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Flower dataset

Context. This dataset belongs to DPhi Data Sprint #25: Flower Recognition.The dataset contains raw jpeg images of five types of flowers. daisy; dandelion; rose; sunflower; tulip; Content. train - contains all the images that are to be used for training your model. In this folder you will find five folders namely - 'daisy', 'dandelion', 'rose', 'sunflower' and 'tulip' which contain the images. Maria-Elena Nilsback and Andrew Zisserman Overview. We have created a 17 category flower dataset with 80 images for each class. The flowers chosen are some common flowers in the UK. The images have large scale, pose and light variations and there are also classes with large varations of images within the class and close similarity to other classes Overview. We have created a 102 category dataset, consisting of 102 flower categories. The flowers chosen to be flower commonly occuring in the United Kingdom. Each class consists of between 40 and 258 images. The details of the categories and the number of images for each class can be found on this category statistics page Context. The Iris flower data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species The Oxford Flowers 102 dataset is a consistent of 102 flower categories commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images. The images have large scale, pose and light variations. In addition, there are categories that have large variations within the category.

[R] – xgboost | Machine Learning, Deep Learning, AI

Context. This dataset contains 4242 images of flowers. The data collection is based on the data flicr, google images, yandex images. You can use this datastet to recognize plants from the photo. Content. The pictures are divided into five classes: chamomile, tulip, rose, sunflower, dandelion

Flowers Dataset Kaggl

Classify flowers from the image Description from the authors: We have created a 17 category flower dataset with 80 images for each class. The flowers chosen are some common flowers in the UK. The images have large scale, pose and light variations and there are also classes with large variations of images within the class and close similarity to other classes

17 Category Flower Dataset - University of Oxfor

102 Category Flower Dataset - University of Oxfor

Description: The Oxford Flowers 102 dataset is a consistent of 102 flower categories commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images. The images have large scale, pose and light variations. In addition, there are categories that have large variations within the category and several very similar categories The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of.

The flowers dataset. The flowers dataset consists of images of flowers with 5 possible class labels. When training a machine learning model, we split our data into training and test datasets. We will train the model on our training data and then evaluate how well the model performs on data it has never seen - the test set Computer Vision group from the University of Oxford. This website uses Google Analytics to help us improve the website content. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request TFDS is a collection of datasets ready to use with TensorFlow, Jax, - datasets/flowers.py at master · tensorflow/datasets Download the flowers dataset. This tutorial uses a dataset of several thousand photos of flowers. The flowers dataset contains 5 sub-directories, one per class: flowers_photos/ daisy/ dandelion/ roses/ sunflowers/ tulips/ Note: all images are licensed CC-BY, creators are listed in the LICENSE.txt file Shape statistic for all images. Before I trained Dogs and Cats Dataset with 64x64 input and the result made sense for more than 0.9 accuracy. For this flowers dataset, by using the customed pre.

Download the Flowers Dataset using TensorFlow Datasets. tfds.load () Loads the named dataset into a tf.data.Dataset. We are do w nloading the tf_flowers dataset. This dataset is only split into a. in this video. we will develop a model with flowers dataset using conventional neural network (CNN) and also with Dense neural network (DNN), (just to compa.. 1.2 Data frames contain rows and columns: the Iris flower dataset. In 1936, Edgar Anderson collected data to quantify the geographic variations of Iris flowers.The data set consists of 50 samples from each of the three sub-species ( Iris setosa, Iris virginica, and Iris versicolor).Four features were measured in centimeters (cm): the lengths and the widths of both sepals and petals The Iris flower data set or Fisher's Iris data set is one of the most famous multivariate data set used for testing various Machine Learning Algorithms. This is my version of EDA on Iris Dataset Tutorial 5: Cross-Validation on Tensorflow Flowers Dataset. Prerequisite: Tutorial 0 (setting up Google Colab, TPU runtime, and Cloud Storage) M any deep learning tutorials provide two datasets.

