| 01 - Batch normalization.mp4 | 5 MB | ||
| 01 - Batch normalization.srt | 4.9 KB | ||
| 01 - Common loss functions in deep learning.mp4 | 10.6 MB | ||
| 01 - Common loss functions in deep learning.srt | 7.9 KB | ||
| 01 - Continuing to optimize deep learning models.mp4 | 6.1 MB | ||
| 01 - Continuing to optimize deep learning models.srt | 5.4 KB | ||
| 01 - Optimizing deep learning models.mp4 | 3.5 MB | ||
| 01 - Optimizing deep learning models.srt | 1.5 KB | ||
| 01 - Parameters versus hyperparameters.mp4 | 9.7 MB | ||
| 01 - Parameters versus hyperparameters.srt | 7.4 KB | ||
| 01 - The bias-variance trade-off.mp4 | 5.9 MB | ||
| 01 - The bias-variance trade-off.srt | 5.7 KB | ||
| 01 - The importance of optimizing deep learning models.mp4 | 6 MB | ||
| 01 - The importance of optimizing deep learning models.srt | 5.1 KB | ||
| 02 - Applying batch normalization to a deep learning model.mp4 | 8 MB | ||
| 02 - Applying batch normalization to a deep learning model.srt | 4.4 KB | ||
| 02 - Batch gradient descent.mp4 | 5.6 MB | ||
| 02 - Batch gradient descent.srt | 5.9 KB | ||
| 02 - Key hyperparameters in deep learning.mp4 | 14.9 MB | ||
| 02 - Key hyperparameters in deep learning.srt | 12.6 KB | ||
| 02 - Lasso and ridge regularization.mp4 | 7.5 MB | ||
| 02 - Lasso and ridge regularization.srt | 6.5 KB | ||
| 02 - What you should know.mp4 | 1.2 MB | ||
| 02 - What you should know.srt | 1.4 KB | ||
| 03 - Applying L1 regularization to a deep learning model.mp4 | 9.8 MB | ||
| 03 - Applying L1 regularization to a deep learning model.srt | 4.6 KB | ||
| 03 - Gradient clipping.mp4 | 9.4 MB | ||
| 03 - Gradient clipping.srt | 7.8 KB | ||
| 03 - Methods for hyperparameter tuning.mp4 | 10.3 MB | ||
| 03 - Methods for hyperparameter tuning.srt | 10.2 KB | ||
| 03 - Stochastic gradient descent (SGD).mp4 | 4.6 MB | ||
| 03 - Stochastic gradient descent (SGD).srt | 4.8 KB | ||
| 03 - Using the exercise files.mp4 | 2.8 MB | ||
| 03 - Using the exercise files.srt | 2.1 KB | ||
| 04 - Applying L2 regularization to a deep learning model.mp4 | 9.6 MB | ||
| 04 - Applying L2 regularization to a deep learning model.srt | 4.9 KB | ||
| 04 - Applying gradient clipping to a deep learning model.mp4 | 9 MB | ||
| 04 - Applying gradient clipping to a deep learning model.srt | 4.4 KB | ||
| 04 - Defining a tunable deep learning model in Keras.mp4 | 17.9 MB | ||
| 04 - Defining a tunable deep learning model in Keras.srt | 9.7 KB | ||
| 04 - Mini-batch gradient descent.mp4 | 5.5 MB | ||
| 04 - Mini-batch gradient descent.srt | 6.1 KB | ||
| 05 - Adaptive Gradient Algorithm (AdaGrad).mp4 | 8.7 MB | ||
| 05 - Adaptive Gradient Algorithm (AdaGrad).srt | 7.6 KB | ||
| 05 - Early stopping and checkpointing.mp4 | 5.1 MB | ||
| 05 - Early stopping and checkpointing.srt | 5.6 KB | ||
| 05 - Elastic Net regularization.mp4 | 4.7 MB | ||
| 05 - Elastic Net regularization.srt | 4 KB | ||
| 05 - Using KerasTuner for hyperparameter tuning.mp4 | 23.5 MB | ||
| 05 - Using KerasTuner for hyperparameter tuning.srt | 11.5 KB | ||
| 06 - Dropout regularization.mp4 | 4.7 MB | ||
| 06 - Dropout regularization.srt | 4.5 KB | ||
| 06 - Learning rate scheduling.mp4 | 11.1 MB | ||
| 06 - Learning rate scheduling.srt | 8.2 KB | ||
| 06 - Root Mean Square Propagation (RMSProp).mp4 | 4.3 MB | ||
| 06 - Root Mean Square Propagation (RMSProp).srt | 3.8 KB | ||
| 07 - Adaptive Delta (AdaDelta).mp4 | 2.6 MB | ||
| 07 - Adaptive Delta (AdaDelta).srt | 2.8 KB | ||
| 07 - Applying dropout regularization to a deep learning model.mp4 | 9.9 MB | ||
| 07 - Applying dropout regularization to a deep learning model.srt | 4.4 KB | ||
| 07 - Training a deep learning model using callbacks.mp4 | 20 MB | ||
| 07 - Training a deep learning model using callbacks.srt | 10.2 KB | ||
| 08 - Adaptive Moment Estimation (Adam).mp4 | 6.1 MB | ||
| 08 - Adaptive Moment Estimation (Adam).srt | 5.2 KB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 66 total files | |||
Deep Learning with Python: Optimizing Deep Learning Models
https://DevCourseWeb.com
Released 02/2025
With Frederick Nwanganga
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 2h 1m 27s | Size: 265 MB
Leverage techniques for optimizing deep learning models and implementing them using Python.
Course details
Discover techniques to optimize deep learning models by improving their performance and efficiency. Emphasizing practical applications, instructor Frederick Nwanganga guides you through hands-on coding exercises, covering the essentials of data preprocessing and augmentation, regularization methods to minimize overfitting, optimization algorithms, advanced hyperparameter tuning methods, and more.This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out “Using GitHub Codespaces" with this course to learn how to get started.
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| 4 GB | freecoursewb | 2 months | 12 | 10 | |
| 498 MB | freecoursewb | 3 months | 16 | 2 | |
| 4 GB | freecoursewb | 5 months | 24 | 10 | |
| 2.5 GB | freecoursewb | 7 months | 14 | 3 | |
| 785.3 MB | freecoursewb | 8 months | 13 | 1 |
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