Natural Language Processing using JavaScript "Natural" Library. This deck covers Natural Language Understanding using JavaScript "Natural" library in detail
How to fine-tune and develop your own large language model.pptxKnoldus Inc.
In this session, we will what are large language models, how we can fin-tune a pre-trained LLM with our data, including data preparation, model training, model evaluation.
The document provides an introduction to natural language processing (NLP), discussing key related areas and various NLP tasks involving syntactic, semantic, and pragmatic analysis of language. It notes that NLP systems aim to allow computers to communicate with humans using everyday language and that ambiguity is ubiquitous in natural language, requiring disambiguation. Both manual and automatic learning approaches to developing NLP systems are examined.
Improved Security Proof for the Camenisch- Lysyanskaya Signature-Based Synchr...MASAYUKITEZUKA1
1. The document presents an improved security proof for the Camenisch-Lysyanskaya signature-based synchronized aggregate signature scheme.
2. It describes the modified Camenisch-Lysyanskaya signature used in the security proof for the Lee-Lee-Yung synchronized aggregate signature scheme. The modified signature replaces the original interactive assumption with the non-interactive 1-MSDH assumption.
3. The authors provide an overview of their security proof, which uses a simulator to convert a signature adversary against the synchronized aggregate signature into a forger against the underlying modified Camenisch-Lysyanskaya signature. This establishes the security of the synchronized aggregate signature under the 1-
- GPT-3 is a large language model developed by OpenAI, with 175 billion parameters, making it the largest neural network ever created at the time.
- GPT-3 is trained on a massive dataset of unlabeled text using an auto-regressive approach, allowing it to perform tasks without any fine-tuning through zero-, one-, or few-shot learning by conditioning on examples or instructions.
- Evaluation showed GPT-3 outperforming state-of-the-art models on several benchmarks in zero- and few-shot settings, demonstrating strong generalization abilities from its massive pre-training.
Adria Recasens, DeepMind – Multi-modal self-supervised learning from videosCodiax
The document summarizes a talk on multi-modal self-supervised learning from videos. It discusses using multiple modalities like vision, audio and language from videos for self-supervised learning. It presents two models: 1) A Multi-Modal Versatile network that can take any modality as input and respects the specificity of each while enabling comparison. 2) BraVe which learns representations by regressing a broad representation of the whole video from a narrow view to leverage different augmentations and modalities. Both models achieve state-of-the-art results on downstream tasks, showing videos provide rich self-supervision and using additional context improves representation learning.
Natural Language Processing using JavaScript "Natural" Library. This deck covers Natural Language Understanding using JavaScript "Natural" library in detail
How to fine-tune and develop your own large language model.pptxKnoldus Inc.
In this session, we will what are large language models, how we can fin-tune a pre-trained LLM with our data, including data preparation, model training, model evaluation.
The document provides an introduction to natural language processing (NLP), discussing key related areas and various NLP tasks involving syntactic, semantic, and pragmatic analysis of language. It notes that NLP systems aim to allow computers to communicate with humans using everyday language and that ambiguity is ubiquitous in natural language, requiring disambiguation. Both manual and automatic learning approaches to developing NLP systems are examined.
Improved Security Proof for the Camenisch- Lysyanskaya Signature-Based Synchr...MASAYUKITEZUKA1
1. The document presents an improved security proof for the Camenisch-Lysyanskaya signature-based synchronized aggregate signature scheme.
2. It describes the modified Camenisch-Lysyanskaya signature used in the security proof for the Lee-Lee-Yung synchronized aggregate signature scheme. The modified signature replaces the original interactive assumption with the non-interactive 1-MSDH assumption.
3. The authors provide an overview of their security proof, which uses a simulator to convert a signature adversary against the synchronized aggregate signature into a forger against the underlying modified Camenisch-Lysyanskaya signature. This establishes the security of the synchronized aggregate signature under the 1-
- GPT-3 is a large language model developed by OpenAI, with 175 billion parameters, making it the largest neural network ever created at the time.
- GPT-3 is trained on a massive dataset of unlabeled text using an auto-regressive approach, allowing it to perform tasks without any fine-tuning through zero-, one-, or few-shot learning by conditioning on examples or instructions.
- Evaluation showed GPT-3 outperforming state-of-the-art models on several benchmarks in zero- and few-shot settings, demonstrating strong generalization abilities from its massive pre-training.
Adria Recasens, DeepMind – Multi-modal self-supervised learning from videosCodiax
The document summarizes a talk on multi-modal self-supervised learning from videos. It discusses using multiple modalities like vision, audio and language from videos for self-supervised learning. It presents two models: 1) A Multi-Modal Versatile network that can take any modality as input and respects the specificity of each while enabling comparison. 2) BraVe which learns representations by regressing a broad representation of the whole video from a narrow view to leverage different augmentations and modalities. Both models achieve state-of-the-art results on downstream tasks, showing videos provide rich self-supervision and using additional context improves representation learning.
