| <html> | |
| <head> | |
| <title>Demo for Dynamic Bar Charts | |
| </title> | |
| <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js" charset="utf-8"></script> | |
| <script> | |
| function loadChart() | |
| { | |
| var profit=[40,50,50,45,30,35,50,60,55,30,40,50]; | |
| var h=100; |
| library(ggplot2) | |
| library(mvtnorm) | |
| plot(seq(1, 6, 0.01), dnorm(seq(1,6,0.01), 3.5,0.5), xlab = "A", ylab="P(A)", main="Prior Distribution", type="l") | |
| mean=c(4,4) | |
| sigma=matrix(c(1,0,0,1), nrow = 2, ncol = 2) | |
| dist<-matrix(rep(1, 36), nrow = 6, ncol = 6) | |
| for(j in c(1:6)) | |
| { |
| // Use Gists to store code you would like to remember later on | |
| console.log(window); // log the "window" object to the console |
| 1. Written Memories: Understanding, Deriving and Extending the LSTM : http://r2rt.com/written-memories-understanding-deriving-and-extending-the-lstm.html | |
| 2. RECURRENT NEURAL NETWORKS TUTORIAL, PART 1 – INTRODUCTION TO RNNS : http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ | |
| 3. The Unreasonable Effectiveness of Recurrent Neural Networks: http://karpathy.github.io/2015/05/21/rnn-effectiveness/ | |
| 4. Understanding LSTM: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ | |
| 5. Generative models : https://blog.openai.com/generative-models/ |
| class Pacer: | |
| def __init__(self, campaign_impression_goal=0, refresh_rate=1, total_cycles=1): | |
| self.campaign_impression_goal = campaign_impression_goal | |
| self.refresh_rate = refresh_rate | |
| self.total_cycles = total_cycles | |
| # variable to hold how many cycles has elapsed | |
| self.cycles_so_far = 0 | |
| # optimal rate is the the rate at which distribution would be uniform | |
| self.optimal_rate = 1.0*self.campaign_impression_goal/self.total_cycles | |
| # flag to mark the first cycle to output all the impressions |
December 18, 2024
What is the Universal Approximation Theorem?
The Universal Approximation Theorem (UAT) states that a feedforward neural network with a single hidden layer, using a suitable activation function, can approximate any continuous function defined on a compact domain (like the unit cube
Formal Statement:
This document outlines essential prompting techniques for leveraging zero-shot learning capabilities of Large Language Models (LLMs). These methods allow you to perform a wide variety of tasks without requiring prior task-specific training data.
Describe the task or concept in clear, natural language so the model understands what to do.
| # Resolution - Definitions | |
| 1. **The brain's inherent ability to analyze data at different resolutions is remarkable.** | |
| 2. **The problem of optimizing data compression** is best suited for attaining intelligence. | |
| 3. Transitioning **from material to intelligence** allows us to put **life into inorganic material** | |
| (through the whole architecture of computation → Neural Networks → LLMs → Intelligence). | |
| 4. **Predicting the future** is the name of the game; each brain does exactly that. |
Jan 7,2025
In the landscape of modern science, few principles have proven as universally powerful as Emmy Noether's Theorem. Published in 1918, this remarkable insight connects symmetries in physical systems to their invariances (conservation laws). Today, over a century later, we're discovering that these same principles govern not just the physical world, but also the behavior of artificial neural networks and deep learning systems.
This comprehensive exploration will bridge the gap between classical physics and cutting-edge artificial intelligence, revealing how Noether's insights illuminate both fields. We'll begin with fundamental mathematical principles, progress through classical applications, and ultimately reveal how these same concepts manifest in modern deep learning architectures.
Leveraging your existing software engineering skills to build production AI applications
Goal: Get your development environment ready
