| 01 - Full-stack deep learning, MLOps, and MLflow.mp4 | 9.8 MB | ||
| 01 - Full-stack deep learning, MLOps, and MLflow.srt | 12.5 KB | ||
| 01 - Introducing full-stack deep learning.mp4 | 7.8 MB | ||
| 01 - Introducing full-stack deep learning.srt | 11.4 KB | ||
| 01 - Loading and exploring the EMNIST dataset.mp4 | 9.9 MB | ||
| 01 - Loading and exploring the EMNIST dataset.srt | 8.8 KB | ||
| 01 - Preparing data for image classification using CNN.mp4 | 9.7 MB | ||
| 01 - Preparing data for image classification using CNN.srt | 6.9 KB | ||
| 01 - Setting up MLflow on the local machine.mp4 | 8.2 MB | ||
| 01 - Setting up MLflow on the local machine.srt | 8.4 KB | ||
| 01 - Summary and next steps.mp4 | 2.5 MB | ||
| 01 - Summary and next steps.srt | 3.2 KB | ||
| 02 - Configuring and training the model using MLflow runs.mp4 | 15.5 MB | ||
| 02 - Configuring and training the model using MLflow runs.srt | 10.9 KB | ||
| 02 - Introducing MLOps.mp4 | 6.6 MB | ||
| 02 - Introducing MLOps.srt | 7.6 KB | ||
| 02 - Logging metrics, parameters, and artifacts in MLflow.mp4 | 11 MB | ||
| 02 - Logging metrics, parameters, and artifacts in MLflow.srt | 11 KB | ||
| 02 - Prerequisites.mp4 | 898.8 KB | ||
| 02 - Prerequisites.srt | 1.1 KB | ||
| 02 - Workaround to get model artifacts on the local machine.mp4 | 4.3 MB | ||
| 02 - Workaround to get model artifacts on the local machine.srt | 3.9 KB | ||
| 03 - Deploying and serving the model locally.mp4 | 13.8 MB | ||
| 03 - Deploying and serving the model locally.srt | 10.6 KB | ||
| 03 - Introducing MLflow.mp4 | 6.3 MB | ||
| 03 - Introducing MLflow.srt | 7.9 KB | ||
| 03 - Set up the dataset and data loader.mp4 | 6.9 MB | ||
| 03 - Set up the dataset and data loader.srt | 6.4 KB | ||
| 03 - Visualizing charts, metrics, and parameters on MLflow.mp4 | 15.2 MB | ||
| 03 - Visualizing charts, metrics, and parameters on MLflow.srt | 12 KB | ||
| 04 - Configuring the image classification DNN model.mp4 | 10.5 MB | ||
| 04 - Configuring the image classification DNN model.srt | 8.7 KB | ||
| 04 - Setting up the environment on Google Colab.mp4 | 13 MB | ||
| 04 - Setting up the environment on Google Colab.srt | 9.2 KB | ||
| 04 - Setting up the objective function for hyperparameter tuning.mp4 | 12.4 MB | ||
| 04 - Setting up the objective function for hyperparameter tuning.srt | 9.8 KB | ||
| 05 - Hyperparameter optimization with Hyperopt and MLflow.mp4 | 13.9 MB | ||
| 05 - Hyperparameter optimization with Hyperopt and MLflow.srt | 11.7 KB | ||
| 05 - Running MLflow and using ngrok to access the MLflow UI.mp4 | 10.3 MB | ||
| 05 - Running MLflow and using ngrok to access the MLflow UI.srt | 9.7 KB | ||
| 05 - Training a model within an MLflow run.mp4 | 11.1 MB | ||
| 05 - Training a model within an MLflow run.srt | 7 KB | ||
| 06 - Exploring parameters and metrics in MLflow.mp4 | 9 MB | ||
| 06 - Exploring parameters and metrics in MLflow.srt | 7.9 KB | ||
| 06 - Identifying the best model.mp4 | 7.8 MB | ||
| 06 - Identifying the best model.srt | 6 KB | ||
| 07 - Making predictions using MLflow artifacts.mp4 | 11.4 MB | ||
| 07 - Making predictions using MLflow artifacts.srt | 8.8 KB | ||
| 07 - Registering a model with the MLflow registry.mp4 | 5.7 MB | ||
| 07 - Registering a model with the MLflow registry.srt | 6 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| demo_01_EMNISTClassificationUsingDNN-checkpoint.ipynb | 1.7 MB | ||
| demo_01_EMNISTClassificationUsingDNN.ipynb | 1.7 MB | ||
| demo_02_EMNISTClassificationUsingCNN.ipynb | 3.1 MB | ||
| demo_03_ModelDeployment-checkpoint.ipynb | 46.3 KB | ||
| demo_03_ModelDeployment.ipynb | 37.7 KB | ||
| emnist-letters-test.csv | 27.3 MB | ||
| emnist-letters-train.csv | 163.7 MB | ||
| ▲ 59 total files | |||
Full-Stack Deep Learning with Python
https://FreeCourseWeb.com
Released 2/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Advanced | Genre: eLearning | Language: English + srt | Duration: 1h 58 | Size: 268 MB
If you seek a more in-depth understanding of deep learning and Python, this hands-on course can help you. In this course, certified Google cloud architect and data engineer Janani Ravi guides you through the intricacies of full-stack deep learning with Python. After a review of full stack deep learning, MLOps, and MLflow, dive into setting up your environment on Google Colab and running MLflow. Learn how to load and explore a dataset, as well as how to log metrics, parameters, and artifacts. Explore model training, evaluation, and hyperparameter tuning. Plus, go over model deployment and predictions.
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