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

1. 1. Introduction/1. Introduction.mp421.64MB
2. 10. The NeuralNets Package/1. ANN with NeuralNets Package.mp484.44MB
3. 11. Saving and Restoring Models/1. Saving - Restoring Models and Using Callbacks.mp4216.19MB
4. 12. Hyperparameter Tuning/1. Hyperparameter Tuning.mp460.61MB
5. 13. CNN - Basics/1. CNN Introduction.mp451.17MB
6. 13. CNN - Basics/2. Stride.mp416.57MB
7. 13. CNN - Basics/3. Padding.mp431.62MB
8. 13. CNN - Basics/4. Filters and Feature maps.mp452.74MB
9. 13. CNN - Basics/5. Channels.mp467.76MB
10. 13. CNN - Basics/6. PoolingLayer.mp446.88MB
11. 14. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.mp47.36MB
12. 14. Creating CNN model in R/2. Data Preprocessing.mp467.01MB
13. 14. Creating CNN model in R/3. Creating Model Architecture.mp471.57MB
14. 14. Creating CNN model in R/4. Compiling and training.mp432.23MB
15. 14. Creating CNN model in R/5. Model Performance.mp468.11MB
16. 15. Analyzing impact of Pooling layer/1. Comparison - Pooling vs Without Pooling in R.mp444.56MB
17. 16. Project Creating CNN model from scratch/1. Project - Introduction.mp449.41MB
18. 16. Project Creating CNN model from scratch/3. Project in R - Data Preprocessing.mp487.73MB
19. 16. Project Creating CNN model from scratch/4. CNN Project in R - Structure and Compile.mp446.11MB
20. 16. Project Creating CNN model from scratch/5. Project in R - Training.mp424.61MB
21. 16. Project Creating CNN model from scratch/6. Project in R - Model Performance.mp423.15MB
22. 17. Project Data Augmentation for avoiding overfitting/1. Project in R - Data Augmentation.mp456.37MB
23. 17. Project Data Augmentation for avoiding overfitting/2. Project in R - Validation Performance.mp423.72MB
24. 18. Transfer Learning Basics/1. ILSVRC.mp420.95MB
25. 18. Transfer Learning Basics/2. LeNET.mp47.01MB
26. 18. Transfer Learning Basics/3. VGG16NET.mp410.36MB
27. 18. Transfer Learning Basics/4. GoogLeNet.mp421.37MB
28. 18. Transfer Learning Basics/5. Transfer Learning.mp430MB
29. 19. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4101.57MB
30. 19. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).mp464.14MB
31. 2. Setting Up R Studio and R crash course/1. Installing R and R studio.mp435.69MB
32. 2. Setting Up R Studio and R crash course/2. Basics of R and R studio.mp438.84MB
33. 2. Setting Up R Studio and R crash course/3. Packages in R.mp482.92MB
34. 2. Setting Up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.mp440.74MB
35. 2. Setting Up R Studio and R crash course/5. Inputting data part 2 Manual data entry.mp425.5MB
36. 2. Setting Up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.mp460.1MB
37. 2. Setting Up R Studio and R crash course/7. Creating Barplots in R.mp496.72MB
38. 2. Setting Up R Studio and R crash course/8. Creating Histograms in R.mp442MB
39. 3. Single Cells - Perceptron and Sigmoid Neuron/1. Perceptron.mp444.76MB
40. 3. Single Cells - Perceptron and Sigmoid Neuron/2. Activation Functions.mp434.61MB
41. 4. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp440.44MB
42. 4. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp460.34MB
43. 4. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4122.19MB
44. 5. Important concepts Common Interview questions/1. Some Important Concepts.mp462.2MB
45. 6. Standard Model Parameters/1. Hyperparameters.mp445.35MB
46. 7. Tensorflow and Keras/1. Keras and Tensorflow.mp414.93MB
47. 7. Tensorflow and Keras/2. Installing Keras and Tensorflow.mp422.81MB
48. 8. R - Dataset for classification problem/1. Data Normalization and Test-Train Split.mp4111.78MB
49. 9. R - Building and training the Model/1. Building, Compiling and Training.mp4130.71MB
50. 9. R - Building and training the Model/2. Evaluating and Predicting.mp499.22MB