Blog
AI Evals For Engineers & PMs: A Retrospective
The initial cohort of the AI Evals for Engineers & PMs course is now in the books. Below are some of my thoughts on the course including why I’m recommending it to you and also some lessons learned along the way.
Gemini (Part I) - Why You Should Consider Gemini
LLM Workshop #4 - L1 Evals and Dataset Curation (Part I)
LLM Workshop #3 - How Far Can We Get With Prompting Alone?”
LLM Workshop #2 - From Noise to Knowledge: Mastering the Art of Objective Definition and Data Refinement”
Structuring Enums for Flawless LLM results with Instructor
LLM Workshop #1 - How to take a course that never ends
A Journey Through Fastbook (AJTFB) - Chapter 9: Tabular Modeling
In chapter of 8 of “Deep Learning for Coders with fastai & PyTorch” we learned that the neural network version of of collaborative model is in fact built on something called TabularModel, and that in fact, an EmbeddingNN is nothing but a TabularModel without any continuous (or real) numbers. “Structured” or “tabular” data describes datasets that look like an Excel spreadsheet or a relational database table, of which, it may be a composed of both categorical and/or real numbers. Working with such data is the subject of chapter 9, so lets go!
A Journey Through Fastbook (AJTFB) - Chapter 8: Collaborative Filtering
This chapter of “Deep Learning for Coders with fastai & PyTorch” moves us away from computer vision to collaborative filtering (think recommendation systems). We’ll explore building these models using the traditional “dot product” approach and also using a neural network, but we’ll begin by covering the idea of “latent factors,” which are both important for colloborative and tabular models. Lets go!
A Journey Through Fastbook (AJTFB) - Chapter 7: Advanced techniques for training image classification models
This chapter of "Deep Learning for Coders with fastai & PyTorch" details several techniques you can apply to getting SOTA results with your image classification models! It’s the last chapter dedicated to computer vision before diving into colloborate filtering, tabular, and NLP models
A Journey Through Fastbook (AJTFB) - Chapter 6: Regression
Its the more things you can do with computer vision chapter of "Deep Learning for Coders with fastai & PyTorch"! Having looked at both multiclass and multilable classification, we now turn our attention to regression tasks. In particular, we’ll look at key point regression models covered in chapter 6. Soooo lets go!
A Journey Through Fastbook (AJTFB) - Chapter 6: Multilabel Classification
Its the more things you can do with computer vision chapter of "Deep Learning for Coders with fastai & PyTorch"! We’ll go over everything you need to know to get started with multi-label classification tasks from datablocks to training and everything in between. Next post we’ll look at regression tasks, in particular key point regression models that are also covered in chapter 6. Soooo lets go!
A Journey Through Fastbook (AJTFB) - Chapter 5: Multiclass classification
Its the image classification chapter of "Deep Learning for Coders with fastai & PyTorch"! We’ll go over everything you need to know to get started with multiclass classification, from setting up your DataBlock and loss function, to some of the core techniques for evaluating and improving your model’s predictions. So without further adieu, lets go …
Contributing to fastai: Setup your local development environment & submit a PR
Multilingual Sequence Classifaction with the MBart Family
A Journey Through Fastbook (AJTFB) - Chapter 4: Stochastic Gradient Descent
The fourth in a weekly-ish series where I revisit the fast.ai book, "Deep Learning for Coders with fastai & PyTorch", and provide commentary on the bits that jumped out to me chapter by chapter. So without further adieu, let’s go!
A Journey Through Fastbook (AJTFB) - Chapter 3: Data Ethics
The third in a weekly-ish series where I revisit the fast.ai book, "Deep Learning for Coders with fastai & PyTorch", and provide commentary on the bits that jumped out to me chapter by chapter. So without further adieu, let’s go!
A Journey Through Fastbook (AJTFB) - Chapter 2: Doing Deep Learning
The second in a weekly-ish series where I revisit the fast.ai book, "Deep Learning for Coders with fastai & PyTorch", and provide commentary on the bits that jumped out to me chapter by chapter. So without further adieu, let’s go!
A Journey Through Fastbook (AJTFB) - Chapter 1: The Basics of Deep Learning
The first in a weekly-ish series where I revisit the fast.ai book, "Deep Learning for Coders with fastai & PyTorch", and provide commentary on the bits that jumped out to me chapter by chapter. So without further adieu, let’s go!
Summarization with blurr
