[GigaCourse.com] Udemy - CNN for Computer Vision with Keras and TensorFlow in R

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[GigaCourse.com] Udemy - CNN for Computer Vision with Keras and TensorFlow in R

磁力链接/BT种子简介

种子哈希：b9e318e35b8ad3985af101d3f9ea9fa132c48a43
文件大小： 2.68G
已经下载：3333次
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收录时间：2021-03-08
最近下载：2026-05-18
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文件列表

  • 11. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4 226.7 MB
  • 9. R - Building and training the Model/1. Building, Compiling and Training.mp4 137.1 MB
  • 4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4 128.1 MB
  • 8. R - Dataset for classification problem/1. Data Normalization and Test-Train Split.mp4 117.2 MB
  • 19. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4 106.5 MB
  • 9. R - Building and training the Model/2. Evaluating and Predicting.mp4 104.0 MB
  • 2. Setting Up R Studio and R crash course/7. Creating Barplots in R.mp4 101.4 MB
  • 16. Project Creating CNN model from scratch/3. Project in R - Data Preprocessing.mp4 92.0 MB
  • 10. The NeuralNets Package/1. ANN with NeuralNets Package.mp4 88.5 MB
  • 2. Setting Up R Studio and R crash course/3. Packages in R.mp4 87.0 MB
  • 14. Creating CNN model in R/3. Creating Model Architecture.mp4 75.1 MB
  • 14. Creating CNN model in R/5. Model Performance.mp4 71.4 MB
  • 13. CNN - Basics/5. Channels.mp4 71.1 MB
  • 14. Creating CNN model in R/2. Data Preprocessing.mp4 70.3 MB
  • 19. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).mp4 67.3 MB
  • 5. Important concepts Common Interview questions/1. Some Important Concepts.mp4 65.2 MB
  • 12. Hyperparameter Tuning/1. Hyperparameter Tuning.mp4 63.6 MB
  • 4. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp4 63.3 MB
  • 2. Setting Up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.mp4 63.0 MB
  • 17. Project Data Augmentation for avoiding overfitting/1. Project in R - Data Augmentation.mp4 59.1 MB
  • 13. CNN - Basics/4. Filters and Feature maps.mp4 55.3 MB
  • 13. CNN - Basics/1. CNN Introduction.mp4 53.7 MB
  • 16. Project Creating CNN model from scratch/1. Project - Introduction.mp4 51.8 MB
  • 13. CNN - Basics/6. PoolingLayer.mp4 49.2 MB
  • 16. Project Creating CNN model from scratch/4. CNN Project in R - Structure and Compile.mp4 48.3 MB
  • 6. Standard Model Parameters/1. Hyperparameters.mp4 47.6 MB
  • 3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp4 46.9 MB
  • 15. Analyzing impact of Pooling layer/1. Comparison - Pooling vs Without Pooling in R.mp4 46.7 MB
  • 2. Setting Up R Studio and R crash course/8. Creating Histograms in R.mp4 44.0 MB
  • 2. Setting Up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.mp4 42.7 MB
  • 4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp4 42.4 MB
  • 2. Setting Up R Studio and R crash course/2. Basics of R and R studio.mp4 40.7 MB
  • 2. Setting Up R Studio and R crash course/1. Installing R and R studio.mp4 37.4 MB
  • 3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp4 36.3 MB
  • 14. Creating CNN model in R/4. Compiling and training.mp4 33.8 MB
  • 13. CNN - Basics/3. Padding.mp4 33.2 MB
  • 18. Transfer Learning Basics/5. Transfer Learning.mp4 31.5 MB
  • 2. Setting Up R Studio and R crash course/5. Inputting data part 2 Manual data entry.mp4 26.7 MB
  • 16. Project Creating CNN model from scratch/5. Project in R - Training.mp4 25.8 MB
  • 17. Project Data Augmentation for avoiding overfitting/2. Project in R - Validation Performance.mp4 24.9 MB
  • 16. Project Creating CNN model from scratch/6. Project in R - Model Performance.mp4 24.3 MB
  • 7. Tensorflow and Keras/2. Installing Keras and Tensorflow.mp4 23.9 MB
  • 1. Introduction/1. Introduction.mp4 22.7 MB
  • 18. Transfer Learning Basics/4. GoogLeNet.mp4 22.4 MB
  • 18. Transfer Learning Basics/1. ILSVRC.mp4 22.0 MB
  • 13. CNN - Basics/2. Stride.mp4 17.4 MB
  • 7. Tensorflow and Keras/1. Keras and Tensorflow.mp4 15.7 MB
  • 2. Setting Up R Studio and R crash course/3. Packages in R.srt 15.2 MB
  • 18. Transfer Learning Basics/3. VGG16NET.mp4 10.9 MB
  • 1. Introduction/2.1 ST Academy - CNN course files R.zip 7.9 MB
