deeplearninginference.app

#Deep LearnIng Inference Application Meta

#Convolutional Neural Network

#Artificial Intelligence

#Machine Learning

#Artificial Neural Network

#Image Analysis

#Shift Invariant Neural Network

#Shared Weight Architecture

#Convolutional Kernel

#Filters

#Feature Map

#Image Recognition

#Video Recognition

#Remommender System

#Image Classification

#Image Segmentation

#Media Image Analysis

#Natural Language Processing

#Brain Computer Interface

#Financial Time Series

#Multilayer Perceptron

#Fully Connected Networks

#One Neuron Connected To All Layers In The Next Layer

#Over Fitting Data

#Penalizing Parameters

#Weight Decay

#Skipped Connections

#Hierarchical Pattern Of Data

#Biological Process

#Animal Visual Cortex

#Cortical Neuron

#Receptive Field

#Perceptual AI

#Edge AI

#Token

#Fine-tuning

#AI model

#Tokenization

#Speech to text

#Text classification

#Sentiment

#Semantic similarity

#Semantic search

#Part of Speech tagging

#Named Entity Recognition

#Intent classification | Intent detection | Intent recognition

#Summarization

#Code Generation

#Training Convolutional Neural Networks (CNN)

#Error surface learning

#Gradient-based learning

#Hyperparameters

#Loss Functions

#Text-to-image diffusion model

#Idiosyncratic prompt

#Prompt alignment

#Direct reward fine-tuning (DRaFT)

#Differentiable reward function

#Complex prompt

#DRaFT method

#DRaFT+ algorithm

#Custom generative AI

#Training

#Layer and tensor fusion

#Retrieval-augmented generation

#Guardrailing

#Data curation

#Pretrained model

#Reinforcement learning from human feedback (RLHF)

#Large language model (LLM)

#Generative text-to-image

#Reinforcement learning (RL)

#Prompt domain

#Backpropagating differentiable reward through diffusion process

#Over-optimization

#Mode collapse

#Script

#Deep learning algorithm

#Model alignment

#Workflows for GenAI models

#Deep generative learning

#Weakly supervised learning

#Neural network

#Prompt engineering

#Quantization

#Vision-Language Model (VLM)

#Deep neural network

#Vectorized neural network

#Deep-learning framework

#Pre-training method

#Fine-tuning method

#Fine-tuning 2D model on 3D scans

#SLice Integration by Vision Transformer (SLIViT)

#Downstream learning

#4D deep learning model

#A-list celebrity home protector | Burglaries targeting high-end items | Burglary report on Lime Orchard Road | Burglar had smashed glass door of residence | Ransacked home and fled | Couple were not home at the time | Unknown whether any items were taken | Lime Orchard Road is within Hidden Valley gated community of Los Angeles in Beverly Hills | Penelope Cruz, Cameron Diaz, Jennifer Lawrence, Adele and Katy Perry have purchased homes there, in addition to Kidman and Urban | Kidman and Urban bought their home for $4.7 million in 2008 | 4,100-square-foot, five-bedroom home built in 1965 and sits on 1¼-acre lot | Property large windows have views of the canyons | Theirs is one of several celebrity properties burglarized in Los Angeles and across country recently | Connected to South American organized-theft rings

#Professional athlete home protector | South American crime rings | Targeting wealthy Southern California neighborhoods for sophisticated home burglaries | Behind burglaries at homes of professional athletes and celebrities | Theft groups conduct extensive research before plotting burglaries | Monitoring target whereabouts and weekly routines via social media | Tracking travel and schedules | Conducting physical surveillance at homes | Attacks staged while targets and their families are away | Robbers aware of where valuables are stored in homes prior to staging break-ins | Burglaries conducted in short amount of time | Bypass alarm systems | Use Wi-Fi jammers to block Wi-Fi connections | Disable devices | Cover security cameras | Obfuscate identities

#27B parameter model | Google Gemma 2 | High-performing lightweight language model | Designed for efficiency and versatility | Part of Gemma family | Available in three sizes: 2B, 9B, and 27B parameters | 27B variant trained on 13 trillion tokens (web documents, code, and mathematics | Excels in text generation, summarization, reasoning