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
1.5 – Setting Up Our Computing Environment
References
- Official JupyterLab documentation
- Official Visual Studio Code website
- Miniforge repository with download instructions
What we covered in this video lecture
In this video, we discussed the three aspects we require from our computing environment, i.e., it should be a place where we can
- write code
- install new Python libraries
- run and debug code
When it comes to running and debugging code, the free & open-source Visual Studio Code is a great choice. It features countless plugins to make it more powerful.
However, for prototyping machine learning code and teaching, we often prefer interactive Jupyter notebooks since they allow us to run our code incrementally and visualize results. Thus, we will spend a lot of time in JupyterLab (the most common program for running Jupyter notebooks) in this class.
For installing Python libraries, many machine learning researchers and practitioners prefer using conda, a program that lets us create virtual environments with different Python and Python package versions. It also makes it super easy to install Python libraries while automatically taking care of dependency management.
Additional resources if you want to learn more
Miniconda / Miniforge
As mentioned in the video, I recommend using conda for managing your virtual environments and Python libraries. Miniconda is the original conda distribution, whereas Miniforge is a community project around conda — the difference is that Miniforge libraries are often more up-to-date, which is why it’s often a preferred choice over Miniconda these days. If you want to learn more about the basic usage, William Falcon (CEO at Lightning AI) and I made a short video explaining the basics here.
PyCharm
There exist many other coding environments that are popular among Python users. Some of the most popular examples include PyCharm, which has many advanced code analysis and debugging features. William Falcon (CEO at Lightning AI) and I made a short video explaining the basics of using the PyCharm IDE (integrated developer environment) here. If you like to try it, we also have a video on debugging with PyCharm here. But no worries, we will revisit the topic of Debugging in Unit 2.
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Quiz: 1.5 Setting Up Our Computing Environment
Watch Video 1 Mark complete and go to Unit 1.6 →
Unit 1.5