Deep Learning Fundamentals
- Deep Learning Fundamentals
- Unit 1Intro to ML and DL
- Unit 2Using Tensors w/ PyTorch
- Unit 3Model Training in PyTorch
- Unit 3.1Using Logistic Regression for Classification
- Unit 3.2The Logistic Regression Computation Graph
- Unit 3.3Model Training with Stochastic Gradient Descent
- Unit 3.4Automatic Differentiation in PyTorch
- Unit 3.5The PyTorch API
- Unit 3.6Training a Logistic Regression Model in PyTorch
- Unit 3.7 Feature Normalization
- Unit 3 ExercisesUnit 3 Exercies
- Unit 4Training Multilayer Neural Networks Overview
- Unit 4.1Logistic Regression for Multiple Classes
- Unit 4.2Multilayer Neural Networks
- Unit 4.3Training a Multilayer Neural Network in PyTorch
- Unit 4.4Defining Efficient Data Loaders
- Unit 4.5Multilayer Neural Networks for Regression
- Unit 4.6Speeding Up Model Training Using GPUs
- Unit 4 ExercisesUnit 4 Exercises
- Unit 5Organizing Your Code with Lightning
- Unit 5.1 Organizing Your Code with Lightning
- Unit 5.2Training a Multilayer Perceptron using the Lightning Trainer
- Unit 5.3Computing Metrics Efficiently with TorchMetrics
- Unit 5.4Making Code Reproducible
- Unit 5.5Organizing Your Data Loaders with Data Modules
- Unit 5.6The Benefits of Logging Your Model Training
- Unit 5.7Evaluating and Using Models on New Data
- Unit 5.8Add Functionality with Callbacks
- Unit 5 ExercisesUnit 5 Exercises
- Unit 6Essential Deep Learning Tips & Tricks
- Unit 6.1 Model Checkpointing and Early Stopping
- Unit 6.2Learning Rates and Learning Rate Schedulers
- Unit 6.3Using More Advanced Optimization Algorithms
- Unit 6.4Choosing Activation Functions
- Unit 6.5Automating The Hyperparameter Tuning Process
- Unit 6.6Improving Convergence with Batch Normalization
- Unit 6.7Reducing Overfitting With Dropout
- Unit 6.8Debugging Deep Neural Networks
- Unit 6 ExercisesUnit 6 Exercises
- Unit 7Getting Started with Computer Vision
- Unit 7.1Working With Images
- Unit 7.2How Convolutional Neural Networks Work
- Unit 7.3Convolutional Neural Network Architectures
- Unit 7.4Training Convolutional Neural Networks
- Unit 7.5Improving Predictions with Data Augmentation
- Unit 7.6Leveraging Pretrained Models with Transfer Learning
- Unit 7.7Using Unlabeled Data with Self-Supervised
- Unit 7 ExercisesUnit 7 Exercises
- Unit 8Natural Language Processing and Large Language Models
- Unit 8.1Working with Text Data
- Unit 8.2Training A Text Classifier Baseline
- Unit 8.3Introduction to Recurrent Neural Networks
- Unit 8.4From RNNs to the Transformer Architecture
- Unit 8.5Understanding Self-Attention
- Unit 8.6Large Language Models
- Unit 8.7A Large Language Model for Classification
- Unit 8 ExercisesUnit 8 Exercises
- Unit 9Techniques for Speeding Up Model Training
- Unit 10 The Finale: Our Next Steps After AI Model Training
Unit 9.1 Accelerated Model Training via Mixed-Precision Training
References
Code
- Part 2: Hands-On Code Demo, 9.1-mixed-precision
What we covered in this video lecture
In this lecture, we delve into the concept of mixed-precision training in deep learning, which involves using a combination of different numerical precisions (typically float32 and float16 or bfloat16) during model training to improve computational efficiency and speed.
Traditional training methods tend to use 32-bit floating-point numbers (float32) to represent weights, biases, activations, and gradients for neural networks. However, this can be computationally expensive and memory-intensive, particularly for large models and data sets. To address this, mixed-precision training employs lower-precision formats, namely 16-bit floating-point numbers (float16) and Brain Floating Point (bfloat16), in parts of the training process where higher precision is not critical.
The balance between speed, memory usage, and precision makes mixed-precision training an increasingly popular approach for training large-scale machine learning models.
Additional resources if you want to learn more
If you want to learn more details about mixed precision training, including benchmarks, check out my blog article Accelerating Large Language Models with Mixed-Precision Techniques.
Furthermore, you might be interested in the related topic of quantization. An interesting research article on this topic is LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.
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Quiz: 9.1 Accelerated Model Training via Mixed-Precision Training (Part 1)
Quiz: 9.1 Accelerated Model Training via Mixed-Precision Training (Part 2)
Watch Video 1 Mark complete and go to Unit 9.2 →
Unit 9.1