My First FastAI Project: Building an "Is it a Bird?" Classifier (And Beating Dependency Hell!)

Heyya !

Last week, I shared why I'm embarking on this FastAI deep learning journey with Gradient Glimpse – to learn, simplify, and inspire. Well, the journey is already getting interesting!

After the initial setup, my first hands-on challenge from FastAI's Lesson 1 was building an "Is it a bird?" image classifier. This project is a FastAI staple, designed to show just how quickly you can get a powerful deep learning model up and running.

Facing the First Hurdle: Dependency Demons

While the concept was straightforward, I quickly ran into a common roadblock: dependency issues. Turns out, a lot of people face this when trying to replicate the "Is it a bird?" example, especially as environments and library versions evolve.

This is where the real-world problem-solving kicked in. Instead of getting stuck, I leveraged ChatGPT to debug and brainstorm solutions. After some back-and-forth, we found the simplest, most elegant method to resolve the conflicts. It was a clear reminder that AI tools aren't just for coding, but for enhancing your entire development workflow!

You can see my working "Is it a bird?" classifier, complete with the solutions to those pesky dependencies, right here on Kaggle: 🔗 [Link to your Kaggle notebook: https://www.kaggle.com/code/mohammedadilsiraju/fastai-is-it-a-bird-2025-july]

Diving Deeper: Chapter 1 Learnings

Beyond the hands-on project, I immersed myself in Chapter 1 of the Fastbook. This chapter is a goldmine, laying the foundational concepts that make FastAI so powerful. Here are some key "glimpses" I gained:

• Introduction to Deep Learning: Understanding what DL is and its diverse use cases – from image recognition to natural language processing.

• FastAI Library Overview: Getting acquainted with FastAI's high-level API and how it simplifies complex PyTorch operations.

• Jupyter Notebook Essentials: Mastering the basic syntax and workflow within Jupyter Notebooks, which are central to FastAI development.

• Core FastAI Functions: Learning practical functions like download_url, create_thumbnail, and resize_images, crucial for data preparation.

• The DataBlock API: A game-changer! Understanding how the DataBlock makes data loading and transformation incredibly flexible and efficient.

• Training & Validation Data: Grasping the critical importance of splitting your dataset for robust model evaluation.

• The Learner & ResNet18: Demystifying the Learner object and discovering powerful pre-trained models like ResNet18 for transfer learning.

• Glimpses Beyond Vision: Brief but exciting introductions to segmentation, tabular analysis, and collaborative filtering, showcasing DL's breadth.

• Machine Learning Fundamentals: A concise refresher on ML models, the training approach, and the vital role of feedback loops in updating model weights.

• Neural Network History & ML Limitations: A quick overview of neural network evolution and where traditional ML falls short.

Building Foundations: Python & Kaggle Learn

Before diving into FastAI, I also completed Kaggle's "Intro to Programming" and "Intro to Machine Learning" courses. These were instrumental in solidifying my base:

• Python for Data Science: Refresher on essential Python programming concepts.

• Data Science Fundamentals: Learning Pandas for data manipulation, data visualization techniques, and crucial feature selection strategies.

• First ML Models: Building my very own models using algorithms like Decision Trees and Random Forests, and even submitting an entry to the 'Housing Prices Competition for Kaggle Learn Users'!

This first week has been intense but incredibly rewarding. Overcoming the dependency hurdle felt like a small victory, and the theoretical groundwork from Chapter 1 has really started to connect the dots.

Stay tuned as I continue to unravel the fascinating world of deep learning with FastAI, sharing every "gradient glimpse" along the way!

Adil