Pre-Training BERT from Scratch for Domain-Specific Knowledge Using PyTorch - Part 51
Master BERT model pre-training from scratch using PyTorch, focusing on domain-specific data. Build custom tokenizers, design architecture, and implement masked language modeling for specialized neural information retrieval systems.
Learning Vision Transformer (ViT) Technology: Research Methods and Resources - 2023
Discover effective strategies for learning and implementing Vision Transformer technology through top academic resources, code repositories, and research platforms like HuggingFace and Papers with Code.
Text Vectorization Made Easy - A Tutorial on Keras Preprocessing Layers for AI
Master text vectorization techniques in Keras preprocessing layers for transforming natural language inputs into numerical sequences and building efficient AI input processing pipelines.
PyTorch Vision Transformer - Guide to Fine-Tuning for Object Identification
Master fine-tuning Vision Transformers (ViT) in PyTorch through hands-on coding, focusing on object identification implementation with practical examples and real-time coding demonstrations in Google Colab.
PyTorch Vision Transformer: Applying Pre-trained and Fine-tuned ViT Models for Image Classification
Master Vision Transformer implementation in PyTorch using pre-trained and fine-tuned ViT models for image classification, with hands-on practice in Google Colab and Huggingface integration.
Vision Transformer-Based Transfer Learning for Medical Image Classification 2023
Explore cutting-edge Vision Transformer technology for medical image classification, focusing on mammogram analysis and early breast cancer detection through advanced deep learning approaches.
Optimizing LLM Fine-Tuning with PEFT and LoRA Adapter-Tuning for GPU Performance
Master efficient fine-tuning of large language models using PEFT and LoRA techniques, optimizing GPU memory usage through INT8 quantization and adapter-tuning for cost-effective model development.
Parameter-Efficient Fine-Tuning with LoRA - Optimizing LLMs for Local GPU Training
Master PEFT LoRA techniques to efficiently fine-tune large language models on local GPUs through low-rank adaptation, reducing memory requirements while maintaining model performance.
How to Fine-tune T5 and Flan-T5 Large Language Models - Theory Introduction
Discover the fundamentals of fine-tuning T5 and Flan-T5 language models, exploring key differences, implementation steps, and practical applications using Hugging Face transformers.
Link Prediction with Graph Neural Networks Using DGL and PyG
Master link prediction in graph neural networks using DGL and PyG frameworks, implementing node connectivity prediction through practical code examples and negative sampling techniques.
Experimental KERAS Feature Space for Medical AI Heart Attack Prediction
Dive into advanced feature scaling with KERAS Feature Space, building a medical AI system to predict heart attack probabilities using structured data classification and optimized data pipelines.
Deep Learning: Convolutional Neural Networks and CNN Architecture - Lecture 6
Dive into the fundamentals of Convolutional Neural Networks (CNNs), exploring convolution operations, cross-correlation techniques, and pooling layers for advanced deep learning applications.
Deep Learning: Regularization and Neural Network Normalization Techniques - Lecture 7
Dive into advanced deep learning concepts including layer normalization, FRN, TLU, and Keras implementation while mastering essential regularization techniques and gradient management.
Deep Learning - Regularization Techniques - Lecture 4
Dive into advanced regularization techniques for deep learning, covering weight decay, early stopping, and noise injection methods to enhance model performance and prevent overfitting.
Deep Learning Optimization: Stochastic Gradient Descent and Momentum - Lecture 3
Dive into advanced optimization techniques for deep learning, covering stochastic gradient descent, mini-batches, momentum, and Stein's unbiased risk estimator.
Deep Learning: Feedforward Neural Networks and Backpropagation - Lecture 2
Dive into fundamental concepts of deep learning, exploring perceptrons, feedforward neural networks, and mastering backpropagation techniques for neural network training.
Deep Learning: Historical Background and Course Motivation - Lecture 1
Discover the foundations of deep learning through a comprehensive introduction covering historical context, motivations, and essential background knowledge for beginning your AI journey.
Deep Learning: RLHF, ChatGPT, and Alignment in LLMs - Lecture 14
Dive into advanced concepts of RLHF, ChatGPT training, and LLM alignment, exploring efficient transformers and the technical foundations behind modern language models.
Deep Learning: Deep Reinforcement Learning and Policy Gradients - Lecture 13
Dive into advanced deep reinforcement learning concepts including policy gradients, REINFORCE algorithm, and their applications in Atari games and Go, taught by Ali Ghodsi.
Deep Learning: Introduction to Reinforcement Learning and Policy Iteration - Lecture 12
Dive into reinforcement learning fundamentals, exploring policy iteration, value iteration, Markov decision processes, and Monte Carlo estimation techniques for advanced AI applications.
Deep Learning: Transformers and Encoder-Decoder Architecture - Lecture 10
Dive into transformer architectures, exploring encoder-decoder mechanisms and positional embeddings for advanced deep learning applications.