Full-Stack Deep Learning with Python (2026)

seeders: 16
leechers: 2
Added 2 months ago by freecoursewb in Other

Download Fast Safe Anonymous
movies, software, shows...

Files

Full-Stack Deep Learning with Python (2026) (Size: 498 MB)
  Bonus Resources.txt 102.4 B
  Ex_Files_FullStack_Deep_Learning
  ExerciseFiles
  datasets
  emnist-letters-test.csv 27.3 MB
  emnist-letters-train.csv 163.7 MB
  Get Bonus Downloads Here.url 204.8 B
  demo_01_EMNISTClassificationUsingDNN.ipynb 1.7 MB
  demo_02_EMNISTClassificationUsingCNN.ipynb 1.5 MB
  demo_03_ModelDeployment.ipynb 41.1 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 - 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 - 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 - 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 - 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 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
  05 - Training a model within an MLflow run.mp4 11.8 MB
  05 - Training a model within an MLflow run.srt 6 KB
  06 - Exploring parameters and metrics in MLflow.mp4 11 MB
  06 - Exploring parameters and metrics in MLflow.srt 9.4 KB
  07 - Making predictions using MLflow artifacts.mp4 13 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
  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
  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 MB
  07 - Tools APIs, UIs, CICD, and monitoring.srt 9.2 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

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.

Related Torrents

torrent name size uploader age seed leech
52
2
3
0
22