MARIO LODI: La testimonianza di un MaestroGiorgio Spano
Conferenza di Mario Lodi
Treviglio - 18 marzo 2007
“Il maestro Mario Lodi non solo ha conosciuto personalmente don Lorenzo Milani, ma ha collaborato con lui nel portare avanti un modo innovativo di fare scuola”
schematizzazione riassuntiva:
- principi fondamentali della Filosofia di Hegel
- la Fenomenologia dello spirito
- L'enciclopedia delle scienze profonde
- La filosofia della storia
by Spano
schematizzazione riassuntiva della vita e dell'opera ab urbe condita. tratte dal libro "corso integrato di LETTERATURA LATINA. 3. L'età di Augusto" di Conte e Pianezzola.
by Spano
1. Giorgio Spano
NUCLEI DEI NERVI CRANICI
Nuclei dei nervi cranici formano 7 colonne cellulari longitudinali.
• Colonne motorie somatiche e viscerali occupano una posizione mediale.
• Colonne sensitive somatiche e viscerali occupano una posizione più laterale.
Colonne motrici Nuclei
1^ colonna. Vicino alla
linea mediana.
4 nuclei motori:
3 per il controllo degli occhi:
- Oculomotore (III – mesencefalo rostrale)
- Trocleare (IV – mesencefalo caudale)
- Abducente (VI – ponte)
Il quarto per il controllo della lingua: Ipoglosso (XII – nel bulbo)
2^ colonna motoria 4 nuclei:
- Trigemino (V – rostralmente nel ponte): controlla i
muscoli masticatori.
- Facciale (VII – più ventralmente nel ponte): controlla
muscoli mimici della faccia.
- Glossofaringei (IX)
- Vago (X) situati nel bulbo. Tendono a fondersi
costituendo il nucleo ambiguo. Controlla muscoli striati
del laringe e del faringe.
- Accessorio spinale (XI – più caudalmente): innerva
muscoli rotatori del collo.
3^ colonna neuronale
(motrice viscerale)
- Di Edinger-Wetsphal le cui fibre seguono il decorso del III
nervo. Terminano nel ganglio ciliare. Controllo del
muscolo costrittore della pupilla.
2 nuclei parasimpatici:
- Salivatorio superiore (le fibre seguono il decorso del VII)
- Salivatorio inferiore (assoni si associano a quelli del IX)
Le fibre di questi due nuclei terminano nei gangli parasimpatici
del capo andando poi, per mezzo di fibre post gangliari, ad
innervare le ghiandole salivari e lacrimali.
- Motore dorsale del vago, innerva i gangli parasimpatici
di visceri toracici e addominali (cuore, polmone,
intestino)
SOLCO LIMITANTE
COLONNE SENSITIVE
Dopo il solco limitante si
trovano le colonne
sensitive viscerale generale
e viscerale speciale
4^ colonna viscerale
generale. (porzione
caudale del tratto solitario)
- Nucleo del tratto solitario. Riceve fibre da gangli sensitivi
viscerali posti fuori del bulbo (fibre associate ai nervi VII,
IX, X).
Le info viscerali da faringe, laringe, cuore polmone, intestino
vengono trasmesse dal nucleo solitario nuclei motori viscerali
(per mediare i riflessi vegetativi) o a centri superiori del sistema
limbico per la regolazione del sistema autonomo.
5^ colonna viscerale
speciale (porzione rostrale
del nucleo solitario)
- Nucleo del tratto solitario (regione rostrale) riceve
informazioni gustative dalle fibre dei nervi VII, IX, X
(innervano i bottoni gustativi della lingua). da questo
nucleo vengono trasmesse alla porzione mediale del
complesso ventrobasale del talamo. neuroni talamici
trasferiscono queste info alla corteccia cerebrale.
2. Giorgio Spano
6^ colonna sensitiva
somatica speciale (più
lateralmente nella parte
rostrale del bulbo e
caudale del ponte.
- Vestibolococleare (VIII). Riceve afferenze uditive dai
neuroni di un ganglio i cui assoni decorrono nella
componente cocleare del nervo VIII e proiettano fasci
ascendenti al talamo. Queste informazioni che ci rendono
consapevoli della posizione assoluta del capo nello
spazio, sono accomunate con informazioni muscolari e
articolari e vengono definite Propriocettive.
7^ colonna sensitiva
somatica generale (colonna
più laterale)
3 sezioni nel nucleo sensitivo del nervo trigemino (V):
- Mesencefalico: sez. più rostrale. Riceve afferenza da
muscoli masticatori e dall’articolazione della mandibola e
proietta al nucleo MOTORE del V nervo, mediando così i
riflessi propriocettivi mandibolari.
- Le altre due sezioni rappresentano il punto di arrivo di
informazioni tattili, termodolorifiche e propriocettive
provenienti dalla faccia e dalla mucosa orale trasportate
dalle cellule del ganglio di Gasser. Questi nuclei
proiettano poi le informazioni al talamo.