics, understand its core concepts, and begin your journey with the conceptual UniFace toolkit. Learn about face detection, alignment, and recognition fundamentals.
- Setting Up Your Advanced Biometrics Development Environment
Learn how to set up a robust development environment for advanced face biometrics projects, covering Python, deep learning frameworks, and essential libraries.
- Generate Face Embeddings for Accurate Biometric Recognition
Learn to generate and compare face embeddings using deep learning to transform faces into unique numerical representations for accurate recognition.
- Unified Cross-Entropy Loss for Robust Face Recognition
Understand the Unified Cross-Entropy Loss, its crucial role in robust face recognition, and how to conceptually integrate it into deep learning models.
- Build Your First Face Recognition Model in Python
Develop your first face recognition model in Python, understanding face detection, feature extraction, and comparison through hands-on coding.
- Real-time Face Verification and Identification Systems
Dive into building real-time face verification and identification systems. Learn the core principles, architecture, and practical considerations for deploying UniFace-like capabilities.
- Addressing Bias and Fairness in Face Biometrics
Explore the critical concepts of bias and fairness in face biometrics, understand their sources, and learn about practical mitigation strategies to build more ethical and robust AI systems.
- Future Trends and Research in Advanced Face Biometrics
Explore the cutting-edge of face biometrics, delve into emerging research areas, and understand the future implications and ethical considerations of advanced facial recognition technology.
- JAX Essentials for Tunix Users
Learn the essentials of JAX for optimizing and diagnosing LLM training workflows in Tunix.
- RLHF: Principles for Aligning LLMs with Human Values
Reinforcement Learning from Human Feedback concepts are explained, detailing how LLMs are aligned with human values to generate helpful, harmless, and honest text.
- Mastering Tunix: JAX-Native LLM Post-Training Library
Learn to effectively use Tunix, a JAX-native library, for LLM post-training, covering its setup, core features, advanced applications, and best practices.
- How AI Model Quantization Works: Deep Dive into Internals
An in-depth exploration of AI model quantization, bridging theoretical model development with practical application.
- Learn Neural Networks & Deep Learning Core Concepts
Grasp the fundamental concepts of neural networks and deep learning, understanding how individual neurons and layered structures function, with a conceptual Python example.
- AI/ML Engineering: A Step-by-Step Learning Path
Acquire foundational knowledge and advanced practical skills to build a thriving career in AI/ML engineering, covering core concepts and advanced applications.
- The AI/ML Landscape & Foundational Math
Learn the basics of AI/ML and foundational math with practical examples in Python.
- Deep Learning Fundamentals & Neural Networks
Learn the basics of deep learning and neural networks through a step-by-step tutorial.
- Convolutional Neural Networks (CNNs) for Computer Vision
Learn how to build and train a CNN for image classification using TensorFlow and Keras.
- The Transformer Architecture & Attention Mechanisms
Explains the Transformer architecture and attention mechanisms, revolutionizing NLP.
- Embeddings, Vector Databases & Semantic Search
Learn about embeddings, vector databases, and semantic search to build advanced AI applications.
- Multimodal Models: Vision-Language Integration
Explore the integration of vision and language in AI, learning about multimodal models and their applications.
- Model Training Workflows & Optimization Techniques
Learn the practical aspects of model training workflows and optimization techniques in machine learning.
- Inference Optimization & Model Deployment
Learn how to optimize and deploy machine learning models for real-world applications, focusing on latency, throughput, cost, edge deployment, and energy efficiency.
- Hardware Considerations: CPU, GPU, & Accelerators
An in-depth look at the hardware that powers AI models, including CPUs, GPUs, and accelerators.
- Experimentation, Tracking & Debugging Model Behavior
Learn how to systematically test, track, and debug machine learning models with Experimentation, Tracking & Debugging.
- Project: Building a Custom Image Classifier
Learn to build a custom image classifier from scratch using PyTorch and transfer learning techniques.
- Learn TensorFlow: Build, Train, & Deploy ML Models
Learn TensorFlow from the basics to advanced concepts, enabling you to confidently build, train, and deploy machine learning models for practical applications.
- Scale TensorFlow Training and Deploy Models with TensorFlow Lite
Readers will learn to scale TensorFlow model training across multiple devices and efficiently deploy models to various environments using TensorFlow Lite.
- TensorFlow Guide: Building Your First Neural Network with Keras
Learn how to build, compile, and train your first neural network using Keras in TensorFlow.
- TensorFlow Guide: Working with Data - `tf.data` API
Learn how to efficiently load, preprocess, and feed data to your models using TensorFlow's tf.data API.
- Develop and Optimize Local LLMs with Data Science Skills
Learn to fine-tune, restructure, quantize, and retrain local LLMs like Ollama using a blend of data science and deep learning techniques.
- Decoding Large Language Models: A Deep Dive into LLM Architectures
An in-depth exploration of Large Language Model architectures, focusing on the Transformer mechanism.