AI50: Introduction to Deep Learning with Practical Notebooks

Welcome to AI50 – a collection of beginner-friendly and intermediate-level deep learning notebooks designed to introduce essential deep learning concepts and applications. Each Colab notebook covers a specific topic with hands-on examples.

Tensorflow Playground
Tensorflow Projector
OpenCV for Beginners

Notebooks Overview

Explore and experiment with deep learning fundamentals using the following notebooks:

0. Basics of Tensorflow

Tensor operations, math and dataset loading in Tensorflow.

• Colab Notebook: Basics

1. Regression

Learn the basics of regression, a supervised learning approach for predicting continuous outputs.

• Colab Notebook: Regression

2. Basic Image Classification

An introduction to image classification using a simple neural network model.

• Colab Notebook: Basic Image Classification

3. Advanced Image Classification Using CNNs

Dive deeper into image classification with Convolutional Neural Networks (CNNs) for improved accuracy and performance.

• Colab Notebook: Advanced Image Classification Using CNNs

4. Transfer Learning and Finetuning Image Classifiers

Learn to leverage pre-trained models for new tasks through transfer learning and finetuning techniques.

• Colab Notebook: Transfer Learning and Finetuning Image Classifiers

5. An Unhealthy Obsession with Autoencoders: Unsupervised Algorithms for Image Denoising and Anomaly Detection

How can a deep neural network learn without supervision? And how does it change the industries?

• Colab Notebook: Autoencoders

6. Basic Text Classification

Introduction to text classification using neural networks for Natural Language Processing (NLP) tasks.

• Colab Notebook: Basic Text Classification

7. Text Classification Using RNNs

Explore Recurrent Neural Networks (RNNs) for handling sequential data in text classification tasks.

• Colab Notebook: Text Classification Using RNNs

8. Text Generation Using RNNs

Experiment with RNNs for generating text sequences, training models to predict and produce text based on patterns in the data.

• Colab Notebook: Text Generation Using RNNs

9. Simple Audio Recognition

Apply deep learning to recognize basic audio signals, using convolutional networks for audio classification.

• Colab Notebook: Simple Audio Recognition

Each notebook can be accessed and run directly in Google Colab, allowing you to explore and experiment with code in a cloud-based environment.

Getting Started

To get started, simply click on any of the links above. Make sure you have a Google account to save your work on Colab.

Happy Learning!