Welcome to this guide where we explore the fascinating world of machine learning through the codes shared on my YouTube channel, Machine Learning With Phil. In this blog, we will dive into various projects that tackle different challenges and improve your understanding of AI concepts. Let’s get started!

Kaggle Venus-Volcanoes

My crude implementation of a convolutional neural network aims to classify images gathered by the Magellan spacecraft. In this project, we face a common challenge: data imbalance. Most images lack the desired feature (i.e., volcanoes). This necessitates creative data engineering during model training.

• In the test set, 84.1% of the data lacks volcano images.
• Despite this imbalance, our model achieves around 88% accuracy, outperforming a naive model predicting all zeroes.

Want to explore this further? Check out the video here, and the dataset can be found on Kaggle.

Reinforcement Learning with Deep Q-Learning

Here, I implemented the Deep Q-learning algorithm using PyTorch to teach an AI agent to play Space Invaders. However, training takes some time, even with a robust setup (1080Ti & i7 7820k @ 4.4 GHz).

• Stay tuned for my upcoming video that chronicles the performance after extended training.
• Learn more about how Deep Q-learning works in my blog post at NeuralNet.ai.
• Watch the video here.

Simple CNN in TensorFlow

This is a fundamental implementation of a Convolutional Neural Network (CNN) using TensorFlow version 1.5. The model achieves an impressive 98% accuracy after just ten epochs of training.

• Watch the tutorial here.
• The dataset can be accessed here.

Reinforcement Learning: Monte Carlo Control

In this implementation, I tackled the blackjack environment from the OpenAI gym using Monte Carlo control without exploring starts. The model trained over 1,000,000 games shows a win rate of about 42%.

• Video tutorial available at this link.

Off Policy Monte Carlo Control in Blackjack

Another variant of the blackjack environment features an off-policy Monte Carlo control approach, which achieves a win rate of approximately 29% after similar training.

• Check the video demonstration here.

Q-Learning in Cart Pole

This implementation focuses on applying the Q-learning algorithm to the cart pole problem, inspired by a course from a lazy programmer.

• Find the video tutorial here.

Double Q-Learning and SARSA Implementation

I also worked on a double Q-learning algorithm as well as SARSA for the cart pole environment, providing a platform to compare the performance of these learning strategies.

• Video tutorials can be found here and here, respectively.

Troubleshooting Your AI Projects

As you explore these projects, you might encounter some hiccups along the way. Here are some troubleshooting ideas:

• Ensure your environment is set up correctly by following the installation instructions provided in each project.
• If you face performance issues, consider optimizing your model architecture or using a more powerful GPU.
• Data imbalance can be mitigated by applying techniques like data augmentation or balancing the dataset.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.