Iris Flower Dataset Kaggl

  1. The flower dataset can be downloaded using the keras sequential API with the help of google API that stores the dataset. The 'get_file' method is used with the API (URL) to fetch the dataset, and store it in memory. Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks? A neural network that contains at least one layer is known as a convolutional layer
  2. g task. You can visit the links provided at the bottom of this post where I have collected all the publicly available plant/flower datasets around the world. Although traning a machine with these dataset might help in some scenerios, there are still more problems to be solved. Millions of plant.
  3. Iris Datasets Iris is a family of flower which contains three type of flower called setosa , versicolor and Virginica. Problem: The problem is that, we have given some features of a flower, and based on these features we have to identify which flower belongs to which category
  4. About the flowers dataset. The dataset is organised in 5 folders. Each folder contains flowers of one kind. The folders are named sunflowers, daisy, dandelion, tulips and roses. The data is hosted in a public bucket on Google Cloud Storage. Excerpt
  5. The Iris Dataset is a multivariate dataset about flowers introduced by Ronald Fisher in his 1936 Paper. We can use it to classify iris flowers among three species (setosa, versicolor, or virginica) from measurements of sepals and petals' length and width. The iris data set contains 3 classes of 50 instances each, where each class refers to a.
  6. The orchid flower dataset was selected from the northern part of Thailand. The dataset contains Thai native orchid flowers, and each class contains at least 20 samples. The orchid dataset including 52 species and the visual characteristics of the flower are varying in terms of shape, color, texture, size, and the other parts of the orchid plant like a leaf, inflorescence, roots, and surroundings
  7. Flowers. class paddlehub.datasets.Flowers(transform: Callable, mode: str = 'train'): Flower classification dataset. Args: transform (Callable) The method of preprocess images. mode (str) The mode for preparing dataset (train, test or val). Default to 'train'

oxford_flowers102 TensorFlow Dataset

  1. Iris Flowers Dataset. The Iris Flowers Dataset involves predicting the flower species given measurements of iris flowers. It is a multi-class classification problem. The number of observations for each class is balanced. There are 150 observations with 4 input variables and 1 output variable
  2. 위에서 dataset은 포스팅대로 진행 후 python train_image_classifier.py -train_dir=\tmp\flowers_logs -dataset_name=flowers -dataset_split_name=train -dataset_dir=\tmp\flowers -batch_size=32 -model_name=mobilenet_v1 -max_number_of_step=15200 -learning_rate=0.001 -learning_rate_decay_type=fixed -optimizer=adam.
  3. g language
  4. VGG Flower (dataset of flower images with 102 categories), Traffic Signs (German traffic sign images with 43 classes) MSCOCO (images collected from Flickr, 80 classes). All datasets except Traffic signs and MSCOCO have a training, validation and test split (proportioned roughly into 70%, 15%, 15%)

Tensorflow-slim 은 기존의 tensorflow를 보다 사용하기 쉽게 만들어 놓은 high-level API 이다. 오리지널 텐서플로우가 사용하기에 간단하지많은 않았던 만큼 API로 만들어 놓은 slim은 상대적으로 사용하기 쉬운. Iris Flower Dataset: The iris flower dataset is built for the beginners who just start learning machine learning techniques and algorithms. With the help of this data, you can start building a simple project in machine learning algorithms. The size of the dataset is small and data pre-processing is not needed --dataset_split_name : 데이터셋 나누는 이름 학습에는 train 학습 데이터를 사용 --dataset_dir : 데이터셋이 저장된 경로 --max_number_of_steps : 학습할 step

Flowers Recognition Kaggl

Downloading Kaggle Datasets (Conventional Way): The conventional way of downloading datasets from Kaggle is: 1. First, go to Kaggle and you will land on the Kaggle homepage. 2. Sign up or Sign in with required credentials. 3. Then select the Data option from the left pane and you will land on the Datasets page.. 4. Now from the variety of domains, select the datasets that match best of your. We have a new algorithmic approach for doing machine learning with quantum computers. We trained our qmodel for the ternary classification of the Iris flower dataset on IBM quantum computers. It reaches the accuracy level of classical ML Data Set Information: This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant

Overview. We have created a 17 category flower dataset with 80 images for each class. The flowers chosen are some common flowers in the UK. The images have large scale, pose and light variations and there are also classes with large varations of images within the class and close similarity to other classes

This is 'Iris flower dataset' assignment of Data Analytics (BE) - CS of Savitribai Phule Pune University - Gyaani Buddy Got a better answer Click Here If you have a pre-existing submission for this assignment making a new one will override it Visualizing the Dataset : We will be plotting graphs to visualize the clustering of the data for all the 3 species. Example 1 : Here will be plotting a graph with length of petals as the x-axis and breadth of petals as the y-axis. Import the required modules : figure, output_file and show from bokeh.plotting; flowers from bokeh.sampledata.iri

This dataset consists of four sets of flower images, from three different species: apple, peach, and pear, and accompanying ground truth images. The images were acquired under a range of imaging conditions. These datasets support work in an accompanying paper that demonstrates a flower identification algorithm that is robust to uncontrolled environments and applicable to different flower species The dataset I am using here for the flower recognition task contains 4242 flower images. Data collection is based on Flickr data, google images, Yandex images. You can use this data set to recognize the flowers in the photo. The images are divided into five classes: chamomile, tulip, rose, sunflower, dandelion Experiment 2: Oxford 102 Category Flower. Following the coding improvement by Alexander Lazarev's Github code which make dataset setup and the number of classes setup more flexible, we are ready to see if ConvNet transfer learning strategy can be easily applied to a different domain on flowers. The Oxford 102 Category Flower Dataset is the flowers commonly appearing in the United Kingdom

