112 тысяч подписчиков
349 видео
Unit 1 | Welcome to Machine Learning and Deep Learning
Unit 1.1 | What is Machine Learning? | Part 1 | How does it work?
Unit 1.1 | What is Machine Learning? | Part 2 | How does it relate to deep learning?
Unit 1.2 | How Can We Use Machine Learning? | Part 1 | Common Application Areas
Unit 1.2 | How Can We Use Machine Learning? | Part 2 | The Three Classic Categories
Unit 1.3 | A Typical Machine Learning Workflow
Unit 1.4 | The First Machine Learning Classifier | Part 1 | Defining the Prediction Task
Unit 1.4 | The First Machine Learning Classifier | Part 2 | Making Predictions
Unit 1.4 | The First Machine Learning Classifier | Part 3 | The Training Process
Unit 1.4 | The First Machine Learning Classifier | Part 4 | Perceptron Training by Example
Unit 1.4 | The First Machine Learning Classifier | Part 5 | Weight Updates
Unit 1.4 | The First Machine Learning Classifier | Part 6 | Perceptron Decision Boundary
Unit 4.4 | Defining Efficient Data Loaders | Part 1 | Avoiding Data Loading Bottlenecks
Unit 4.3 | Training a Multilayer Perceptron in PyTorch | Part 1
PyTorch Lightning - Debugging with fast dev run
Episode 3: From PyTorch to PyTorch Lightning
Run PyTorch on TPU and GPU without changing code
Unit 1.6 | Perceptron in Python | Part 1| Coding Example
Building ML Pipelines Like Legos with Scikit-Learn and Lightning AI
Version Control Your Code Using Git ... And Thank Yourself Later | Ep 6
Unit 7.4 | Training CNNs | Part 3 | Introducing the CIFAR Dataset
Jupyter notebooks vs Python projects: Learn when when to use which | Ep 1
Contributor Meetup: PyTorch Lightning Flash - Your PyTorch AI Factory
Unit 2.2 | What are Tensors? | Part 01 | Tensors for Data
PyTorch Lightning - Automatic Batch Size Finder
Lightning Early Stopping
Converting from PyTorch to PyTorch Lightning
Twitch Live Coding: Deep Dive into a Single Example Code Flow
Unit 3.6 | Training a Logistic Regression Model in PyTorch | Part 2
Unit 5.1 | Getting Started with Structuring Your PyTorch Code using Lightning
PyTorch Lightning - Automatic Learning Rate Finder
Self-Supervised Learning of Image Features with SwAV (with author Mathilde Caron)
Episode 1: Training a classification model on MNIST with PyTorch
PyTorch Lightning - Customizing a Distributed Data Parallel (DDP) Sampler
How Thunder JIT builds on Thunder Python interpreter to convert PyTorch models to Thunder traces
PyConIL 2021 - From Research to Production, Minus the Boilerplate
Unit 4.5 | Multilayer Neural Networks for Regression | Part 2 | Coding
The 8 Essential Terminal Commands you Need to Know | Ep 2
Managing Code Projects with Git Branching | Ep 7
Unit 9.1 | Accelerated Model Training via Mixed-Precision Training | Part 1
How to Debug Python Code -- Find Errors More Efficiently | Ep 5
Mixed Precision Training
Unit 6.3 | Using More Advanced Optimization Algorithms | Part 2 | Adaptive Learning Rates
Grid AI in 3 minutes - Run pytorch, tensorflow, lightning, keras on cloud GPUs and CPUs
PyTorch Lightning Training Intro
How to Deploy Diffusion Models
Unit 7.5 | Data Augmentation | Part 1 | Concepts and Examples
Unit 3.6 | Training a Logistic Regression Model in PyTorch | Part 3
Unit 6.1 | Model Checkpointing and Early Stopping | Part 3
Unit 6.2 | Learning Rates and Learning Rate Schedulers | Part 1 | Finding a Good Learning Rate
Unit 4.1 | Logistic Regression for Multiple Classes | Part 5 | The Cross Entropy Loss Function
Unit 5.2 | Training a Multilayer Perceptron in PyTorch Lightning | Part 1
Thunder Sessions | Session 26 | Scale up FSDP with Thunder!
Unit 5 | Organizing Your PyTorch Code with Lightning
Self Supervised Learning
Lightning Progress Bar
Unit 7 | Getting Started with Computer Vision
Unit 4.3 | Training a Multilayer Perceptron in PyTorch | Part 4