  • 14. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.mp4 7.7 MB
  • 18. Transfer Learning Basics/2. LeNET.mp4 7.3 MB
  • 4. Neural Networks - Stacking cells to create network/3. Back Propagation.srt 23.3 kB
  • 11. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.srt 20.9 kB
  • 9. R - Building and training the Model/1. Building, Compiling and Training.srt 15.8 kB
  • 2. Setting Up R Studio and R crash course/7. Creating Barplots in R.srt 13.7 kB
  • 19. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).srt 13.4 kB
  • 5. Important concepts Common Interview questions/1. Some Important Concepts.srt 13.4 kB
  • 8. R - Dataset for classification problem/1. Data Normalization and Test-Train Split.srt 12.4 kB
  • 4. Neural Networks - Stacking cells to create network/2. Gradient Descent.srt 12.2 kB
  • 16. Project Creating CNN model from scratch/3. Project in R - Data Preprocessing.srt 11.6 kB
  • 2. Setting Up R Studio and R crash course/2. Basics of R and R studio.srt 11.1 kB
  • 3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.srt 9.9 kB
  • 4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.srt 9.7 kB
  • 12. Hyperparameter Tuning/1. Hyperparameter Tuning.srt 9.7 kB
  • 9. R - Building and training the Model/2. Evaluating and Predicting.srt 9.7 kB
  • 6. Standard Model Parameters/1. Hyperparameters.srt 9.2 kB
  • 19. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).srt 8.6 kB
  • 3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.srt 8.0 kB
  • 10. The NeuralNets Package/1. ANN with NeuralNets Package.srt 7.9 kB
  • 17. Project Data Augmentation for avoiding overfitting/1. Project in R - Data Augmentation.srt 7.7 kB
  • 14. Creating CNN model in R/2. Data Preprocessing.srt 7.4 kB
  • 16. Project Creating CNN model from scratch/1. Project - Introduction.srt 7.3 kB
  • 13. CNN - Basics/4. Filters and Feature maps.srt 6.7 kB
  • 2. Setting Up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.srt 6.5 kB
  • 14. Creating CNN model in R/5. Model Performance.srt 6.4 kB
  • 14. Creating CNN model in R/3. Creating Model Architecture.srt 6.3 kB
  • 2. Setting Up R Studio and R crash course/8. Creating Histograms in R.srt 6.0 kB
  • 13. CNN - Basics/5. Channels.srt 6.0 kB
  • 2. Setting Up R Studio and R crash course/1. Installing R and R studio.srt 5.8 kB
  • 18. Transfer Learning Basics/5. Transfer Learning.srt 5.5 kB
  • 16. Project Creating CNN model from scratch/4. CNN Project in R - Structure and Compile.srt 5.3 kB
  • 13. CNN - Basics/6. PoolingLayer.srt 5.2 kB
  • 13. CNN - Basics/3. Padding.srt 4.7 kB
  • 18. Transfer Learning Basics/1. ILSVRC.srt 4.5 kB
  • 15. Analyzing impact of Pooling layer/1. Comparison - Pooling vs Without Pooling in R.srt 4.2 kB
  • 2. Setting Up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.srt 4.1 kB
  • 1. Introduction/1. Introduction.srt 3.7 kB
  • 7. Tensorflow and Keras/1. Keras and Tensorflow.srt 3.6 kB
  • 14. Creating CNN model in R/4. Compiling and training.srt 3.1 kB
  • 18. Transfer Learning Basics/4. GoogLeNet.srt 3.1 kB
  • 7. Tensorflow and Keras/2. Installing Keras and Tensorflow.srt 3.0 kB
  • 2. Setting Up R Studio and R crash course/5. Inputting data part 2 Manual data entry.srt 3.0 kB
  • 16. Project Creating CNN model from scratch/5. Project in R - Training.srt 2.9 kB
  • 13. CNN - Basics/2. Stride.srt 2.8 kB
  • 17. Project Data Augmentation for avoiding overfitting/2. Project in R - Validation Performance.srt 2.6 kB
  • 16. Project Creating CNN model from scratch/6. Project in R - Model Performance.srt 2.6 kB
  • 18. Transfer Learning Basics/3. VGG16NET.srt 1.9 kB
  • 18. Transfer Learning Basics/2. LeNET.srt 1.7 kB
  • Readme.txt 962 Bytes
  • 16. Project Creating CNN model from scratch/2. Data for the project.html 232 Bytes
  • 4. Neural Networks - Stacking cells to create network/4. Quiz.html 165 Bytes
  • 5. Important concepts Common Interview questions/2. Quiz.html 165 Bytes
  • 16. Project Creating CNN model from scratch/2.1 Download the project dataset.html 127 Bytes
  • 1. Introduction/2. Course resources.html 82 Bytes
  • [GigaCourse.com].url 49 Bytes
  • 14. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.srt 0 Bytes

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