Hassan Jalloh

Linear algebra is the backbone of deep learning. Tensors (N-dimensional arrays) represent data, weights, and gradients. Scalars (single numbers), vectors (1D), matrices (2D), and higher-dimensional tensors enable operations like element-wise addition, matrix multiplication, and broadcasting. For example, matrix multiplication (e.g., `A @ B`) combines layers in neural networks, while broadcasting automates dimension alignment (e.g., adding a scalar to a matrix). Special matrices like identity matrices (`torch.eye(3)`) and transposes (`A.T`) are crucial for transformations and gradient calculations. Reshaping tensors (`A.view(2, 3)`) adapts data for model inputs.
A practical example: linear regression using PyTorch. Data (house sizes vs. prices) is modeled as `y_pred = X @ W + b`, with mean squared error (MSE) loss. Autograd computes gradients (`loss.backward()`) for weight (`W`) and bias (`b`) updates via gradient descent. Over epochs, `W` converges to 2.0 and `b` to 0.0, learning the true relationship `y = 2X`. Key steps include forward passes, loss calculation, backward passes for gradients, and parameter updates with gradient zeroing.
#LinearAlgebra #DeepLearning #Tensors #MatrixMultiplication #PyTorch #Autograd #GradientDescent #MachineLearning #DataScience #NeuralNetworks #Broadcasting #Reshaping #LinearRegression #MSE #ParameterOptimization

7 months ago | [YT] | 0

Hassan Jalloh

For decades, coding has been a manual, iterative craft. But tools like GitHub Copilot and Codeium aren’t just "code generators." They’re collaboration engines—transforming how we build, debug, and scale software.
This isn’t a minor upgrade; it’s a paradigm shift. As someone who’s led tech teams through seismic changes, here’s the truth: resistance is obsolescence. If you’re still writing every line from scratch, you’re working harder, not smarter.
Here’s how to stay ahead in the AI-driven coding era:
1. Experiment with AI Pair Programmers
You don’t have to abandon your IDE, but refusing to test AI tools is professional malpractice. Dedicate an hour to GitHub Copilot. Let it suggest functions, debug errors, or write tests. This isn’t about replacing developers—it’s about accelerating them.
2. Master the Art of Problem Framing
AI coders thrive on precision. The clearer your prompts (e.g., “Build a Python function to validate emails with regex”), the sharper the output. Hone this skill, and you’ll outpace those stuck in trial-and-error mode.
3. Automate the Grunt Work
If your team isn’t leveraging AI for repetitive tasks—documentation, boilerplate code, or dependency updates—you’re bleeding time and money. Integrate AI into CI/CD pipelines, code reviews, and legacy system migrations.
4. Grapple with the Ethics
AI-generated code isn’t flawless. Who’s liable for security gaps? How do you avoid IP infringement? Stay informed on governance, compliance, and best practices—this is the new table stakes for tech leaders.
5. Elevate Human Creativity
AI can write code, but it can’t architect visionary solutions or understand user pain points. The more AI handles syntax, the more your value shifts to big-picture thinking, user empathy, and innovation. Double down there.
The future belongs to developers who merge AI’s speed with human ingenuity. Adapt or get left in the commit history.
#AIcoding #FutureOfTech #CodeAutomation

7 months ago | [YT] | 0

Hassan Jalloh

🎉 Welcome to My Channel! 🎉

Hey there! I’m thrilled to have you here on this exciting journey. Whether you're into creativity, innovation, or just looking for some fresh insights, this is the place to be. Hit that subscribe button and let's make something amazing together! 🚀

1 year ago | [YT] | 0