# Full Stack Deep Learning

## Full Stack Deep Learning

- [Full Stack Deep Learning](https://fall2019.fullstackdeeplearning.com/master.md): Full Stack Deep Learning helps you bridge the gap from training machine learning models to deploying AI systems in the real world.
- [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)
- [Xavier Amatriain (Curai)](https://fall2019.fullstackdeeplearning.com/guest-lectures/xavier-amatriain.md): Co-founder and CTO at Curai. Previously: VP of Engineering at Quora, led Algorithms Engineering at Netflix.
- [Chip Huyen (Snorkel)](https://fall2019.fullstackdeeplearning.com/guest-lectures/chip-huyen-nvidia.md): Chip created the TensorFlow for Deep Learning Research course at Stanford University, has worked on production ML teams at Snorkel an Nvidia, and has published many popular resources for ML Engineers.
- [Lukas Biewald (Weights & Biases)](https://fall2019.fullstackdeeplearning.com/guest-lectures/lukas-biewald-weights-and-biases.md): Lukas is co-founder and CEO of Weights & Biases, an ML tooling company. He previously co-founded and led data labeling company Figure Eight (acquired by Appen).
- [Jeremy Howard (Fast.ai)](https://fall2019.fullstackdeeplearning.com/guest-lectures/jeremy-howard-fast.ai.md): Jeremy Howard is the co-founder of fast.ai, a research institute dedicated to making deep learning more accessible. Previously, Jeremy founded a med tech startup Enlitic, and was President of Kaggle.
- [Richard Socher (Salesforce)](https://fall2019.fullstackdeeplearning.com/guest-lectures/richard-socher-salesforce.md): Richard is Chief Scientist at Salesforce, which he joined through acquisition of his startup Metamind. Previously, Richard was a professor in the Stanford CS department.
- [Raquel Urtasun (Uber ATG)](https://fall2019.fullstackdeeplearning.com/guest-lectures/raquel-urtasun-uber-atg.md): Raquel is currently the Chief Scientist and Head of Uber ATG, and also a Professor at University of Toronto
- [Yangqing Jia (Alibaba)](https://fall2019.fullstackdeeplearning.com/guest-lectures/yangqing-jia-alibaba.md): Yangqing is currently the VP AI / Big Data at Alibaba, and was formerly Director of AI Platform at Facebook. He co-created the Caffe2 and Caffe deep learning frameworks.
- [Andrej Karpathy (Tesla)](https://fall2019.fullstackdeeplearning.com/guest-lectures/andrej-karpathy-tesla.md): Andrej is currently Senior Director of AI at Tesla,
 and was formerly a Research Scientist at OpenAI. His educational materials about deep learning remain among the most popular.
- [Jai Ranganathan (KeepTruckin)](https://fall2019.fullstackdeeplearning.com/guest-lectures/jai-ranganathan-keeptruckin.md): Jai is currently SVP Product at KeepTruckin, and was formerly VP of various AI and Data matters at Uber.
- [Franziska Bell (Toyota Research)](https://fall2019.fullstackdeeplearning.com/guest-lectures/franziska-bell-toyota-research.md): Franziska is currently the Senior Director at Toyota Research Institute, Formerly Director of Data Science at Uber
- [Corporate Training](https://fall2019.fullstackdeeplearning.com/certification/exam-preparation.md)
- [Certification](https://fall2019.fullstackdeeplearning.com/certification/certification.md)


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