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Full-Stack Deep Learning with Python (2026)

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Full-Stack Deep Learning with Python (2026)

Torrent Contents Size: 498 MB

Full-Stack Deep Learning with Python (2026)
Bonus Resources.txt
TXT
102.4 B
Ex_Files_FullStack_Deep_Learning
ExerciseFiles
datasets
emnist-letters-test.csv
CSV
27.3 MB
emnist-letters-train.csv
CSV
163.7 MB
Get Bonus Downloads Here.url
URL
204.8 B
demo_01_EMNISTClassificationUsingDNN.ipynb
IPYNB
1.7 MB
demo_02_EMNISTClassificationUsingCNN.ipynb
IPYNB
1.5 MB
demo_03_ModelDeployment.ipynb
IPYNB
41.1 KB
~Get Your Files Here !
01 - Introduction
01 - Full-stack landscape and strategy.mp4
MP4
6.2 MB
01 - Full-stack landscape and strategy.srt
SRT
8.5 KB
02 - 1. An Overview of Full-Stack Deep Learning
01 - Components Planning and data collection.mp4
MP4
8.1 MB
01 - Components Planning and data collection.srt
SRT
11.8 KB
02 - Components Model training and deployment.mp4
MP4
5.2 MB
02 - Components Model training and deployment.srt
SRT
6.9 KB
03 - 2. MLOps with MLflow
01 - Machine learning operations (MLOps).mp4
MP4
8.5 MB
01 - Machine learning operations (MLOps).srt
SRT
9.8 KB
02 - Managing the ML lifecycle with MLflow.mp4
MP4
6.3 MB
02 - Managing the ML lifecycle with MLflow.srt
SRT
7.2 KB
03 - Setting up the environment on Google Colab.mp4
MP4
16.6 MB
03 - Setting up the environment on Google Colab.srt
SRT
9.2 KB
04 - 3. Model Training and Evaluation Using MLflow
01 - Loading and exploring the EMNIST dataset.mp4
MP4
12.4 MB
01 - Loading and exploring the EMNIST dataset.srt
SRT
8.7 KB
02 - Logging metrics parameters and artifacts in MLflow.mp4
MP4
16.4 MB
02 - Logging metrics parameters and artifacts in MLflow.srt
SRT
12.7 KB
03 - Set up the dataset and data loader.mp4
MP4
8.4 MB
03 - Set up the dataset and data loader.srt
SRT
6.1 KB
04 - Configuring the image classification DNN model.mp4
MP4
10.8 MB
04 - Configuring the image classification DNN model.srt
SRT
8.1 KB
05 - 4. Hyperparameter Tuning with Optuna
01 - Setting up the objective function for hyperparameter tuning.mp4
MP4
14.8 MB
01 - Setting up the objective function for hyperparameter tuning.srt
SRT
10.6 KB
02 - Hyperparameter optimization with Optuna and MLflow.mp4
MP4
15.7 MB
02 - Hyperparameter optimization with Optuna and MLflow.srt
SRT
12 KB
03 - Identifying the best model.mp4
MP4
7.2 MB
03 - Identifying the best model.srt
SRT
5.1 KB
04 - Registering a model with the MLflow registry.mp4
MP4
7.3 MB
04 - Registering a model with the MLflow registry.srt
SRT
6.4 KB
06 - 5. Model Deployment and Predictions
01 - Setting up MLflow on the local machine.mp4
MP4
9.4 MB
01 - Setting up MLflow on the local machine.srt
SRT
8.9 KB
02 - Workaround to get model artifacts on local machine.mp4
MP4
5.1 MB
02 - Workaround to get model artifacts on local machine.srt
SRT
4.3 KB
03 - Deploying and serving the model locally.mp4
MP4
14.2 MB
03 - Deploying and serving the model locally.srt
SRT
10.4 KB
07 - Conclusion
01 - Summary and next steps.mp4
MP4
2.9 MB
01 - Summary and next steps.srt
SRT
3.3 KB
05 - Training a model within an MLflow run.mp4
MP4
11.8 MB
05 - Training a model within an MLflow run.srt
SRT
6 KB
06 - Exploring parameters and metrics in MLflow.mp4
MP4
11 MB
06 - Exploring parameters and metrics in MLflow.srt
SRT
9.4 KB
07 - Making predictions using MLflow artifacts.mp4
MP4
13 MB
07 - Making predictions using MLflow artifacts.srt
SRT
9.2 KB
08 - Preparing data for image classification using CNN.mp4
MP4
10.8 MB
08 - Preparing data for image classification using CNN.srt
SRT
6.4 KB
09 - Configuring and training the model using MLflow runs.mp4
MP4
16.1 MB
09 - Configuring and training the model using MLflow runs.srt
SRT
10.5 KB
10 - Visualizing charts metrics and parameters on MLflow.mp4
MP4
16.6 MB
10 - Visualizing charts metrics and parameters on MLflow.srt
SRT
11.7 KB
04 - Running MLflow and using ngrok to access the MLflow UI.mp4
MP4
12.9 MB
04 - Running MLflow and using ngrok to access the MLflow UI.srt
SRT
10.6 KB
03 - Artifacts in full-stack deep learning.mp4
MP4
3.4 MB
03 - Artifacts in full-stack deep learning.srt
SRT
4.4 KB
04 - Tools Compute, orchestration, and experiments.mp4
MP4
5.7 MB
04 - Tools Compute, orchestration, and experiments.srt
SRT
7.7 KB
05 - Tools Versioning, labeling, and feature stores.mp4
MP4
4.8 MB
05 - Tools Versioning, labeling, and feature stores.srt
SRT
6.6 KB
06 - Tools Deep learning frameworks and debugging.mp4
MP4
5.5 MB
06 - Tools Deep learning frameworks and debugging.srt
SRT
6.9 KB
07 - Tools APIs, UIs, CICD, and monitoring.mp4
MP4
7 MB
07 - Tools APIs, UIs, CICD, and monitoring.srt
SRT
9.2 KB
02 - Full-stack deep learning MLOps and MLflow.mp4
MP4
8.5 MB
02 - Full-stack deep learning MLOps and MLflow.srt
SRT
10.1 KB
03 - Prerequisites.mp4
MP4
869.9 KB
03 - Prerequisites.srt
SRT
1.1 KB

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