Don’t let those PhDs have all the fun- you too can use deep learning to solve practical problems. If you are looking for a guide that starts at the ground floor and takes you to the cutting edge of research, this is the book for you. Praise for Deep Learning for Coders with fastai and PyTorch Jeremy Howard & Sylvain Gugger Foreword by Soumith Chintala A fastai Learner from Scratchįastai & PyTorch AI Applications Without a PhD A Neural Net from the FoundationsĬhapter 19. Application Architectures Deep DiveĬhapter 17. A Language Model from ScratchĪctivation Regularization and Temporal Activation RegularizationĬhapter 13. Understanding fastai’s Applications: Wrap UpĬhapter 12.
TfmdLists and Datasets: Transformed CollectionsĪpplying the Mid-Level Data API: SiamesePair Putting Our Texts into Batches for a Language ModelĬhapter 11. Collaborative Filtering Deep Diveīootstrapping a Collaborative Filtering Modelĭeep Learning for Collaborative Filtering Training a State-of-the-Art ModelĬhapter 8. Pixels: The Foundations of Computer VisionĬhapter 6. Under the Hood: Training a Digit Classifier Understanding fastai’s ApplicationsĬhapter 4. Identifying and Addressing Ethical Issuesįairness, Accountability, and Transparencyĭeep Learning in Practice: That’s a Wrap! Integrating Machine Learning with Product Design
Unforeseen Consequences and Feedback Loopsīugs and Recourse: Buggy Algorithm Used for Healthcare Benefitsįeedback Loops: YouTube’s Recommendation Systemīias: Professor Latanya Sweeney “Arrested” Turning Your Model into an Online Application Training Your Model, and Using It to Clean Your Data Image Recognizers Can Tackle Non-Image Tasksĭeep Learning Is Not Just for Image Classification The Software: PyTorch, fastai, and Jupyter (And Why It Doesn’t Matter)
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