Linkedin - Full-Stack Deep Learning with Python

Files:

• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 01 - Introduction
• 01 - Full-stack deep learning, MLOps, and MLflow.mp4 (9.8 MB)
• 01 - Full-stack deep learning, MLOps, and MLflow.srt (12.5 KB)
• 02 - Prerequisites.mp4 (898.8 KB)
• 02 - Prerequisites.srt (1.1 KB)
02 - 1. An Overview of Full-Stack Deep Learning
• 01 - Introducing full-stack deep learning.mp4 (7.8 MB)
• 01 - Introducing full-stack deep learning.srt (11.4 KB)
• 02 - Introducing MLOps.mp4 (6.6 MB)
• 02 - Introducing MLOps.srt (7.6 KB)
• 03 - Introducing MLflow.mp4 (6.3 MB)
• 03 - Introducing MLflow.srt (7.9 KB)
• 04 - Setting up the environment on Google Colab.mp4 (13.0 MB)
• 04 - Setting up the environment on Google Colab.srt (9.2 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)
03 - 2. Model Training and Evaluation Using MLflow
• 01 - Loading and exploring the EMNIST dataset.mp4 (9.9 MB)
• 01 - Loading and exploring the EMNIST dataset.srt (8.8 KB)
• 02 - Logging metrics, parameters, and artifacts in MLflow.mp4 (11.0 MB)
• 02 - Logging metrics, parameters, and artifacts in MLflow.srt (11.0 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)
• 04 - Configuring the image classification DNN model.mp4 (10.5 MB)
• 04 - Configuring the image classification DNN model.srt (8.7 KB)
• 05 - Training a model within an MLflow run.mp4 (11.1 MB)
• 05 - Training a model within an MLflow run.srt (7.0 KB)
• 06 - Exploring parameters and metrics in MLflow.mp4 (9.0 MB)
• 06 - Exploring parameters and metrics in MLflow.srt (7.9 KB)
• 07 - Making predictions using MLflow artifacts.mp4 (11.4 MB)
• 07 - Making predictions using MLflow artifacts.srt (8.8 KB)
04 - 3. Model Training and Hyperparameter Tuning
• 01 - Preparing data for image classification using CNN.mp4 (9.7 MB)
• 01 - Preparing data for image classification using CNN.srt (6.9 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)
• 03 - Visualizing charts, metrics, and parameters on MLflow.mp4 (15.2 MB)
• 03 - Visualizing charts, metrics, and parameters on MLflow.srt (12.0 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)
• 06 - Identifying the best model.mp4 (7.8 MB)
• 06 - Identifying the best model.srt (6.0 KB)
• 07 - Registering a model with the MLflow registry.mp4 (5.7 MB)
• 07 - Registering a model with the MLflow registry.srt (6.0 KB)
05 - 4. Model Deployment and Predictions
• 01 - Setting up MLflow on the local machine.mp4 (8.2 MB)
• 01 - Setting up MLflow on the local machine.srt (8.4 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)
06 - Conclusion
• 01 - Summary and next steps.mp4 (2.5 MB)
• 01 - Summary and next steps.srt (3.2 KB)
• Bonus Resources.txt (0.4 KB)
Ex_Files_Full_Stack_Deep_Learning_Python Exercise Files final_code datasets
• emnist-letters-test.csv (27.3 MB)
• emnist-letters-train.csv (163.7 MB)
• demo_01_EMNISTClassificationUsingDNN.ipynb (1.7 MB)
• demo_02_EMNISTClassificationUsingCNN.ipynb (3.1 MB)
• demo_03_ModelDeployment.ipynb (37.7 KB)
ipynb_checkpoints
• demo_01_EMNISTClassificationUsingDNN-checkpoint.ipynb (1.7 MB)
• demo_03_ModelDeployment-checkpoint.ipynb (46.3 KB)