Download Full-Stack Deep Learning with Python (2026) torrent

Full-Stack Deep Learning with Python (2026)

Torrent:	Full-Stack Deep Learning with Python (2026)
Description:	Full-Stack Deep Learning with Python (2026) https://WebToolTip.com Released: 03/2026 Duration: 2h 35m \| .MP4 1280x720, 30 fps(r) \| AAC, 48000 Hz, 2ch \| 337 MB Level: Advanced \| Genre: eLearning \| Language: English Full-stack deep learning encompasses the complete lifecycle of building and deploying machine learning systems—from project planning and data preparation to model training, optimization, and deployment. In this course, join data engineer Janani Ravi as she explores each stage of the lifecycle in Python, using MLflow for MLOps and Optuna for hyperparameter tuning. Learn how to manage machine learning artifacts and environments for reproducibility and scalability, and practice deploying models to serve real-world applications. Upon completing this course, you’ll be equipped with the skills you need to automate and optimize machine learning processes and build full-stack deep learning systems from end to end. This course was created by Loonycorn. We are pleased to host this content in our library.
Downloads:	200
Category:	Other /Tutorials
Size:	498 MB
Show Files »	[ WebToolTip.com ] Full-Stack Deep Learning with Python (2026) 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) files
Added:	2026-04-05 16:13:00
Language:	English
Peers:	Seeders : 16 , Leechers : 2
Release name:	Full-Stack Deep Learning with Python (2026)
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