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种子名称：
[GigaCourse.com] Udemy - CNN for Computer Vision with Keras and TensorFlow in R
文件类型：
视频
文件数目：
50个文件
文件大小：
2.66 GB
收录时间：
2022-3-26 14:13
已经下载：
3次
资源热度：
225
最近下载：
2024-5-25 18:13
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种子包含的文件
[GigaCourse.com] Udemy - CNN for Computer Vision with Keras and TensorFlow in R.torrent
1. Introduction/1. Introduction.mp421.64MB
10. The NeuralNets Package/1. ANN with NeuralNets Package.mp484.44MB
11. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4216.19MB
12. Hyperparameter Tuning/1. Hyperparameter Tuning.mp460.61MB
13. CNN - Basics/1. CNN Introduction.mp451.17MB
13. CNN - Basics/2. Stride.mp416.57MB
13. CNN - Basics/3. Padding.mp431.62MB
13. CNN - Basics/4. Filters and Feature maps.mp452.74MB
13. CNN - Basics/5. Channels.mp467.76MB
13. CNN - Basics/6. PoolingLayer.mp446.88MB
14. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.mp47.36MB
14. Creating CNN model in R/2. Data Preprocessing.mp467.01MB
14. Creating CNN model in R/3. Creating Model Architecture.mp471.57MB
14. Creating CNN model in R/4. Compiling and training.mp432.23MB
14. Creating CNN model in R/5. Model Performance.mp468.11MB
15. Analyzing impact of Pooling layer/1. Comparison - Pooling vs Without Pooling in R.mp444.56MB
16. Project Creating CNN model from scratch/1. Project - Introduction.mp449.41MB
16. Project Creating CNN model from scratch/3. Project in R - Data Preprocessing.mp487.73MB
16. Project Creating CNN model from scratch/4. CNN Project in R - Structure and Compile.mp446.11MB
16. Project Creating CNN model from scratch/5. Project in R - Training.mp424.61MB
16. Project Creating CNN model from scratch/6. Project in R - Model Performance.mp423.15MB
17. Project Data Augmentation for avoiding overfitting/1. Project in R - Data Augmentation.mp456.37MB
17. Project Data Augmentation for avoiding overfitting/2. Project in R - Validation Performance.mp423.72MB
18. Transfer Learning Basics/1. ILSVRC.mp420.95MB
18. Transfer Learning Basics/2. LeNET.mp47.01MB
18. Transfer Learning Basics/3. VGG16NET.mp410.36MB
18. Transfer Learning Basics/4. GoogLeNet.mp421.37MB
18. Transfer Learning Basics/5. Transfer Learning.mp430MB
19. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4101.57MB
19. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).mp464.14MB
2. Setting Up R Studio and R crash course/1. Installing R and R studio.mp435.69MB
2. Setting Up R Studio and R crash course/2. Basics of R and R studio.mp438.84MB
2. Setting Up R Studio and R crash course/3. Packages in R.mp482.92MB
2. Setting Up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.mp440.74MB
2. Setting Up R Studio and R crash course/5. Inputting data part 2 Manual data entry.mp425.5MB
2. Setting Up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.mp460.1MB
2. Setting Up R Studio and R crash course/7. Creating Barplots in R.mp496.72MB
2. Setting Up R Studio and R crash course/8. Creating Histograms in R.mp442MB
3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp444.76MB
3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp434.61MB
4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp440.44MB
4. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp460.34MB
4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4122.19MB
5. Important concepts Common Interview questions/1. Some Important Concepts.mp462.2MB
6. Standard Model Parameters/1. Hyperparameters.mp445.35MB
7. Tensorflow and Keras/1. Keras and Tensorflow.mp414.93MB
7. Tensorflow and Keras/2. Installing Keras and Tensorflow.mp422.81MB
8. R - Dataset for classification problem/1. Data Normalization and Test-Train Split.mp4111.78MB
9. R - Building and training the Model/1. Building, Compiling and Training.mp4130.71MB
9. R - Building and training the Model/2. Evaluating and Predicting.mp499.22MB
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