Все публикации

11. Cifar-10 Image0:15:06

11. Cifar-10 Image Dataset Classification by Convolutional Neural Network(CNN) in Computer Vision

3.1 Unsupervised Machine0:04:42

3.1 Unsupervised Machine Learning

10. Classification Fashion0:12:19

10. Classification Fashion MNIST Cloth Images by Using CNN in Tensorflow and Keras

8. Image Data0:19:23

8. Image Data Augmentation in Tensorflow and Keras

2.15 Text Visualization0:06:12

2.15 Text Visualization with Word Cloud

2.14 Visualizations Dependency0:03:11

2.14 Visualizations Dependency Parse Trees and Named Entities

2.13 Word Clouds0:08:29

2.13 Word Clouds in NLP with Python

2.12 Word Sense0:09:39

2.12 Word Sense Disambiguation Using the Lesk Algorithm

7. Load Large0:05:47

7. Load Large Datasets from Directories with tensorflow and keras

2.11 Finding Text0:16:20

2.11 Finding Text Similarity Jaccard and Cosine Similarity in NLP

6. Max and0:12:53

6. Max and Average Pooling layer in CNNs

2.10 Term Frequency–Inverse0:06:11

2.10 Term Frequency–Inverse Document Frequency TFIDF

2.10 Term Frequency–Inverse0:06:11

2.10 Term Frequency–Inverse Document Frequency TFIDF

5. Convolutional Layer0:28:08

5. Convolutional Layer Parameters( filter size, padding and stride)

2.9 Zipf's Law0:13:24

2.9 Zipf's Law in Natural Language Processing

4. Convolution Layer0:16:33

4. Convolution Layer in convolutional neural networks in Tensorflow and Keras

3. Scale Image0:24:04

3. Scale Image pixel Data with keras ImageDataGenerator

Bag of Words0:11:19

Bag of Words Model

2.8 Relationship between0:13:29

2.8 Relationship between a continuous and Categorical Variable

2.7 Relationships within0:08:18

2.7 Relationships within the Data

2.6 Analysis of0:09:48

2.6 Analysis of Continuous Data in Pandas and Numpy

2.5 Categorical Data0:07:06

2.5 Categorical Data Analysis

2.4 Distribution of0:04:59

2.4 Distribution of Values of Features in a Dataset

2.3 Summary Statistics0:10:17

2.3 Summary Statistics and Missing Values in Dataset