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 9.5 Increasing Batch Sizes to Increase Throughput

#### References

Measuring the Effects of Data Parallelism on Neural Network Training, https://arxiv.org/abs/1811.03600

Group Normalization, https://arxiv.org/abs/1803.08494

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour, https://arxiv.org/abs/1706.02677

Measuring the Effects of Data Parallelism on Neural Network Training, https://arxiv.org/abs/1811.03600

Group Normalization, https://arxiv.org/abs/1803.08494

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour, https://arxiv.org/abs/1706.02677

Measuring the Effects of Data Parallelism on Neural Network Training, https://arxiv.org/abs/1811.03600

On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima, https://arxiv.org/abs/1609.04836

**Code**

- Part 2: Code Demo, 9.5-batchsize-finder/

**What we covered in this video lecture**

In this lecture, we discussed the topic of increasing batch sizes to boost throughput in machine learning model training. The batch size, or the number of training samples processed before the model is updated, plays a critical role in the efficiency and effectiveness of model training. By increasing the batch size, we can process more data simultaneously, leading to higher computational efficiency and increased throughput, particularly on hardware like GPUs which excel in parallel processing.

However, in practice, throughput is not always everything, and we have to make sure to strike a careful balance between batch size, learning rate, computational resources, and the potential impact on model performance, which are all crucial considerations in machine learning training pipelines.

**Additional resources if you want to learn more**

I highly recommend checking out the various papers referenced in the lecture and in the reference section above if you want to learn more about the impact of batch sizes on the computational and predictive performance.

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#### Quiz: 9.5 Increasing Batch Sizes to Increase Throughput (PART 1)

#### Quiz: 9.5 Increasing Batch Sizes to Increase Throughput (PART 2)

##### Watch Video 1 Mark complete and go to Unit 9 Exercises →

Unit 9.5