Model Checkpoint in Tensorflow | Save Best Model using Checkpoint and Callbacks
Pathshala
Model Checkpoint in Tensorflow | Save Best Model using Checkpoint and Callbacks
10:08
Save Model in JSON file | Save model in YAML file | Save weights and model in h5 file HDF5
Pathshala
Save Model in JSON file | Save model in YAML file | Save weights and model in h5 file HDF5
12:29
Confusion Matrix With Example | Precision, Recall, F1 Score and Support | Evaluate Model
Pathshala
Confusion Matrix With Example | Precision, Recall, F1 Score and Support | Evaluate Model
18:18
Named Entity Recognition (NER) model using Deep Neural Network | TimeDistributed Layer
Pathshala
Named Entity Recognition (NER) model using Deep Neural Network | TimeDistributed Layer
32:11
Multiple Output Model In Tensorflow2.0 | The Functional API
Pathshala
Multiple Output Model In Tensorflow2.0 | The Functional API
11:18
Introduction To The Functional API | Build A Model With The Functional API In Tensorflow2.0
Pathshala
Introduction To The Functional API | Build A Model With The Functional API In Tensorflow2.0
11:52
Time Series Analysis Using Deep Neural Network | Stock Price Prediction System with LSTM
Pathshala
Time Series Analysis Using Deep Neural Network | Stock Price Prediction System with LSTM
9:29
House Price Prediction System with Deep Neural Network on Boston Housing Dataset | (Tensorflow 2.0 )
Pathshala
House Price Prediction System with Deep Neural Network on Boston Housing Dataset | (Tensorflow 2.0 )
House Price Prediction System with Deep Neural Network on Boston Housing Dataset | (Tensorflow 2.0 )
Pathshala
House Price Prediction System with Deep Neural Network on Boston Housing Dataset | (Tensorflow 2.0 )
13:46
Introduction to Conv1D | Sentiment Analysis using Convolutional Neural Network (CNN)
Pathshala
Introduction to Conv1D | Sentiment Analysis using Convolutional Neural Network (CNN)
12:22
Introduction To Long Short Term Memory (LSTM) | IMDB Sentiment Analysis Using LSTM In Tensorflow 2.0
Pathshala
Introduction To Long Short Term Memory (LSTM) | IMDB Sentiment Analysis Using LSTM In Tensorflow 2.0
17:26
Introduction to Gated Recurrent Units | Sentiment Analysis using GRU Layer from Tensorflow2.0
Pathshala
Introduction to Gated Recurrent Units | Sentiment Analysis using GRU Layer from Tensorflow2.0
12:17
IMDB Sentiment Analysis Using SimpleRNN Layer From Tensorflow | Working With Sequential Data (Text)
Pathshala
IMDB Sentiment Analysis Using SimpleRNN Layer From Tensorflow | Working With Sequential Data (Text)
15:23
Sentiment Analysis | Learning Embedding For Words Of Corpus Using Embedding Layer | RNN In Tensoflow
Pathshala
Sentiment Analysis | Learning Embedding For Words Of Corpus Using Embedding Layer | RNN In Tensoflow
13:08
Sentiment Analysis Using Deep Neural Network (SimpleRNN) With Word2Vec Pretrained Word Embeddings
Pathshala
Sentiment Analysis Using Deep Neural Network (SimpleRNN) With Word2Vec Pretrained Word Embeddings
17:04
Creating Environments In Anaconda On Local Machine
Pathshala
Creating Environments In Anaconda On Local Machine
2:58
Installing Keras And Other Libraries In Anaconda In Specific Environment On Local Machine
Pathshala
Installing Keras And Other Libraries In Anaconda In Specific Environment On Local Machine
3:09
Drawbacks Of One-hot Encoding | Introduction To Word Embeddings | Load And Use Pretrained Word2Vec
Pathshala
Drawbacks Of One-hot Encoding | Introduction To Word Embeddings | Load And Use Pretrained Word2Vec
17:42
Tokenization On Text In TF 2.0 | Converting Text To A Sequence Of Number | TF text vectorization
Pathshala
Tokenization On Text In TF 2.0 | Converting Text To A Sequence Of Number | TF text vectorization
12:04
VGG16 Pretrained Model For Cat Dog Classification Using Feature Extraction And Fine Tunning Method
Pathshala
VGG16 Pretrained Model For Cat Dog Classification Using Feature Extraction And Fine Tunning Method
14:24
Pretrained Models | When And Why To Use Pre-trained Model | How To Use Pre-trained Model (Theory)
Pathshala
Pretrained Models | When And Why To Use Pre-trained Model | How To Use Pre-trained Model (Theory)
7:38
Feature Maps Visualization Of CNN | Interpretation Of Output Of Conv2D And Maxpooling Layer
Pathshala
Feature Maps Visualization Of CNN | Interpretation Of Output Of Conv2D And Maxpooling Layer
10:30
Image Augmentation With Keras For Increasing Size Of Dataset | ImageDataGenerator TF Keras
Pathshala
