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Perché TensorFlow

Che tu sia un esperto o un principiante, TensorFlow è una piattaforma end-to-end che semplifica la creazione e la distribuzione di modelli ML.

Un intero ecosistema per aiutarti a risolvere problemi complessi e reali con il machine learning

Facile costruzione del modello

TensorFlow offre più livelli di astrazione, così puoi scegliere quello giusto per le tue esigenze. Costruisci e addestra modelli utilizzando l'API Keras di alto livello, che semplifica l'utilizzo di TensorFlow e del machine learning.

Se hai bisogno di maggiore flessibilità, l'esecuzione entusiasta consente un'iterazione immediata e un debug intuitivo. Per attività di formazione ML di grandi dimensioni, utilizza l'API della strategia di distribuzione per la formazione distribuita su diverse configurazioni hardware senza modificare la definizione del modello.

Produzione ML robusta ovunque

TensorFlow ha sempre fornito un percorso diretto alla produzione. Che si tratti di server, dispositivi edge o Web, TensorFlow ti consente di addestrare e distribuire facilmente il tuo modello, indipendentemente dal linguaggio o dalla piattaforma utilizzata.

Utilizza TFX se hai bisogno di una pipeline ML di produzione completa. Per eseguire l'inferenza su dispositivi mobili e periferici, utilizza TensorFlow Lite. Addestra e distribuisci modelli in ambienti JavaScript utilizzando TensorFlow.js.

Potente sperimentazione per la ricerca

Costruisci e addestra modelli all'avanguardia senza sacrificare velocità o prestazioni. TensorFlow ti offre flessibilità e controllo con funzionalità come l'API funzionale Keras e l'API di sottoclasse dei modelli per la creazione di topologie complesse. Per una prototipazione semplice e un debug veloce, utilizza l'esecuzione impaziente.

TensorFlow supporta inoltre un ecosistema di potenti librerie aggiuntive e modelli con cui sperimentare, tra cui Ragged Tensors, TensorFlow Probability, Tensor2Tensor e BERT.

Scopri come le aziende utilizzano TensorFlow

Airbnb
Coca Cola
Mente profonda
GE Sanità
Google
Intel
NERSC
Twitter

Scopri come funziona l'apprendimento automatico

Hai mai desiderato sapere come funziona una rete neurale? O quali sono i passaggi per risolvere un problema di ML? Non preoccuparti, ci pensiamo noi. Di seguito è riportata una rapida panoramica dei fondamenti del machine learning. Oppure, se stai cercando informazioni più approfondite, vai alla nostra pagina di formazione per contenuti per principianti e avanzati.

Introduzione al machine learning

L'apprendimento automatico è la pratica di aiutare il software a eseguire un'attività senza programmazione o regole esplicite. Con la programmazione informatica tradizionale, un programmatore specifica le regole che il computer dovrebbe utilizzare. Tuttavia, il machine learning richiede una mentalità diversa. Il machine learning nel mondo reale si concentra molto più sull'analisi dei dati che sulla codifica. I programmatori forniscono una serie di esempi e il computer apprende i modelli dai dati. Puoi pensare al machine learning come alla “programmazione con i dati”.

Passaggi per risolvere un problema di machine learning

Esistono più passaggi nel processo per ottenere risposte dai dati utilizzando il machine learning. Per una panoramica dettagliata, consulta questa guida che mostra il flusso di lavoro completo per la classificazione del testo e descrive passaggi importanti come la raccolta di un set di dati, l'addestramento e la valutazione di un modello con TensorFlow.

Anatomia di una rete neurale

Una rete neurale è un tipo di modello che può essere addestrato a riconoscere modelli. È composto da livelli, inclusi livelli di input e output, e almeno un livello nascosto . I neuroni in ogni strato apprendono rappresentazioni sempre più astratte dei dati. Ad esempio, in questo diagramma visivo vediamo i neuroni che rilevano linee, forme e trame. Queste rappresentazioni (o caratteristiche apprese) consentono di classificare i dati.

Addestrare una rete neurale

Le reti neurali vengono addestrate mediante la discesa del gradiente. I pesi in ogni livello iniziano con valori casuali e questi vengono migliorati in modo iterativo nel tempo per rendere la rete più accurata. Viene utilizzata una funzione di perdita per quantificare quanto sia imprecisa la rete e una procedura chiamata backpropagation viene utilizzata per determinare se ciascun peso deve essere aumentato o diminuito per ridurre la perdita.

La nostra comunità

La community di TensorFlow è un gruppo attivo di sviluppatori, ricercatori, visionari, armeggiatori e risolutori di problemi. La porta è sempre aperta per contribuire, collaborare e condividere le tue idee.

