Deep Learning Prerequisites: The Numpy Stack in Python (V2+)

The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence

Created by: Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

What Will I Learn?

• Understand supervised machine learning (classification and regression) with real-world examples using Scikit-Learn
• Understand and code using the Numpy stack
• Make use of Numpy, Scipy, Matplotlib, and Pandas to implement numerical algorithms
• Understand the pros and cons of various machine learning models, including Deep Learning, Decision Trees, Random Forest, Linear Regression, Boosting, and More!
• Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Requirements

• Understand linear algebra and the Gaussian distribution
• Be comfortable with coding in Python
• You should already know "why" things like a dot product, matrix inversion, and Gaussian probability distributions are useful and what they can be used for

Target audience

• Students and professionals with little Numpy experience who plan to learn deep learning and machine learning later
• Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code