Full-Stack Deep Learning with Python (2026)

Files:

• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 01 - Introduction
• 01 - Full-stack landscape and strategy.mp4 (6.2 MB)
• 01 - Full-stack landscape and strategy.srt (8.5 KB)
• 02 - Full-stack deep learning MLOps and MLflow.mp4 (8.5 MB)
• 02 - Full-stack deep learning MLOps and MLflow.srt (10.1 KB)
• 03 - Prerequisites.mp4 (869.9 KB)
• 03 - Prerequisites.srt (1.1 KB)
02 - 1. An Overview of Full-Stack Deep Learning
• 01 - Components Planning and data collection.mp4 (8.1 MB)
• 01 - Components Planning and data collection.srt (11.8 KB)
• 02 - Components Model training and deployment.mp4 (5.2 MB)
• 02 - Components Model training and deployment.srt (6.9 KB)
• 03 - Artifacts in full-stack deep learning.mp4 (3.4 MB)
• 03 - Artifacts in full-stack deep learning.srt (4.4 KB)
• 04 - Tools Compute, orchestration, and experiments.mp4 (5.7 MB)
• 04 - Tools Compute, orchestration, and experiments.srt (7.7 KB)
• 05 - Tools Versioning, labeling, and feature stores.mp4 (4.8 MB)
• 05 - Tools Versioning, labeling, and feature stores.srt (6.6 KB)
• 06 - Tools Deep learning frameworks and debugging.mp4 (5.5 MB)
• 06 - Tools Deep learning frameworks and debugging.srt (6.9 KB)
• 07 - Tools APIs, UIs, CICD, and monitoring.mp4 (7.0 MB)
• 07 - Tools APIs, UIs, CICD, and monitoring.srt (9.2 KB)
03 - 2. MLOps with MLflow
• 01 - Machine learning operations (MLOps).mp4 (8.5 MB)
• 01 - Machine learning operations (MLOps).srt (9.8 KB)
• 02 - Managing the ML lifecycle with MLflow.mp4 (6.3 MB)
• 02 - Managing the ML lifecycle with MLflow.srt (7.2 KB)
• 03 - Setting up the environment on Google Colab.mp4 (16.6 MB)
• 03 - Setting up the environment on Google Colab.srt (9.2 KB)
• 04 - Running MLflow and using ngrok to access the MLflow UI.mp4 (12.9 MB)
• 04 - Running MLflow and using ngrok to access the MLflow UI.srt (10.6 KB)
04 - 3. Model Training and Evaluation Using MLflow
• 01 - Loading and exploring the EMNIST dataset.mp4 (12.4 MB)
• 01 - Loading and exploring the EMNIST dataset.srt (8.7 KB)
• 02 - Logging metrics parameters and artifacts in MLflow.mp4 (16.4 MB)
• 02 - Logging metrics parameters and artifacts in MLflow.srt (12.7 KB)
• 03 - Set up the dataset and data loader.mp4 (8.4 MB)
• 03 - Set up the dataset and data loader.srt (6.1 KB)
• 04 - Configuring the image classification DNN model.mp4 (10.8 MB)
• 04 - Configuring the image classification DNN model.srt (8.1 KB)
• 05 - Training a model within an MLflow run.mp4 (11.8 MB)
• 05 - Training a model within an MLflow run.srt (6.0 KB)
• 06 - Exploring parameters and metrics in MLflow.mp4 (11.0 MB)
• 06 - Exploring parameters and metrics in MLflow.srt (9.4 KB)
• 07 - Making predictions using MLflow artifacts.mp4 (13.0 MB)
• 07 - Making predictions using MLflow artifacts.srt (9.2 KB)
• 08 - Preparing data for image classification using CNN.mp4 (10.8 MB)
• 08 - Preparing data for image classification using CNN.srt (6.4 KB)
• 09 - Configuring and training the model using MLflow runs.mp4 (16.1 MB)
• 09 - Configuring and training the model using MLflow runs.srt (10.5 KB)
• 10 - Visualizing charts metrics and parameters on MLflow.mp4 (16.6 MB)
• 10 - Visualizing charts metrics and parameters on MLflow.srt (11.7 KB)
05 - 4. Hyperparameter Tuning with Optuna
• 01 - Setting up the objective function for hyperparameter tuning.mp4 (14.8 MB)
• 01 - Setting up the objective function for hyperparameter tuning.srt (10.6 KB)
• 02 - Hyperparameter optimization with Optuna and MLflow.mp4 (15.7 MB)
• 02 - Hyperparameter optimization with Optuna and MLflow.srt (12.0 KB)
• 03 - Identifying the best model.mp4 (7.2 MB)
• 03 - Identifying the best model.srt (5.1 KB)
• 04 - Registering a model with the MLflow registry.mp4 (7.3 MB)
• 04 - Registering a model with the MLflow registry.srt (6.4 KB)
06 - 5. Model Deployment and Predictions
• 01 - Setting up MLflow on the local machine.mp4 (9.4 MB)
• 01 - Setting up MLflow on the local machine.srt (8.9 KB)
• 02 - Workaround to get model artifacts on local machine.mp4 (5.1 MB)
• 02 - Workaround to get model artifacts on local machine.srt (4.3 KB)
• 03 - Deploying and serving the model locally.mp4 (14.2 MB)
• 03 - Deploying and serving the model locally.srt (10.4 KB)
07 - Conclusion
• 01 - Summary and next steps.mp4 (2.9 MB)
• 01 - Summary and next steps.srt (3.3 KB)
• Bonus Resources.txt (0.1 KB)
Ex_Files_FullStack_Deep_Learning ExerciseFiles 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 (1.5 MB)
• demo_03_ModelDeployment.ipynb (41.1 KB)