,

Perché TensorFlow

Che tu sia un esperto o un principiante, TensorFlow è una piattaforma end-to-end che semplifica la creazione e la distribuzione di modelli ML.

Un intero ecosistema per aiutarti a risolvere problemi complessi e reali con il machine learning

Facile costruzione del modello

TensorFlow offre più livelli di astrazione, così puoi scegliere quello giusto per le tue esigenze. Costruisci e addestra modelli utilizzando l'API Keras di alto livello, che semplifica l'utilizzo di TensorFlow e del machine learning.

Se hai bisogno di maggiore flessibilità, l'esecuzione entusiasta consente un'iterazione immediata e un debug intuitivo. Per attività di formazione ML di grandi dimensioni, utilizza l'API della strategia di distribuzione per la formazione distribuita su diverse configurazioni hardware senza modificare la definizione del modello.

Produzione ML robusta ovunque

TensorFlow ha sempre fornito un percorso diretto alla produzione. Che si tratti di server, dispositivi edge o Web, TensorFlow ti consente di addestrare e distribuire facilmente il tuo modello, indipendentemente dal linguaggio o dalla piattaforma utilizzata.

Utilizza TFX se hai bisogno di una pipeline ML di produzione completa. Per eseguire l'inferenza su dispositivi mobili e periferici, utilizza TensorFlow Lite. Addestra e distribuisci modelli in ambienti JavaScript utilizzando TensorFlow.js.

Potente sperimentazione per la ricerca

Costruisci e addestra modelli all'avanguardia senza sacrificare velocità o prestazioni. TensorFlow ti offre flessibilità e controllo con funzionalità come l'API funzionale Keras e l'API di sottoclasse dei modelli per la creazione di topologie complesse. Per una prototipazione semplice e un debug veloce, utilizza l'esecuzione entusiasta.

TensorFlow supporta inoltre un ecosistema di potenti librerie aggiuntive e modelli con cui sperimentare, tra cui Ragged Tensors, TensorFlow Probability, Tensor2Tensor e BERT.

Scopri come le aziende utilizzano TensorFlow

Airbnb
Coca Cola
Mente profonda
GE Sanità
Google
Intel
NERSC
Twitter

Scopri come funziona l'apprendimento automatico

Hai mai desiderato sapere come funziona una rete neurale? O quali sono i passaggi per risolvere un problema di ML? Non preoccuparti, ci pensiamo noi. Di seguito è riportata una rapida panoramica dei fondamenti del machine learning. Oppure, se stai cercando informazioni più approfondite, vai alla nostra pagina di formazione per contenuti per principianti e avanzati.

Introduzione al machine learning

L'apprendimento automatico è la pratica di aiutare il software a eseguire un'attività senza programmazione o regole esplicite. Con la programmazione informatica tradizionale, un programmatore specifica le regole che il computer dovrebbe utilizzare. Tuttavia, il machine learning richiede una mentalità diversa. Il machine learning nel mondo reale si concentra molto più sull'analisi dei dati che sulla codifica. I programmatori forniscono una serie di esempi e il computer apprende i modelli dai dati. Puoi pensare al machine learning come alla “programmazione con i dati”.

Passaggi per risolvere un problema di machine learning

Esistono più passaggi nel processo per ottenere risposte dai dati utilizzando il machine learning. Per una panoramica dettagliata, consulta questa guida che mostra il flusso di lavoro completo per la classificazione del testo e descrive passaggi importanti come la raccolta di un set di dati, l'addestramento e la valutazione di un modello con TensorFlow.

Anatomia di una rete neurale

Una rete neurale è un tipo di modello che può essere addestrato a riconoscere modelli. È composto da livelli, inclusi livelli di input e output, e almeno un livello nascosto . I neuroni in ogni strato apprendono rappresentazioni sempre più astratte dei dati. Ad esempio, in questo diagramma visivo vediamo i neuroni che rilevano linee, forme e trame. Queste rappresentazioni (o caratteristiche apprese) consentono di classificare i dati.

Addestrare una rete neurale

Le reti neurali vengono addestrate mediante la discesa del gradiente. I pesi in ogni livello iniziano con valori casuali e questi vengono migliorati in modo iterativo nel tempo per rendere la rete più accurata. Viene utilizzata una funzione di perdita per quantificare quanto sia imprecisa la rete e una procedura chiamata backpropagation viene utilizzata per determinare se ciascun peso deve essere aumentato o diminuito per ridurre la perdita.

La nostra comunità

La community di TensorFlow è un gruppo attivo di sviluppatori, ricercatori, visionari, armeggiatori e risolutori di problemi. La porta è sempre aperta per contribuire, collaborare e condividere le tue idee.