Adding Layers and Forward Functions to Your Neural Network in PyTorch
Learn to create neural networks in PyTorch: design layers, implement forward functions, and adapt models for various data types. Hands-on examples cover basic to complex architectures for structured and unstructured data.
Everything You Need to Know About Tensors in Deep Learning With PyTorch
Comprehensive guide to PyTorch tensors: creation, operations, data types, and GPU usage. Hands-on examples cover essential concepts for deep learning beginners.
Build a Neural Network for Classification from Scratch with PyTorch
Learn to build a neural network for classification using PyTorch, covering data preparation, network structure, and implementation. Gain hands-on experience in deep learning fundamentals.
TorchVision Quick Start - Building an Image Classifier in 10 Minutes
Build an image classifier for orangutans and komodo dragons using PyTorch's torchvision library and a pretrained ResNet model. Learn to leverage pre-trained models for quick and effective computer vision tasks.
PyTorch's Computational Graph and Torchviz Visualization Tutorial
Learn to build neural networks, understand PyTorch's computational graph, and visualize deep learning networks using PyTorchViz in this comprehensive tutorial.
Building a Machine Learning Drowsiness Detector App with Sound Alerts
Build a ML drowsiness detector app with PyTorch YOLOv5 and Tkinter. Detect awake/drowsy states, play alerts for sleeping, and learn object detection implementation in a practical application.
Building a Neural Network with PyTorch - Coding Challenge
Learn to build a neural network using PyTorch in a fast-paced, 15-minute coding challenge. Follow along as the instructor attempts this task for the first time, offering insights and practical tips.
Mean Average Precision Explained and PyTorch Implementation
Learn to calculate and implement Mean Average Precision (mAP) for evaluating object detection models, with a detailed explanation and hands-on PyTorch implementation from scratch.
Intersection Over Union Explained and PyTorch Implementation
Comprehensive guide to Intersection over Union (IoU) in object detection, covering theory and practical implementation in PyTorch. Ideal for understanding this crucial metric.
Pytorch Seq2Seq with Attention for Machine Translation
Build a Seq2Seq model with Attention in PyTorch for German-to-English machine translation, applying it to the Multi30k dataset and learning from scratch.
Pytorch Text Generator With Character Level LSTM
Learn to build a character-level LSTM text generator in PyTorch, focusing on generating new baby names. Explore practical implementation of RNNs for creative text generation tasks.
Pytorch ResNet Implementation from Scratch
Learn to implement ResNet models (ResNet50, ResNet101, ResNet152) from scratch using PyTorch. Gain insights into the architecture and practical coding techniques for deep learning.
PyTorch GoogLeNet - InceptionNet Implementation from Scratch
Learn to implement GoogLeNet/InceptionNet from scratch in PyTorch, with explanations of network architecture and step-by-step coding guidance.
PyTorch NLP Model Training and Fine-Tuning on Colab TPU Multi-GPU with Accelerate
Learn to train and fine-tune PyTorch NLP models on Colab TPU using Hugging Face's Accelerate library for efficient multi-GPU/TPU implementation with minimal code changes.
Finetuning Whisper with Huggingface and Pytorch Lightning
Explore fine-tuning Whisper using Hugging Face and PyTorch Lightning for advanced speech recognition. Learn techniques to enhance model performance and adapt it to specific use cases.
Batch Normalization in PyTorch - Adding Normalization to Convolutional Neural Network Layers
Learn to implement batch normalization in convolutional neural networks using PyTorch. Explore the benefits, create and compare networks, and optimize performance through hands-on coding and practical examples.
PyTorch Sequential Models - Neural Networks Made Easy
Learn to build neural networks using PyTorch's Sequential class. Explore multiple ways to construct models, set random seeds for reproducibility, and understand the benefits of this approach for creating efficient deep learning architectures.
PyTorch DataLoader Source Code Debugging - Batch Building and Normalization
Debugging session exploring PyTorch DataLoader's inner workings, focusing on data normalization, batch building, and parameter impacts. Insights into efficient data handling for deep learning projects.
PyTorch Dataset Normalization Using torchvision.transforms.Normalize()
Learn to normalize datasets in PyTorch using torchvision.transforms.Normalize(). Explore feature scaling, standardization, and color channel normalization. Understand the impact on neural network training.
Object Detection with Transformers - From Training to Deployment
Explore cutting-edge object detection using transformers, from fundamental concepts to advanced techniques. Learn scalable training with Determined AI and deployment using MLflow.
PyTorch and COVID-19 Research: Advancements in AI and Data Analysis - Keynotes
Explore PyTorch's advancements in machine learning and learn how AI is helping manage COVID-19 research data, featuring insights on industry adoption and scientific information accessibility.