Image Augmentation With Keras For Increasing Size Of Dataset | ImageDataGenerator TF Keras
9:41
Introduction To Convolutional Neural Network | Convolutional Neural Network Example | Deep Learning
Pathshala
Introduction To Convolutional Neural Network | Convolutional Neural Network Example | Deep Learning
14:39
People Card | Digital Visiting Card | Virtual Card | Get Search Enabled On Google By Your Own Name
Pathshala
People Card | Digital Visiting Card | Virtual Card | Get Search Enabled On Google By Your Own Name
4:11
Accessing Google Drive In Colab | Working With Large File | Reading From File From Google Drive
Pathshala
Accessing Google Drive In Colab | Working With Large File | Reading From File From Google Drive
4:22
Training NN On CSV File Dataset In Google Colab Using Pandas Library To Extract And Process Dataset
Pathshala
Training NN On CSV File Dataset In Google Colab Using Pandas Library To Extract And Process Dataset
8:33
Model Summary | Plotting Model | Getting Layers With Weights | Saving Models | Loading Weight
Pathshala
Model Summary | Plotting Model | Getting Layers With Weights | Saving Models | Loading Weight
11:05
Need of Learning Rate Decay | Using Learning Rate Decay In Tensorflow 2 with Callback and Scheduler
Pathshala
Need of Learning Rate Decay | Using Learning Rate Decay In Tensorflow 2 with Callback and Scheduler
11:07
Learning Rate | Effect of High and Very Small Learning Rate | Using Learning Rate in Tensorflow 2.0
Pathshala
Learning Rate | Effect of High and Very Small Learning Rate | Using Learning Rate in Tensorflow 2.0
6:09
Batch Normalization | Why To Use Batch Normalization | How To Use Batch Normalization In Tensorflow
Pathshala
Batch Normalization | Why To Use Batch Normalization | How To Use Batch Normalization In Tensorflow
6:23
Dataset Standardization And Normalization | Need For Normalization | Normalization With Tensorflow
Pathshala
Dataset Standardization And Normalization | Need For Normalization | Normalization With Tensorflow
7:54
What Is Batch | Mini Batch In TF| Effect Of Low And High Batch Size | Mini Batch Effect On Accuracy
Pathshala
What Is Batch | Mini Batch In TF| Effect Of Low And High Batch Size | Mini Batch Effect On Accuracy
7:15
What Is Early Stopping | Using Early Stopping In Tensorflow2.0 | Reducing Overfitting / Variance
Pathshala
What Is Early Stopping | Using Early Stopping In Tensorflow2.0 | Reducing Overfitting / Variance
4:58
Dropout Layer In Tensorflow2.0 | Variance / Overfitting Reduction | High Dev/Validation Accuracy
Pathshala
Dropout Layer In Tensorflow2.0 | Variance / Overfitting Reduction | High Dev/Validation Accuracy
7:46
What Is Bias And Variance | Identifying From Graphs | Actions For Reducing Bias And Variance
Pathshala
What Is Bias And Variance | Identifying From Graphs | Actions For Reducing Bias And Variance
8:22
What Is Validation Dataset?
Pathshala
What Is Validation Dataset?
6:06
Evaluating Trained Model
Pathshala
Evaluating Trained Model
3:28
Plotting Accuracy and Loss Graph for Trained Model using Matplotlib with History Callback
Pathshala
Plotting Accuracy and Loss Graph for Trained Model using Matplotlib with History Callback
6:08
Hand Written Digit Recognition using TF2.0
Pathshala
Hand Written Digit Recognition using TF2.0
20:34
Basics of Artificial Neural Network ANN
Pathshala
Basics of Artificial Neural Network ANN
23:34
Linear Regression In Neural Network With Tensorflow 2.0
Pathshala
Linear Regression In Neural Network With Tensorflow 2.0
8:22
Google Colab Tutorial
Pathshala
Google Colab Tutorial
7:38
Machine Learning With Applications | Difference Between ML And Classical Programming With Example
Pathshala
Machine Learning With Applications | Difference Between ML And Classical Programming With Example
6:48
How to use google meet for Creating, Joining and Operating Meeting
Pathshala
How to use google meet for Creating, Joining and Operating Meeting
8:25
Kisan Vikas Patra (KVP) to double amount with Interest Rate, Maturity, Benefit and Tax Exemption
Pathshala
Kisan Vikas Patra (KVP) to double amount with Interest Rate, Maturity, Benefit and Tax Exemption
4:10
How to apply for e-pass (Travel Permission) in Lockdown (Maharashtra)
Pathshala
How to apply for e-pass (Travel Permission) in Lockdown (Maharashtra)
9:28
अंक ओळख (१ ते १०)
Pathshala
अंक ओळख (१ ते १०)
1:22
Text Animation
Pathshala
Text Animation
2:34
Introduction to Color in Marathi
Pathshala
Introduction to Color in Marathi
2:23