* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.

* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Piensa en una enorme hoja de cálculo con números y multiplicando todos juntos al mismo tiempo.

### Major Features and Improvements

*   Added F-Score metrics `tf.keras.metrics.FBetaScore`, `tf.keras.metrics.F1Score`, and `tf.keras.metrics.R2Score`.
*   Added activation function `tf.keras.activations.mish`.

				MetricV2{
					Description: getMinioProcessIOReadBytesMD(),
					Value:       float64(io.ReadBytes),
				})
		}

		if io.WriteBytes > 0 {
			metrics = append(metrics,
				MetricV2{
					Description: getMinioProcessIOWriteBytesMD(),
					Value:       float64(io.WriteBytes),
				})
		}

		if io.RChar > 0 {
			metrics = append(metrics,
				MetricV2{

	}

	metrics := getHistogramMetrics(ttfbHist, getBucketTTFBDistributionMD(), false, false)
	// additional labels for +Inf for all histogram metrics
	if expPoints := len(labels) * (len(histBuckets) + 1); expPoints != len(metrics) {
		t.Fatalf("Expected %v data points when toLowerAPILabels=false but got %v", expPoints, len(metrics))
	}

	name := rm.Name
	metrics := make([]MetricV2, 0, 3)
	metrics = append(metrics, MetricV2{
		Description:    getResourceMetricDescription(subSys, name, help),
		Value:          rm.Current,
		VariableLabels: cloneMSS(rm.Labels),
	})

	if requireAvgMax {
		avgName := MetricName(fmt.Sprintf("%s_avg", name))
		avgHelp := fmt.Sprintf("%s (avg)", help)
		metrics = append(metrics, MetricV2{

// server lists all metrics that could be returned for the requested path.
//
// The (repeatable) `buckets` query parameter is a list of bucket names (or it
// could be a comma separated value) to return metrics with a bucket label.
// Bucket metrics will be returned only for the provided buckets. If no buckets
// parameter is provided, no bucket metrics are returned.

	// Queued operations
	Queued InQueueMetric `json:"queued"`
	// Proxy stats
	Proxied ProxyMetric `json:"proxied"`
	// replication metrics summary for each site replication peer
	Metrics map[string]SRMetric `json:"replMetrics"`
	// uptime of node being queried for site replication metrics
	Uptime int64 `json:"uptime"`

import (
	"context"
	"sync"
	"time"

	"github.com/minio/madmin-go/v3"
	"github.com/minio/minio/internal/cachevalue"
)

// metricsCache - cache for metrics.
//
// When serving metrics, this cache is passed to the MetricsLoaderFn.
//
// This cache is used for metrics that would result in network/storage calls.
type metricsCache struct {
	dataUsageInfo       *cachevalue.Cache[DataUsageInfo]

		}
		return nil
	}
}

// MetricsGroup - represents a group of metrics. It includes a `MetricsLoaderFn`
// function that provides a way to load the metrics from the system. The metrics
// are cached and refreshed after a given timeout.
//
// For metrics with a `bucket` dimension, a list of buckets argument is required
// to collect the metrics.
//
// It implements the prometheus.Collector interface for metric groups without a