Flower_classification_dataset Kaggl

Iris flower dataset¶. The iris flower dataset is a common dataset used in machine learning.. It has been created Ronald Fisher in 1936. It contains the petal length, petal width, sepal length and sepal width of 150 iris flowers from 3 different species. Dataset has been downloaded from Kaggle.. To go through this example, you need to install AutoClassWrapper Datasets & DataLoaders¶. Code for processing data samples can get messy and hard to maintain; we ideally want our dataset code to be decoupled from our model training code for better readability and modularity. PyTorch provides two data primitives: torch.utils.data.DataLoader and torch.utils.data.Dataset that allow you to use pre-loaded datasets as well as your own data The adopted dataset is the 102 Category Flower Dataset created by M. Nilsback and A. Zisserman [3], which is a collection of 8189 labelled flowers images belonging to 102 different classes. For each class, there are between 40 and 258 instances and all the dataset images have significant scale, pose and light variations Oxford Flowers. The use of SITT for augmenting data improves the performance of ResNet-18 by 1.5% for 5-shot classification on 102 Category Flower Dataset. Caltech-UCSD Birds 200. The use of SITT for augmenting data improves the performance of ResNet-18 by 1.4% for 5-shot classification on CUB-200-2011.. The Iris Dataset. ¶. This data sets consists of 3 different types of irises' (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150x4 numpy.ndarray. The rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and Petal Width. The below plot uses the first two features

Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market. With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision. A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images sklearn.datasets. .load_iris. ¶. Load and return the iris dataset (classification). The iris dataset is a classic and very easy multi-class classification dataset. Read more in the User Guide. If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object The authors of this blog are Stan Zwinkels & Ted de Vries Lentsch.. This blog aims to present our attempt to create a detection algorithm for detecting rip e flowers of the Alstroemeria genus Morado. Throughout this blog, we explain our process to create a dataset and detection model that achieves an F1 score of more than .75.This blog is part of the course Seminar Computer Vision By Deep.

About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators. The CNN is a BVLC reference CaffeNet fine-tuned for the Oxford 102 category flower dataset. The number of outputs in the inner product layer has been set to 102 to reflect the number of flower categories. Hyperparameter choices reflect those in Fine-tuning CaffeNet for Style Recognition on Flickr Style Data For this tutorial we will use the well studied Iris flower dataset. This dataset is comprised of 150 instances that describe the measurements of iris flowers, each of which is classified as one of three species of iris. The attributes are numeric and the problem is a multi-class classification problem Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large.

17 category flowers Kaggl

CIL:39085, Sisymbrium officinale, pollen, flower

tf_flowers TensorFlow Dataset

Flower images in the latter were consistently taken from a specific range of camera angle and distance. Therefore, this allows the flower images in this dataset to be more consistent and easier to distinguish (Figs. 4a and b show some random but representative examples) 本文主要讲解该算法的实现过程实验环境python3.6+pytorch1.2+cuda10.1数据集102 Category Flower Dataset数据集由102类产自英国的花卉组成,每类由40-258张图片组成下边是代码实现过程及讲解数据加载#选择设备device = torch.device(cuda:0)#对三种数据集进行不同预处理,对训练数据进行.. # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this fil 3 — Create a dataset of (image, label) pairs. We will be using Dataset.map and num_parallel_calls is defined so that multiple images are loaded simultaneously. labeled_ds = list_ds.map (process_path, num_parallel_calls=AUTOTUNE) Let's check what is in labeled_ds. for image, label in labeled_ds.take (1) IRIS Flower Prediction Using Machine Learning Algorithms. September 6, 2019. September 12, 2020. The Iris flower data set or Fisher's Iris data set is a multivariate data set. The data set consists of 50 samples from each of the three species of Iris (Setosa, Virginica, and Versicolor). Four features were measured from each sample: the length.

