AI competition

Deepseek r1, a Chinese ChatGPT rival, launched this weekend. Built by grad students on a fraction of OpenAI’s budget, it rivals GPT tech without massive hardware needs!
Check this out: https://chat.deepseek.com

Building a Multi-Layer Neural Network from Scratch: Solving the Spiral Classification Problem.

Learn how neural networks evolved from the simple perceptron to modern deep learning architectures. In this tutorial, we implement a multi-layer neural network from scratch to solve the non-linear spiral classification problem that a single-layer perceptron can't handle.

https://www.youtube.com/watch?v=F09squ1quN0

Fixing Unstable Gradient Descent: Momentum and Gradient Clipping in Deep Learning

Learn how to solve common stability issues in neural network training with two powerful techniques. In this tutorial, we fix unstable stochastic gradient descent (SGD) by implementing proper momentum and gradient clipping from scratch. Watch how we transform an unstable training process that fails to converge into a robust optimization algorithm that can handle higher learning rates and train 5x faster!

📼 YouTube: https://youtu.be/V60ylfHqTe8

Dive deep into machine learning by implementing a neural network from scratch to classify clothing items using the Fashion MNIST dataset

📼 YouTube: https://www.youtube.com/watch?v=reiqm-oI4oc

Building MicroTorch: Implementing a PyTorch-like Tensor Class from Scratch. Backbone of the Tensor

Dive into the foundational elements of deep learning frameworks by building MicroTorch, a PyTorch-inspired tensor library. In this tutorial, we implement a Tensor class with automatic differentiation capabilities essential for neural network training.

📼 YouTube: https://www.youtube.com/watch?v=7GHa_Cla5wU

Clone - Musculoskeletal, superintelligent androids.

Ground zero for the age of androids.

https://x.com/clonerobotics

Building PyTorch from Scratch: Tensor Operations, Transpose & Backward Method Explained

Learn how to build PyTorch from scratch! In this video, we implement transpose operation and the backward method for automatic differentiation. Understand how tensors track dependencies, compute gradients, and apply the chain rule for backpropagation.

📼 YouTube: https://www.youtube.com/watch?v=xUkEKzq7XeQ

Building PyTorch: A Hands-On Guide to the Core Foundations of a Training Framework

Dive into the essentials of neural networks with this hands-on tutorial! Learn how to create a neural network from the ground up, focusing on the Parameter and Module classes, backpropagation, and practical implementation. Explore key concepts like initialization methods (Xavier, He), gradient tracking, and sequential deep learning models - all explained step-by-step with real code examples.

📼 YouTube: https://www.youtube.com/watch?v=b16qKLmp2ro

Building PyTorch: Crafting Linear Layers and Parameter Counting in MicroTorch

In this hands-on tutorial, we refine our tensor operations and introduce linear layers from scratch. Explore how to fix transpose gradients, implement parameter counting for modules, and build efficient linear layers using matrix dot products. We’ll dive into dimensionality checks, Xavier initialization, and bias handling - all with practical code examples.

📼 YouTube: https://www.youtube.com/watch?v=F9GH3nF4nkM

Me

Building PyTorch: Mastering Matrix Multiplication and Linear Layers in MicroTorch

Dive deep into implementing matrix multiplication (dot product) and linear layers from scratch. Learn the calculus behind forward and backward passes, handle multi-dimensional tensors, and build PyTorch-style APIs with real code examples. We’ll cover gradient computation, broadcasting, and operator overloading—key foundations for neural network training.

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

Prepping for your next Machine Learning interview? These 3 resources have you covered:

1️⃣ NeetCode – Master coding interviews: https://neetcode.io/practice

2️⃣ DeepML – Practice ML & Data Science problems: https://www.deep-ml.com/

3️⃣ TensorGym – Sharpen AI skills with unique exercises: https://tensorgym.com/exercises

#MachineLearning #AI #TechInterviews

Building PyTorch: Adding Broadcasting and Addition Operations to MicroTorch

Handle gradient propagation across different tensor shapes, implement backward broadcasting, and enable the plus operator - all with practical code examples. We'll finalize the linear layer and set the stage for a full training loop

https://www.youtube.com/watch?v=pdZij4qj2WQ

Building PyTorch: Overloading Operators for Subtraction and Multiplication in MicroTorch

We'll overload essential tensor operators—subtraction, multiplication, and in-place operations—while ensuring proper gradient tracking and broadcasting. Learn how to implement negation, element-wise multiplication, and right-hand operations, all with real code examples, and see them in action with a mini-experiment.

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

Building PyTorch: Enriching MicroTorch with Logs, Exponents, and Activation Functions

We'll expand the tensor class by adding logarithm, hyperbolic tangent, power, division, and exponential operations, complete with gradient support. Then, we'll build tanh and sigmoid activation functions using these operations - all with practical code examples. See how autograd simplifies backpropagation for free!

📼 YouTube: https://www.youtube.com/watch?v=IM_W-RGMTVc

Building PyTorch: Enhancing MicroTorch with Squeeze, View, and Clip Operations

In this episode of our MicroTorch series, we dive into tensor shape manipulations and gradient clipping! We’ll add squeeze, unsqueeze, view, and clip operations to the tensor class, ensuring proper forward and backward passes for autograd

YouTube: https://youtu.be/9EaSwTdoTag

Thank you all for your support!
300 subs from absolute zero-only because of you! Every comment, repost, like, and view is my best motivation.
See you soon in the new video on Sunday!

Also, check out my X! I'm trying to be more active: https://x.com/datasantaa

If you want to go deeper in Matrix Calculus for DL I think this's the best course: MIT 18.S096 Matrix Calculus For Machine Learning And Beyond, IAP 2023 Instructors: Alan Edelman, Steven G. Johnson

https://www.youtube.com/playlist?list=PLUl4u3cNGP62EaLLH92E_VCN4izBKK6OE

Mastering Tensor Operations: Comparison, Where, and More in MicroTorch

In this episode of our MicroTorch series, we explore essential tensor operations for deep learning! We'll implement comparison operators (less than, greater than, equals) to create boolean tensors, build the "where" method for conditional element-wise selection, and add useful operations like maximum, minimum, threshold, mask_fill, sign, and clip. These methods are key for neural network development, enabling efficient tensor manipulations and gradient computations. Follow along with detailed code examples and tests to see how these operations work in practice.

📼 YouTube: https://www.youtube.com/watch?v=4_kJy6hmv2E

Building MicroTorch: Indexing and Absolute Value Operations for Tensors

In this episode of our MicroTorch series, we dive into tensor indexing and absolute value operations for deep learning! We'll implement the "get item" method to enable flexible tensor indexing, handle gradient computations for indexed tensors, and build the "abs" method to compute the absolute value of tensor elements. These operations are crucial for neural network development, ensuring proper forward and backward passes for autograd. Follow along with detailed code examples and tests to see how these methods work in practice

📼 YouTube: https://www.youtube.com/watch?v=mk2FkpeZOAM