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| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 0 |
164б |
| 001 Introduction.en.srt |
2.67Кб |
| 001 Introduction.mp4 |
11.45Мб |
| 002 What is a Model_.en.srt |
1.67Кб |
| 002 What is a Model_.mp4 |
4.78Мб |
| 003 How do we create a Model_.en.srt |
3.02Кб |
| 003 How do we create a Model_.mp4 |
6.70Мб |
| 004 Types of Machine Learning.en.srt |
4.82Кб |
| 004 Types of Machine Learning.mp4 |
13.12Мб |
| 005 Creating a Spyder development environment.en.srt |
2.31Кб |
| 005 Creating a Spyder development environment.mp4 |
17.80Мб |
| 006 python-np-pd-plt.zip |
1.02Кб |
| 006 Python NumPy Pandas Matplotlib crash course.en.srt |
16.69Кб |
| 006 Python NumPy Pandas Matplotlib crash course.mp4 |
136.73Мб |
| 007 Building and evaluating a Classification Model.mp4 |
111.66Мб |
| 007 git-hub-link.txt |
51б |
| 007 ml-classification.zip |
1.06Кб |
| 008 Saving the Model and the Scaler.en.srt |
4.93Кб |
| 008 Saving the Model and the Scaler.mp4 |
26.61Мб |
| 009 Predicting locally with deserialized Pickle objects.en.srt |
3.42Кб |
| 009 Predicting locally with deserialized Pickle objects.mp4 |
21.82Мб |
| 009 use-model.zip |
337б |
| 010 use-model-colab.zip |
2.51Кб |
| 010 Using the Model in Google Colab environment.en.srt |
5.15Кб |
| 010 Using the Model in Google Colab environment.mp4 |
25.29Мб |
| 011 flask-hello-world.zip |
672б |
| 011 Flask REST API Hello World.en.srt |
5.08Кб |
| 011 Flask REST API Hello World.mp4 |
33.49Мб |
| 012 classifier-rest-service.zip |
822б |
| 012 Creating a REST API for the Model.en.srt |
4.44Кб |
| 012 Creating a REST API for the Model.mp4 |
43.86Мб |
| 013 Signing up for a Google Cloud free trial.en.srt |
1.79Кб |
| 013 Signing up for a Google Cloud free trial.mp4 |
9.01Мб |
| 014 classifier-rest-service-on-GCP.zip |
898б |
| 014 Hosting the Machine Learning REST API on the Cloud.mp4 |
52.17Мб |
| 015 Deleting the VM instance.en.srt |
783б |
| 015 Deleting the VM instance.mp4 |
3.77Мб |
| 016 google-cloud-functions-serverless-ml.zip |
851б |
| 016 Serverless Machine Learning API using Cloud Functions.en.srt |
11.36Кб |
| 016 Serverless Machine Learning API using Cloud Functions.mp4 |
55.22Мб |
| 017 colab-rest-api.zip |
4.81Кб |
| 017 Creating a REST API on Google Colab.en.srt |
4.13Кб |
| 017 Creating a REST API on Google Colab.mp4 |
29.09Мб |
| 018 Postman REST client.en.srt |
2.15Кб |
| 018 Postman REST client.mp4 |
13.12Мб |
| 019 Understanding Deep Learning Neural Network.en.srt |
6.15Кб |
| 019 Understanding Deep Learning Neural Network.mp4 |
22.35Мб |
| 020 Building and deploying PyTorch models.en.srt |
12.03Кб |
| 020 Building and deploying PyTorch models.mp4 |
64.45Мб |
| 020 save-export-reload-pytorch-models.zip |
11.26Кб |
| 021 Creating a REST API for the PyTorch Model.mp4 |
33.41Мб |
| 021 pytorch-flask.zip |
777б |
| 022 Saving & loading TensorFlow Keras models.mp4 |
38.67Мб |
| 022 tf-serving-save-export.zip |
21.15Кб |
| 023 Understanding Docker containers.en.srt |
3.50Кб |
| 023 Understanding Docker containers.mp4 |
9.08Мб |
| 024 Creating a REST API using TensorFlow Model Server.mp4 |
42.34Мб |
| 024 tf-model-serving.zip |
3.04Кб |
| 025 Converting a PyTorch model to TensorFlow format using ONNX.mp4 |
22.30Мб |
| 025 pytorch-create-save-onnx.zip |
7.48Кб |
| 026 Converting text to numeric values using bag-of-words model.en.srt |
5.79Кб |
| 026 Converting text to numeric values using bag-of-words model.mp4 |
33.34Мб |
| 027 tf-idf model for converting text to numeric values.en.srt |
5.19Кб |
| 027 tf-idf model for converting text to numeric values.mp4 |
29.80Мб |
