> For the complete documentation index, see [llms.txt](https://fall2019.fullstackdeeplearning.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://fall2019.fullstackdeeplearning.com/course-content.md).

# Course Content

- [Setting up Machine Learning Projects](https://fall2019.fullstackdeeplearning.com/course-content/setting-up-machine-learning-projects.md): How To Set Your Machine Learning Projects Up For Success
- [Overview](https://fall2019.fullstackdeeplearning.com/course-content/setting-up-machine-learning-projects/overview.md): According to a 2019 report, 85% of AI projects fail to deliver on their intended promises to business. Why do so many projects fail?
- [Lifecycle](https://fall2019.fullstackdeeplearning.com/course-content/setting-up-machine-learning-projects/lifecycle.md): What is the lifecycle of a machine learning project?
- [Prioritizing](https://fall2019.fullstackdeeplearning.com/course-content/setting-up-machine-learning-projects/prioritizing.md): How do you decide which machine learning projects to work on?
- [Archetypes](https://fall2019.fullstackdeeplearning.com/course-content/setting-up-machine-learning-projects/archetypes.md): What are the different archetypes of machine learning projects?
- [Metrics](https://fall2019.fullstackdeeplearning.com/course-content/setting-up-machine-learning-projects/metrics.md): How do you pick metrics to optimize your machine learning project?
- [Baselines](https://fall2019.fullstackdeeplearning.com/course-content/setting-up-machine-learning-projects/baselines.md): How to choose a good baseline to know whether your model is performing well or not?
- [Infrastructure and Tooling](https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling.md): The Training and Evaluation Phases of Your Machine Learning Workflow
- [Overview](https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/overview.md): What are the components of a machine learning system?
- [Software Engineering](https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/software-engineering.md): What are the good software engineering practices for Machine Learning developers?
- [Computing and GPUs](https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/hardware.md): How to choose appropriate hardware for your compute needs? Should you compute in the cloud or using your own GPUs?
- [Resource Management](https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/resource-management.md): How to effectively manage compute resources?
- [Frameworks and Distributed Training](https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/frameworks-and-distributed-training.md): How to choose a deep learning framework? How to enable distributed training for your models?
- [Experiment Management](https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/experiment-management.md): How to keep track of your model experiments?
- [Hyperparameter Tuning](https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/hyperparameter-tuning.md): How to tune your model hyper-parameters?
- [All-in-one Solutions](https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/all-in-one-solutions.md): How to choose between different machine learning platforms?
- [Data Management](https://fall2019.fullstackdeeplearning.com/course-content/data-management.md): The Data Phase of Your Machine Learning Workflow
- [Overview](https://fall2019.fullstackdeeplearning.com/course-content/data-management/overview.md): Why is data management important?
- [Sources](https://fall2019.fullstackdeeplearning.com/course-content/data-management/sources.md): Where do the training data come from?
- [Labeling](https://fall2019.fullstackdeeplearning.com/course-content/data-management/labeling.md): What are effective ways to label your data?
- [Storage](https://fall2019.fullstackdeeplearning.com/course-content/data-management/storage.md): What are appropriate ways to store your data?
- [Versioning](https://fall2019.fullstackdeeplearning.com/course-content/data-management/versioning.md): What are the different levels of versioning your data?
- [Processing](https://fall2019.fullstackdeeplearning.com/course-content/data-management/processing.md): What are efficient ways to process your data?
- [Machine Learning Teams](https://fall2019.fullstackdeeplearning.com/course-content/ml-teams.md): How To Build Your Machine Learning Teams Effectively
- [Overview](https://fall2019.fullstackdeeplearning.com/course-content/ml-teams/overview.md): Why is running a Machine Learning team hard?
- [Roles](https://fall2019.fullstackdeeplearning.com/course-content/ml-teams/roles.md): What are the different roles inside a Machine Learning team? What skills are needed for each of them?
- [Team Structure](https://fall2019.fullstackdeeplearning.com/course-content/ml-teams/team-structure.md): How to structure a Machine Learning team inside an organization?
- [Managing Projects](https://fall2019.fullstackdeeplearning.com/course-content/ml-teams/managing-projects.md): How to manage machine learning projects properly?
- [Hiring](https://fall2019.fullstackdeeplearning.com/course-content/ml-teams/hiring.md): How to source Machine Learning talent? How to interview Machine Learning candidates? How to find a job as a Machine Learning practitioner?
- [Training and Debugging](https://fall2019.fullstackdeeplearning.com/course-content/training-and-debugging.md): How To Troubleshoot Your Deep Learning Models
- [Overview](https://fall2019.fullstackdeeplearning.com/course-content/training-and-debugging/overview.md): Why is deep learning troubleshooting hard?
- [Start Simple](https://fall2019.fullstackdeeplearning.com/course-content/training-and-debugging/start-simple.md): How to start simple with deep learning models?
- [Debug](https://fall2019.fullstackdeeplearning.com/course-content/training-and-debugging/debug.md): How to implement and debug deep learning models?
- [Evaluate](https://fall2019.fullstackdeeplearning.com/course-content/training-and-debugging/evaluate.md): How to evaluate deep learning model?
- [Improve](https://fall2019.fullstackdeeplearning.com/course-content/training-and-debugging/improve.md): How to improve deep learning model?
- [Tune](https://fall2019.fullstackdeeplearning.com/course-content/training-and-debugging/tune.md): How to tune deep learning models?
- [Conclusion](https://fall2019.fullstackdeeplearning.com/course-content/training-and-debugging/conclusion.md): What are the key takeaways to troubleshoot deep neural networks?
- [Testing and Deployment](https://fall2019.fullstackdeeplearning.com/course-content/testing-and-deployment.md): The Testing and Deployment Phase of Your Machine Learning Workflow
- [Project Structure](https://fall2019.fullstackdeeplearning.com/course-content/testing-and-deployment/project-structure.md): What are the different components of a machine learning system?
- [ML Test Score](https://fall2019.fullstackdeeplearning.com/course-content/testing-and-deployment/ml-test-score.md): How can you test your machine learning system?
- [CI / Testing](https://fall2019.fullstackdeeplearning.com/course-content/testing-and-deployment/ci-testing.md): What do testing and continuous integration mean?
- [Docker](https://fall2019.fullstackdeeplearning.com/course-content/testing-and-deployment/docker.md): What is Docker?
- [Web Deployment](https://fall2019.fullstackdeeplearning.com/course-content/testing-and-deployment/web-deployment.md): How to deploy your models to the web?
- [Monitoring](https://fall2019.fullstackdeeplearning.com/course-content/testing-and-deployment/monitoring.md): How to monitor your machine learning system?
- [Hardware/Mobile](https://fall2019.fullstackdeeplearning.com/course-content/testing-and-deployment/hardware-mobile.md): How to deploy your models to hardware and mobile devices?
- [Research Areas](https://fall2019.fullstackdeeplearning.com/course-content/research-areas.md): Professor Pieter Abbeel covers state of the art deep learning methods that are just now becoming usable in production.
- [Labs](https://fall2019.fullstackdeeplearning.com/course-content/labs.md): Course Project: Build and Deploy an End-to-End Deep Learning System
- [Where to go next](https://fall2019.fullstackdeeplearning.com/course-content/where-to-go-next.md)