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Achievement: QuickdrawAchievement: Pull Sharkx2

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Achievement: QuickdrawAchievement: Pull Sharkx2

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Neill-Erasmus/README.md

Neill Jean Erasmus

💥 About Me

I am a Computer Science student at Nelson Mandela University with a passion for AI and machine learning. Through academic pursuits and hands-on projects, I’ve developed a strong skill set that allows me to tackle real-world challenges using technology.

I am committed to leveraging AI and machine learning to drive innovation and solve complex problems. Always eager to learn, I continuously strive to push the boundaries of what is possible and contribute meaningfully to projects requiring both technical expertise and creative thinking.

I am currently seeking opportunities to apply my skills in machine learning, AI, and software development, with a focus on innovation and impactful solutions.

🚀 Skills

  • Programming Languages: Python, C#
  • Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, NLP, Dimensionality Reduction
  • Deep Learning: CNNs, RNNs, Autoencoders, GANs, Self-Organizing Maps
  • AI: Q-Learning, Deep Q-Learning, A3C, Augmented Random Search
  • Computer Vision: CNNs, GANs, DCGANs

🌱 Learning

I’m dedicated to deepening my understanding of AI, machine learning, and data science. Continuously refining my skills and knowledge.

Studies

  • Bachelors Degree in Computer Science – Nelson Mandela University. Currently in my final undergraduate year.

📫 Contact

Let's connect! Feel free to reach out:

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  1. kung-fu kung-fu Public

    I utilized the A3C (Asynchronous Advantage Actor-Critic) algorithm to train a Deep Q-Learning (DQN) model, specifically tailored to solve the Kungfu gym environment.

    Python 1

  2. pacman pacman Public

    I developed and trained a deep convolutional Q-learning model to enable an agent to successfully solve the Pacman gym environment.

    Python

  3. lunar-lander lunar-lander Public

    Trained a Deep Q-Learning agent to autonomously land a lunar module in OpenAI's Gymnasium Lunar Lander environment.

    Python

  4. sugarcane-leaf-disease-detection sugarcane-leaf-disease-detection Public

    I developed and trained a convolutional neural network (CNN) to recognize diseases in sugarcane by analyzing images of the leaves.

    Jupyter Notebook 1

  5. ai-generated-art ai-generated-art Public

    I created and trained a DCGAN (Deep Convolutional Generative Adversarial Network) to produce artificial portraits using a dataset containing more than 6000 images.

    Python 2

  6. diabetes-classification diabetes-classification Public

    An artificial neural network-based model for diabetes prediction, leveraging machine learning techniques to analyze relevant health data and provide accurate predictions regarding the likelihood of…

    Jupyter Notebook 1