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种子名称： [DesireCourse.Net] Udemy - Deploy Machine Learning & NLP Models with Dockers (DevOps)
文件类型： 视频
文件数目： 54个文件
文件大小： 2.15 GB
收录时间： 2021-7-26 00:26
已经下载： 3次
资源热度： 343
最近下载： 2025-1-16 07:31

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[DesireCourse.Net] Udemy - Deploy Machine Learning & NLP Models with Dockers (DevOps).torrent

1. Course Overview/1. Introduction.mp410.46MB

1. Course Overview/2. I have a model. Now what.mp46.09MB

1. Course Overview/3. Skills Checklist.mp47.41MB

1. Course Overview/4. Learning Goals.mp44.3MB

2. Docker basics/1. Why docker.mp420.39MB

2. Docker basics/2. What are docker containers.mp412.05MB

2. Docker basics/3. Importance of docker containers in machine learning.mp414.79MB

2. Docker basics/4. Where devops meets data science.mp411.62MB

2. Docker basics/5. Summary.mp42.05MB

3. Flask basics/1. Introduction.mp44.09MB

3. Flask basics/2. Setting up a Flask Project.mp49.19MB

3. Flask basics/3. Simple Flask API to add two numbers.mp436.45MB

3. Flask basics/4. Taking user input with GET requests.mp433.46MB

3. Flask basics/5. POST request with Flask.mp447.95MB

3. Flask basics/6. Using Flask in the context of Machine Learning.mp430.27MB

4. Exposing a Random Forest Machine Learning service as an API/1. Introduction.mp43.94MB

4. Exposing a Random Forest Machine Learning service as an API/2. API & Dataset Overview.mp47.58MB

4. Exposing a Random Forest Machine Learning service as an API/3. Training the Random Forest model.mp445.78MB

4. Exposing a Random Forest Machine Learning service as an API/4. Pickling the Random Forest model.mp433.86MB

4. Exposing a Random Forest Machine Learning service as an API/5. Exposing the Random Forest model as a Flask API.mp454.26MB

4. Exposing a Random Forest Machine Learning service as an API/6. Testing the API model.mp436.61MB

4. Exposing a Random Forest Machine Learning service as an API/7. Providing file input to Flask API.mp475.71MB

4. Exposing a Random Forest Machine Learning service as an API/8. Flasgger for autogenerating UI.mp485.17MB

4. Exposing a Random Forest Machine Learning service as an API/9. Summary.mp413.49MB

5. Writing and building the Dockerfile/1. Introduction.mp42.22MB

5. Writing and building the Dockerfile/2. Base Image & FROM command.mp414.76MB

5. Writing and building the Dockerfile/3. COPY and EXPOSE commands.mp421.74MB

5. Writing and building the Dockerfile/4. WORKDIR, RUN and CMD commands.mp429.55MB

5. Writing and building the Dockerfile/5. Preparing the flask scripts for dockerizing.mp421.59MB

5. Writing and building the Dockerfile/6. Writing the Dockerfile.mp445.99MB

5. Writing and building the Dockerfile/7. Building the docker image.mp474.79MB

5. Writing and building the Dockerfile/8. Running the Random Forest model on Docker.mp478.73MB

6. Building a production grade Docker application/1. Introduction.mp426.52MB

6. Building a production grade Docker application/2. Overall Architecture.mp423.62MB

6. Building a production grade Docker application/3. Configuring the WSGI file.mp462.43MB

6. Building a production grade Docker application/4. Writing a production grade Dockerfile.mp461.41MB

6. Building a production grade Docker application/5. Running and debugging a docker container in production.mp486.01MB

7. Building NLP based Text Clustering application/1. Introduction.mp418.13MB

7. Building NLP based Text Clustering application/2. Stemming & Lemmatization for cleaner text.mp490.87MB

7. Building NLP based Text Clustering application/3. Converting unstructured to structured data.mp495.68MB

7. Building NLP based Text Clustering application/4. KMeans Clustering.mp482.2MB

7. Building NLP based Text Clustering application/5. Preparing the excel output.mp4108.84MB

7. Building NLP based Text Clustering application/6. Making the output Downloadable.mp465.5MB

7. Building NLP based Text Clustering application/7. Finding top keywords for kmeans clusters.mp474.96MB

7. Building NLP based Text Clustering application/8. Final output with charts.mp476.39MB

7. Building NLP based Text Clustering application/9. Summary.mp416.46MB

8. API for image recognition with deep learning/1. Introduction.mp45.07MB

8. API for image recognition with deep learning/2. Visualizing the input images.mp468.33MB

8. API for image recognition with deep learning/3. Preparing the input images.mp489.54MB

8. API for image recognition with deep learning/4. Building the deep learning model.mp493.14MB

8. API for image recognition with deep learning/5. Training and saving the trained deep learning model.mp431.02MB

8. API for image recognition with deep learning/6. Generating test images.mp434.23MB

8. API for image recognition with deep learning/7. Flask API wrapper for making predictions.mp478.29MB

8. API for image recognition with deep learning/8. Summary.mp418.42MB