kbrajwani

import keras
import numpy as np
from keras import backend as K
import tensorflow as tf
from tensorflow.python.keras.backend import set_session
from keras.applications import vgg16


def get_session():
    config = tf.ConfigProto()

kbrajwani

STATIC_URL = '/static/'
STATIC_ROOT = os.path.join(BASE_DIR, 'static')


MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
MEDIA_URL = 'media/'

kbrajwani

from django.shortcuts import render
from django.http import JsonResponse
import base64
from django.core.files.base import ContentFile
from django.core.files.storage import default_storage
from django.conf import settings 
from tensorflow.python.keras.backend import set_session
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications.imagenet_utils import decode_predictions

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<form method="post" enctype="multipart/form-data">
    {% csrf_token %}
    <input type="file" name="sentFile" />
    <input type="submit" name="submit" value="Upload" />
</form>
{{name}}

kbrajwani

from django.contrib import admin
from django.urls import path
from . import views
urlpatterns = [
    path('', views.index, name='homepage'),
    path('admin/', admin.site.urls),
]

kbrajwani

language: en
pipeline:
- name: entity_mapping.SpellChecker
- name: nlp_spacy
  model: en_core_web_md
  case_sensitive: false
- name: tokenizer_spacy
- name: ner_crf
- name: intent_featurizer_spacy
- name: intent_classifier_sklearn

kbrajwani

Sathyajith Bhat - Practical Docker with Python_ Build, Release and Distribute Your Python App with

https://augmentedstartups.com/category/artificial-intelligence/

https://yanjia.li/dive-really-deep-into-yolo-v3-a-beginners-guide/

https://towardsdatascience.com/knowledge-distillation-and-the-concept-of-dark-knowledge-8b7aed8014ac

https://towardsdatascience.com/3-steps-to-update-parameters-of-faster-r-cnn-ssd-models-in-tensorflow-object-detection-api-7eddb11273ed

kbrajwani

https://towardsdatascience.com/distilling-bert-how-to-achieve-bert-performance-using-logistic-regression-69a7fc14249d

https://towardsdatascience.com/knowledge-distillation-and-the-concept-of-dark-knowledge-8b7aed8014ac

https://blog.floydhub.com/knowledge-distillation/

kbrajwani

--------------------------------------metric explain-----------------------------
A great metric that should always be used when dealing with the classification problem is the confusion matrix.

The accuracy of the model is basically the total number of correct predictions divided by the total number of predictions.

The precision of a class defines how trustable is the result when the model answers that a point belongs to that class.

The recall of a class expresses how well the model is able to detect that class.

The F1 score of a class is given by the harmonic mean of precision and recall (2×precision×recall / (precision + recall)), it combines precision and recall of a class in one metric.

kbrajwani

https://www.kaggle.com/bamps53/private0-9704-tpu-keras-metric-learning/data
https://www.kaggle.com/c/bengaliai-cv19/discussion/136030
https://www.kaggle.com/c/bengaliai-cv19/discussion/136815
https://www.kaggle.com/c/bengaliai-cv19/discussion/135984
https://www.kaggle.com/haqishen/notebooks
https://www.kaggle.com/haqishen/train-efficientnet-b0-w-36-tiles-256-lb0-87
https://www.kaggle.com/c/humpback-whale-identification/discussion/82366
https://www.kaggle.com/c/humpback-whale-identification/discussion/82352
https://www.kaggle.com/nroman/melanoma-pytorch-starter-efficientnet
https://www.kaggle.com/iafoss/panda-concat-tile-pooling-starter-0-79-lb