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Trained a single layer neural network in C to predict AND GATE from scratch using gradient descent and Batch training of dataset in almost 300K epochs

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AADITYANSHA

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79,936 görüntüleme • 3 ay önce •via X (Twitter)

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Andrej Karpathy summarizes how neural networks function, including the process of using a loss function in training and applying backpropagation for optimizing the network parameters through gradient descent.
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Andrej Karpathy summarizes how neural networks function, including the process of using a loss function in training and applying backpropagation for optimizing the network parameters through gradient descent.

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I was bored, so I created a self-learning Flappy Bird from scratch using a neural network and a genetic algorithm.
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I was bored, so I created a self-learning Flappy Bird from scratch using a neural network and a genetic algorithm.

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Implemented a visualization for the neural network today. This is a neural network being trained to learn the snake game in a terminal. It's built in Rust using Ratatui More input details in thread
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if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

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ℏεsam

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I vibecoded this neural network visualization for my students and open sourced it. It shows a simple MLP trained on MNIST handwritten digits at several training steps. The visualization is using Three.js and it comes with training code in PyTorch . Link + repo 👇
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I vibecoded this neural network visualization for my students and open sourced it. It shows a simple MLP trained on MNIST handwritten digits at several training steps. The visualization is using Three.js and it comes with training code in PyTorch . Link + repo 👇

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Andrej Karpathy explaining neural nets in 59 seconds is still the bar. Loss, backprop, gradient descent. Go watch Neural Networks: Zero to Hero on YouTube.
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Andrej Karpathy explaining neural nets in 59 seconds is still the bar. Loss, backprop, gradient descent. Go watch Neural Networks: Zero to Hero on YouTube.

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Canadian engineers have created a robot that is almost impossible to beat in a puck game For a year and a half, it was trained in simulations — it played thousands of virtual matches and learned to predict the puck’s trajectory, taking into account bounces off the boards and tiny surface irregularities. To do this, a separate neural network was trained specifically to calculate the physics of impacts. Now the robot reacts faster than a human and simultaneously keeps multiple possible puck movements in mind.

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Trained a small neural network in Verse to play Snake #UEFN #CreatedInFortnite #Verse

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Mnist neural network visualization written from scratch in Odin and Raylib. repo: I'm still new to Odin so if you see a way to imporve something, let me know :)

Bones

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Watch Spot crouch, jump, climb boxes and leap across gaps, controlled by a neural network trained with reinforcement learning (RL) and multi-expert distillation. Multiple expert policies were trained and distilled together into a single policy that was fine tuned to improve performance over diverse terrains. This work was inspired by ANYmal’s parkour capabilities. The neural network processes depth data from Spot's sensors to construct an understanding of the environment.

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I trained a neural network to mimic a forest trail near my apartment (web demo in the post)

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With Helix 02, Figure has introduced a new foundational layer for whole-body control that replaces 109k lines of hand-engineered C++ with a single neural prior for stable, natural motion. - Learned from 1,000+ hours of human motion data and sim-to-real RL across 200,000+ parallel environments. - A 10M-parameter neural network that takes full-body joint state and base motion as input and outputs joint-level actuator commands at 1 kHz (1,000 times a second).

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I'm making a game from scratch in C and getting it shipped to Steam in 90 days. I'm also recording every single step of the process to share with ya. The whole thing is 100% free in ->
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I'm making a game from scratch in C and getting it shipped to Steam in 90 days. I'm also recording every single step of the process to share with ya. The whole thing is 100% free in ->

randy

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Motion planning in complex tasks is hard and still done via slow, explicit, traditional planners. We present a generalist Neural Motion Planner -- a single neural network that plans complex dynamic motions quickly and accurately at test time. Building upon our lab's sim2real efforts, the key idea is to create many complex scenes in simulation and then distill classical motion planner trajectories into a single reactive neural network policy. More details in the thread below! 👇 Open-sourced:

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Quick tip for my graphic designers, In PS, overlay a photo or texture and apply a clipping masked gradient map. (Color to BLK) Open the layer options of the overlay texture, Hold ALT to split the anchor of "Blend if" Underlying layer. See-through Texture Gradient Effect.
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Quick tip for my graphic designers, In PS, overlay a photo or texture and apply a clipping masked gradient map. (Color to BLK) Open the layer options of the overlay texture, Hold ALT to split the anchor of "Blend if" Underlying layer. See-through Texture Gradient Effect.

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