Forward Pass: Here the input data is passed through the neural network to obtain the predicted output. In forward pass, First calculate the output of the hidden layer. hidden_output = X•W1 + b1 Then apply the sigmoid activation to the output. output = sigmoid( (X•W1) + b1)
MACHINE LEARNING
-
Building a Simple 2-Layer Neural Network with Sigmoid Activation
By
–
Below is the simple Neural Network consists of 2 layers: – Hidden Layer
– Output Layer First Initialize the size of layers along with the weights & biases. And also define the sigmoid activation function & it's derivative which is really key to introduce non-linearity. -
Neural Network Implementation from Scratch in Python
By
–
Neural Network implemented from scratch in Python (step-by-step explanation with code):
-
AI Machine Learning forecasting election results overview
By
–
Nice overview of applying AI Machine Learning & Data Science towards election result forecasting
-
LLMs sometimes claim 9.11 > 9.9 with specific prompts
By
–
To clarify: I'm not saying any LLM consistently acts like it believes 9.11 > 9.9 no matter how you prompt it. I'm saying if you prompt in this specific way, many leading models will tell you 9.11 > 9.9 and that's weird. If you want to repro, pls paste prompt from the alt text.
-
Indexify: New AI Data Processing and Indexing Platform
By
–
Announcing Indexify https://
bit.ly/3zD4szc
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Training stability control with loss and gradient norm thresholding
By
–
Cool! For the spike I'd try e.g. `-sl 7 -sg 7` to keep instability in check earlier in the training. (will skip update if loss/gradnorm > 7 sigma outlier is detected)
-
Train Small Language Models Efficiently with Synthetic Data
By
–
Want to train a #small language model for your use case, but short on data and training #infra? Good news: Predibase + @gretel_ai make it easy to create high-quality #synth data and #finetune + serve #SLMs, cost efficiently. Check out our tutorial: https://
pbase.ai/3WnfEc7 -
Rio Yokota Presents Neural Scaling Laws Research at ICML
By
–
Sign up now to attend to hear Rio Yokota present at the 7th workshop on Neural Scaling Laws at ICML 2024 Rio from the Tokyo Institute of Technology will present research on continual pre-training of open-source models on Japanese text. July 22, 2024, 9:00AM Local Time
-
Q* Strawberry Algorithm Research Findings and Leaked Papers
By
–
El directo ya lo tenéis resubido. Hemos hablado de 2 artículos con filtraciones y 3 papers que apuntarían sobre la línea de investigación del famoso algoritmo Q* (ahora bautizado Strawberry)