| 028 Creating and saving text classifier and tf-idf models.en.srt |
12.45Кб |
| 028 Creating and saving text classifier and tf-idf models.mp4 |
67.56Мб |
| 028 text-classifier.zip.zip |
29.80Кб |
| 029 Creating a Twitter developer account.en.srt |
2.51Кб |
| 029 Creating a Twitter developer account.mp4 |
16.84Мб |
| 030 Deploying tf-idf and text classifier models for Twitter sentiment analysis.en.srt |
5.56Кб |
| 030 Deploying tf-idf and text classifier models for Twitter sentiment analysis.mp4 |
40.27Мб |
| 030 twitter-sentiment-analysis.zip |
34.31Кб |
| 031 Creating a text classifier using PyTorch.en.srt |
3.95Кб |
| 031 Creating a text classifier using PyTorch.mp4 |
24.47Мб |
| 031 text-classifier-pytorch.zip |
18.42Кб |
| 032 Creating a REST API for the PyTorch NLP model.mp4 |
27.59Мб |
| 032 pytorch-nlp-rest.zip |
1.02Кб |
| 033 twitter-pytorch-rest.zip |
24.02Кб |
| 033 Twitter sentiment analysis with PyTorch REST API.mp4 |
40.15Мб |
| 034 Creating a text classifier using TensorFlow.en.srt |
1.72Кб |
| 034 Creating a text classifier using TensorFlow.mp4 |
10.45Мб |
| 034 text-classifier-tensorflow.zip |
8.73Кб |
| 035 Creating a REST API for TensforFlow models using Flask.en.srt |
2.76Кб |
| 035 Creating a REST API for TensforFlow models using Flask.mp4 |
19.16Мб |
| 035 tf-nlp-flask-rest.zip |
5.09Мб |
| 036 Serving TensorFlow models serverless.en.srt |
6.98Кб |
| 036 Serving TensorFlow models serverless.mp4 |
45.18Мб |
| 036 tf-serverless.zip |
5.08Мб |
| 037 pytorch-serverless.zip |
20.24Кб |
| 037 Serving PyTorch models serverless.en.srt |
3.25Кб |
| 037 Serving PyTorch models serverless.mp4 |
22.65Мб |
| 038 TensorFlow.js introduction.en.srt |
2.28Кб |
| 038 TensorFlow.js introduction.mp4 |
5.05Мб |
| 039 Installing Visual Studio Code and Live Server.en.srt |
2.82Кб |
| 039 Installing Visual Studio Code and Live Server.mp4 |
15.88Мб |
| 040 javascript-crash-course.zip |
27.27Кб |
| 040 JavaScript crash course (optional).en.srt |
24.68Кб |
| 040 JavaScript crash course (optional).mp4 |
147.10Мб |
| 041 Adding TensforFlow.js to a web page.en.srt |
2.00Кб |
| 041 Adding TensforFlow.js to a web page.mp4 |
15.36Мб |
| 041 tfjs1.zip |
542б |
| 042 Basic tensor operations using TensorFlow.js.en.srt |
1.66Кб |
| 042 Basic tensor operations using TensorFlow.js.mp4 |
11.39Мб |
| 042 tfjs2.zip |
642б |
| 043 Deploying Keras model on a web page using TensorFlow.js.en.srt |
3.63Кб |
| 043 Deploying Keras model on a web page using TensorFlow.js.mp4 |
23.90Мб |
| 043 tfjs4.zip |
10.30Кб |
| 044 Deriving formula from a Linear Regression Model.en.srt |
11.69Кб |
| 044 Deriving formula from a Linear Regression Model.mp4 |
64.05Мб |
| 044 linear-regression.zip |
1.00Кб |
| 045 Machine Learning Operations (MLOps).mp4 |
9.05Мб |
| 046 MLflow Introduction.en.srt |
1.06Кб |
| 046 MLflow Introduction.mp4 |
4.81Мб |
| 047 ml-pipeline-mlfow.zip |
841б |
| 047 Tracking Model training experiments with MLfLow.en.srt |
10.04Кб |
| 047 Tracking Model training experiments with MLfLow.mp4 |
71.46Мб |
| 048 Why track ML experiments_.en.srt |
1.06Кб |
| 048 Why track ML experiments_.mp4 |
2.28Мб |
| 049 Running MLflow on Colab.en.srt |
3.14Кб |
| 049 Running MLflow on Colab.mp4 |
25.11Мб |
| 049 running-mlflow-on-colab.zip |
5.72Кб |
| 050 mlflow-pytorch.zip |
7.58Кб |
| 050 Tracking PyTorch experiments with MLflow.en.srt |
2.77Кб |
| 050 Tracking PyTorch experiments with MLflow.mp4 |
19.12Мб |
| 051 Deploying Models with MLflow.en.srt |
2.29Кб |
| 051 Deploying Models with MLflow.mp4 |
13.07Мб |
| 051 mlflow-deploy.zip |
635б |
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| TutsNode.com.txt |
63б |