ChatGPT plugins like Wolfram make this tool 🔥

https://openai.com/blog/chatgpt-plugins

The SAM, which stands for Segment Anything Model, is a fundamental model designed for image segmentation that originates from FAIR.
You can try out a demonstration of the SAM model

https://segment-anything.com/demo

Dive into Learning From Data using the famous MNIST dataset. Explore the classical Logistic Regression algorithm, a simple yet powerful method, and aim to achieve an almost perfect score on MNIST. We review the fundamentals, analyze the data, and evaluate performance metrics. Finally, we apply dimensionality reduction (PCA) and feature engineering (Polynomial Features) to boost performance, achieving an impressive 98.05% accuracy.

📼 Youtube: https://www.youtube.com/watch?v=csZ4dIAPowA

👋 Check my blog post for the text content

✌️The Jupyter notebook can be found here

Mastering Derivatives: From Math to Code - Python Numerical Differentiation

Unlock the secrets of derivatives! Learn the mathematical concept, see how it translates into Python code, and discover three numerical differentiation methods: forward, backward, and central. Watch as we visualize their performance, helping you understand which method provides the most precise results for your calculations!

📼 Youtube: https://www.youtube.com/watch?v=Xynwf7su6DI

👋 Check my blog post for the text content

✌️The Jupyter notebook can be found here

Gradient Descent: Downhill to the Minima. A Visual Journey to Optimization.

Whether you're a beginner curious about AI or an expert looking to deepen your understanding, this video is crafted for you! Follow along with our interactive Python plot where we demonstrate the gradient descent in action. See how the algorithm navigates through a function's landscape to find the optimal solution. Watch as we code in Python, but also explain the logic behind each line.

📼 YouTube: https://youtu.be/LE9O2ntmXGg

👋 Check my blog post for the text content

✌️The Jupyter notebook can be found here

3D Gradient Descent: The Rollercoaster of Optimization

Learn what the gradient is, why it's key to finding the steepest path, and how to use it in both 2D and 3D spaces. We'll code, visualize, and play with gradient descent, showing you how to adjust settings like learning rate and iterations. See it in action with a 3D paraboloid, where we chase the minimum down a hill. Get hands-on with interactive plots and find all the code linked below the video.

📼 YouTube: https://youtu.be/ijhJMyxYZfw

Unlocking Global Minima with Momentum

In this video, we tackle real-world problems by exploring a multi-dimensional, non-linear function - the QFSP (Quadratic Function with Sinusoidal Perturbations). Dive into the complexities of optimization in machine learning with this deep dive into gradient descent.

📼 YouTube: https://www.youtube.com/watch?v=Ob-AxSwNTFA

Cross-Entropy Loss Explained: A Complete Visual Guide

Learn Cross-Entropy Loss step by step! We start with simple building blocks and gradually move to the full picture. Perfect for everyone who wants to understand deep learning classification.

📼 YouTube: https://www.youtube.com/watch?v=BKjOBO1hkkA

Understanding Logits to Probabilities: How Neural Networks Make Decisions with Sigmoid Function

Learn how neural networks transform raw outputs (logits) into interpretable probabilities! This tutorial walks through the complete process using NumPy and Python. Perfect for anyone wanting to understand the fundamentals of neural network decision-making.

📼 YouTube: https://youtu.be/rcg1PTRDAWc

Understanding Weight Initialization in Neural Networks: Normal, Xavier, He, and Leaky He

Weight initialization is crucial in training neural networks, as it sets the starting point for optimization algorithms. The activation function applies a non-linear transformation in our network. Different activation functions serve different purposes. Choosing the right weight initialization and activation function is key to better neural network performance.

📼 YouTube: https://www.youtube.com/watch?v=MQzim4eHr6Q

We implement a complete linear layer with forward and backward propagation, binary cross-entropy loss, and stochastic gradient descent with momentum.

📼 YouTube: https://youtu.be/RO0cDCnPeyE