Entity Embeddings for Categorical Variables
Learn to implement entity embeddings for categorical variables using TensorFlow/Keras, with a focus on converting the model to PyTorch. Practical application using Kaggle competition data.
Bengali.AI- Handwritten Grapheme Classification Using PyTorch
Learn to approach Kaggle's Bengali.AI grapheme classification challenge using PyTorch, covering data exploration, framework setup, and dataset creation for multi-label image classification.
Industry Shocking Text-To-Music AI Model by Facebook Audiocraft - Full Tutorial - Better Than MusicLM
Learn to use Facebook's Audiocraft AI model for text-to-music generation. Install, run, and experiment with this powerful tool on your computer or Google Colab to create amazing AI-generated music.
Introduction to Deep Learning with PyTorch
Learn to implement deep learning models efficiently using PyTorch Lightning, reducing boilerplate code and enabling advanced features like multi-GPU training and model sharding.
ElasticON EMEA - The Search for Relevance with Vector Search
Explore vector search in Elastic, enabling semantic queries for text, images, and audio. Learn about new intelligent discovery methods and combining vector with traditional search techniques.
Training Deep Learning Computer Vision Models in Pytorch Part II by Pranay Goel
Learn advanced techniques for training deep learning computer vision models using PyTorch, including optimization strategies and model architectures for improved performance and accuracy.
Training Deep Learning Computer Vision Models in Pytorch - Part I
Learn to train deep learning computer vision models using PyTorch, covering essential techniques and best practices for developing effective image analysis solutions.
How to Use Facebook's DETR Object Detection Algorithm in Python - Full Tutorial
Hands-on tutorial implementing Facebook's DETR object detection algorithm in Python, covering model loading, image processing, output handling, and visualization with various test cases.
GPU-Acceleration for PyTorch on M1 Macs - Using with BERT
Explore GPU acceleration for PyTorch on M1 Macs, including installation, implementation with BERT, and best practices for training large language models on Mac hardware.
The Truck Backer-Upper
Explore neural network applications in vehicle control, focusing on the Truck Backer-Upper problem. Learn about state transitions, training strategies, and Bayesian neural networks.
Gradient Descent and the Backpropagation Algorithm
Explore gradient descent and backpropagation algorithms, covering supervised learning, parametrized models, loss functions, and neural network architectures with PyTorch implementation.
Graph Convolutional Networks - GCNs
Explore Graph Convolutional Networks (GCNs), their connection to CNNs, and implementation using PyTorch and DGL. Learn about tensors on vertices and edges, residual gated GCNs, and domain sparsity.
Self - Cross, Hard - Soft Attention and the Transformer
Explore attention mechanisms and Transformer architecture, covering self/cross and hard/soft attention, key-value stores, and practical implementation in PyTorch for advanced deep learning applications.
AE, DAE, and VAE with PyTorch - Generative Adversarial Networks and Code
Explore autoencoders, VAEs, and GANs with PyTorch. Learn implementation, visualization, and comparison of generative models through hands-on coding and insightful analogies.
Latent Variable Energy Based Models
Explore Latent Variable Energy Based Models (LV-EBMs) training, covering free energy concepts, loss functionals, and model manifolds. Learn practical implementation using PyTorch and gain insights into decoder learning and latent size selection.
Recurrent Neural Networks, Vanilla and Gated - LSTM
Explore recurrent neural networks, including LSTM, covering architectures, training methods, and practical implementations using PyTorch for sequence-based tasks in deep learning.
Neural Nets - Rotation and Squashing
Explore neural network fundamentals, including affine transformations, non-linearities, and PyTorch implementation. Learn about ReLU, tanh, and deep network architectures through intuitive explanations and coding examples.
Tools, Classification with Neural Nets, PyTorch Implementation
Explore neural network classification, PyTorch implementation, and advanced concepts like space-fabric stretching and regression uncertainty estimation in this comprehensive tutorial.
Supervised and Self-Supervised Transfer Learning With PyTorch Lightning
Explore supervised and self-supervised transfer learning techniques using PyTorch Lightning, comparing their implementation, training, and generalization capabilities in deep learning applications.
Attention and the Transformer
Explore self-attention, key-value stores, and transformer architecture in deep learning. Learn to implement attention mechanisms and understand encoder-decoder paradigms for advanced neural network design.
PyTorch Activation and Loss Functions
Explore activation and loss functions in PyTorch, including kink vs. smooth activations, margin-based losses, and designing effective loss functions for Energy-Based Models (EBMs).