Saikat Das. You can implement Iris Flower dataset , introduced by the British statistician and biologist Ronald Fisher in 1936. It contains 50 samples from each of three species of Iris flower and four features of each sample, i.e., the length and the width of the sepals and petals (centimeters) This dataset is often used for practicing any algorithm made for image classification as the dataset is fairly easy to conquer. Hence, I recommend that this should be your first dataset if you are just foraying in the field. MNIST comes with Keras by default and you can simply load the train and test files using a few lines of code In the cell below you will download the Flowers dataset using TensorFlow Datasets. If you look at the TensorFlow Datasets documentation you will see that the name of the Flowers dataset is tf_flowers.You can also see that this dataset is only split into a TRAINING set. You will therefore have to use tfds.splits to split this training set into to a training_set and a validation_set

There are 150 observations in total, 50 samples each of three species of the Iris flower: Setosa, Virginica and Versicolor, with 5 attributes of those species: Sepal Length, Sepal Width, Petal Length, Petal Width. With Anderson's collected data and Richard Fisher's linear function, they created one of the most famous datasets of all time Using a uniquely comprehensive 39-y flowering phenology dataset from the Colorado Rocky Mountains that contains more than 2 million flower counts, we reveal a diversity of species-level phenological shifts that bring into question the accuracy of previous estimates of long-term phenological change Also, the dataset we have downloaded has following directory structure. flower_photos |__ daisy |__ dandelion |__ roses |__ sunflowers |__ tulips. As you can see there are no folders containing training and validation data. Therefore, we will have to create our own training and validation set. Let's write some code that will do this

The Iris dataset is the simplest, yet the most famous data analysis task in the ML space. In this article, you will build a solution for data analysis & classification task from an Iris dataset using Scala. This article is an excerpt taken from Modern Scala Projects written by Ilango Gurusamy. The following diagrams together help in understanding the different components of this project 5.1,3.5,1.4,0.2,Iris-setosa 4.9,3.0,1.4,0.2,Iris-setosa 4.7,3.2,1.3,0.2,Iris-setosa 4.6,3.1,1.5,0.2,Iris-setosa 5.0,3.6,1.4,0.2,Iris-setosa 5.4,3.9,1.7,0.4,Iris. Flower classification dataset. By 02.12.2020 02.12.2020 Categories: Flower classification dataset Comments on Flower classification dataset. Last Updated on December 13, In this short post you will discover how you can load standard classification and regression datasets in R. Using Keras Pre-trained Deep Learning models for your own.

Oxford 102 Flower (102 Category Flower Dataset) Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The flowers chosen to be flower commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images. 348. Oxford 102 Flower (102 Category Flower Dataset) Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The flowers chosen to be flower commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images. 347.

17 Flower Category Dataset Animals with attributes A dataset for Attribute Based Classification. It consists of 30475 images of 50 animals classes with six pre-extracted feature representations for each image. Stanford Dogs Dataset Dataset of 20,580 images of 120 dog breeds with bounding-box annotation, for fine-grained image categorization LDA 첫 단계로서 3가지 붓꽃 데이터 샘플 종류별 즉 class 라벨별로 평균 벡터를 구하자. 평균 벡터는 리스트형으로서 mean_vectors 라는 변수 명을 부여하자. 두 번째 단계에서는 3가지 붓꽃 종류 각각 별로 scatter 매트릭스에 해당하는 within-class scatter 매트릭스를. Using Pre-trained models, we are trying to predict the flower classes. Learn data science and machine learning by building real-world projects on Jovian. Sign up to execute flowers-image-dataset and 160,000+ data science projects. Build your own projects and share them online! Sign Up. edwardpraveen89

Purple Iris Flower Royalty Free Stock Images - Image: 31024669

Oxford 102 Flower Dataset Papers With Cod

UCI Machine Learning Repository: One-hundred plant species leaves data set Data Set. One-hundred plant species leaves data set Data Set. Download: Data Folder, Data Set Description. Abstract: Sixteen samples of leaf each of one-hundred plant species. For each sample, a shape descriptor, fine scale margin and texture histogram are given 방법: BindingSource가 있는 Windows Forms 컨트롤에서 데이터 원본 업데이트 반영. 03/30/2017; 읽는 데 3분 걸림; a; o; 이 문서의 내용. 데이터 바인딩된 컨트롤을 사용하는 경우 데이터 소스에서 목록 변경 이벤트가 발생하지 않을 때 데이터 소스의 변경 내용에 대응해야 하는 경우가 있습니다

Flowers Classification Datase

Fun and easy ML application ideas for beginners using image datasets: Cat vs Dogs: Using Cat and Stanford Dogs dataset to classify whether an image contains a dog or a cat. Iris Flower classification: You can build an ML project using Iris flower dataset where you classify the flowers in any of the three species. What you learn from this toy project will help you learn to classify physical. IRIS Dataset Analysis (Python) The best way to start learning data science and machine learning application is through iris data. It is a multi-class classification problem and it only has 4 attributes and 150 rows. Also called Fisher's Iris data set or Anderson's Iris data set. Collected by Edgar Anderson and Gaspé Peninsula Adam, Covariance, Iris flowers dataset, Scikit, Sklearn, TensorFlow, 머신러닝 Sklearn 의 SVC 는 hyperplane 경계에서 데이터가 혼합되어 있는 경우 중요 파라메터인 γ 와 C 값을 조절하여 Overfitting 조절이 가능하다

For comparison we have analyzed some of these dataset here. Iris flowers data, taken from the UCI repository [14], has been used in many previous studies. It contains 3 classes (Iris Setosa, Virginica and Versicolor flowers), 4 attributes (sepal and. H. Altay Guvenir. A Classification Learning Algorithm Robust to Irrelevant Features In this article, we will see how to build a Random Forest Classifier using the Scikit-Learn library of Python programming language and in order to do this, we use the IRIS dataset which is quite a common and famous dataset. The Random forest or Random Decision Forest is a supervised Machine learning algorithm used for classification, regression, and other tasks using decision trees This dataset contains 7156 images of orchid flowers and consists of 156 orchid species. Most of the images are Flickr images under the Creative Commons license and some of the images are obtained from websites like Go Botany (Native Plant Trust), Encyclopedia of Life (EoL), etc. Different from other flower datasets, besides containing images, this dataset also containing flower's characteristics

Here is an animated visua­liza­tion of the famous Fisher's Iris flower dataset from 1936. The set is widely used in the pattern recog­ni­tion lite­ra­ture. T.. About Iris dataset ¶. The iris dataset contains the following data. 50 samples of 3 different species of iris (150 samples total) Measurements: sepal length, sepal width, petal length, petal width. The format for the data: (sepal length, sepal width, petal length, petal width) 2. Display Iris Dataset ¶ Similarly Iris flower species has three subspecies Setosa, Versicolor and Virginica. We are using Iris dataset because it is frequently available. The dataset of Iris flower contains 3 classes of 50 instances each. With the help of Machine learning, Iris dataset identifies the sub classes of Iris flower The flower dataset can be downloaded using a google API that basically links to the flower dataset. The 'get_file' method can be used to pass the API as a parameter. Once this is done, the data gets downloaded into the environment

Iris Flower Dataset Kaggle Keras.The pictures are divided into five classes: Fisher's classic 1936 paper, the use of multiple measurements in taxonomic problems, and can also be found on the it includes three iris species with 50 samples each as well as some properties about each flower Using Albumentations with Tensorflow¶. Using Albumentations with Tensorflow. Author: Ayushman Buragohain. In [2]: !pip install -q -U albumentations !echo $ (pip freeze | grep albumentations) is successfully installed. albumentations==0.4.6 is successfully installed

Machine Learning Example: Iris Flower Dataset. Raw. ml-iris-example.py. from sklearn. datasets import load_iris. from sklearn. model_selection import train_test_split. from sklearn. neighbors import KNeighborsClassifier. import numpy as np. iris_dataset = load_iris ( Our version of the flowers dataset contains only two flower varieties: daisies and dandelions. Preparing Our Input Pipeline. Once our data is loaded into the notebook, we'll adapt and create TFRecord files specific to how this TF-Slim implementation expects. Fortunately, scripts are largely written for us Comparison of LDA and PCA 2D projection of Iris dataset¶. The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal width. Principal Component Analysis (PCA) applied to this data identifies the combination of attributes (principal components, or directions in the feature space) that account for. Testing the Dataset. Now we have dimensions of a new flower in a numpy array called x_new and we want to predict the species of this flower. We do this using the predict method which takes this array as input and spits out predicted target value as output. So the predicted target value comes out to be 0 which stands for setosa

Iris flower data set - Wikipedi

Datasets distributed with R Sign in or create your account; Project List Matlab-like plotting library.NET component and COM server; A Simple Scilab-Python Gateway; A Virtual GUI Keyboard for Scilab; accsum; Accurate and portable elementary function flower dataset to predict the result. F. To Evaluate the Model. Now we are in the final stage that is to evaluate the model by choosing any one or more classification algorithm which REFERENCES we have used. And the evaluation is done on target variable Species. Here we have used K-NN and Decision tree as the. SHOPPING Flowers Dataset Tensorflow Flowers Dataset Tensorflow Reviews : Best Price!! Where I Can Get Online Clearance Deals on Flowers Dataset Tensorflow Save More! Flowers Dataset Tensorflow BY Flowers Dataset Tensorflow in Articles @Subscribe Flowers Dataset Tensorflow is actually the most popular goods presented this week

Video: Classify Flowers with Transfer Learning TensorFlow Hu

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