Udemy - Deployment of Machine Learning Models in Production | Python
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| Torrent Hash : | F2BF4C45530F1331A1BAA6FA7C699E08A23D9EBA |
| Torrent Added : | at June 2, 2023, 12:16 a.m. in Other |
| Torrent Size : | 4.1 GB |
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Udemy - Deployment of Machine Learning Models in Production | Python
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Udemy - Deployment of Machine Learning Models in Production | Python
030 DistilBERT-App.zip -
TutsNode.com.txt -
003 Sentiment-Classification-using-BERT.zip -
069 NGINX-uWSGI-and-Flask-Installation-Guide-Jupyter-Notebook.zip -
060 NGINX-uWSGI-and-Flask-Installation-Guide-Jupyter-Notebook.zip -
068 Congrats! You Have Deployed ML Model in Production.en.srt -
003 DO NOT SKIP IT _ Download Working Files.html -
070 FastText Research Paper Review.en.srt -
041 Deploy DistilBERT Model at Your Local Machine.en.srt -
079 Preparing Prediction APIs.en.srt -
050 Make Your ML Model Accessible to the World.en.srt -
049 Deploy ML Model on EC2 Server.en.srt -
072 Data Preparation.en.srt -
057 Install TensorFlow 2 and KTRAIN.en.srt -
012 BERT Model Training.en.srt -
046 Install TensorFlow 2 and KTRAIN.en.srt -
008 Must Read.html -
019 Number of Characters Distribution in Tweets.en.srt -
016 BERT Intro - Disaster Tweets Dataset Understanding.en.srt -
027 Word Embeddings and Classification with Deep Learning Part 2.en.srt -
058 Create Extra RAM from SSD by Memory Swapping.en.srt -
059 Deploy DistilBERT ML Model on EC2 Ubuntu Machine.en.srt -
037 Flask App Preparation.en.srt -
015 Resources Folder.html -
0 -
070 FastText Research Paper Review.mp4 -
040 Build Predict API.en.srt -
081 Testing Prediction API at AWS Ubuntu Machine.en.srt -
067 Configuring NGINX with uWSGI, and Flask Server.en.srt -
029 BERT Model Evaluation.en.srt -
075 Creating Fresh Ubuntu Machine.en.srt -
032 Data Preparation.en.srt -
030 What is DistilBERT_.en.srt -
063 Setting Up uWSGI Server.en.srt -
011 Train-Test Split and Preprocess with BERT.en.srt -
083 Deploy FastText Model in Production with NGINX, uWSGI, and Flask.en.srt -
033 DistilBERT Model Training.en.srt -
069 What is Multi-Label Classification_.en.srt -
026 Word Embeddings and Classification with Deep Learning Part 1.en.srt -
025 Classification with Word2Vec and SVM.en.srt -
038 Run Your First Flask Application.en.srt -
028 BERT Model Building and Training.en.srt -
051 Install Git Bash and Commander Terminal on Local Computer.en.srt -
014 Saving and Loading Fine Tuned Model.en.srt -
030 Sentiment-Classification-using-DistilBERT.zip -
047 Run Your First Flask Application on AWS EC2.en.srt -
066 Start API Services at System Startup.en.srt -
071 Notebook Setup.en.srt -
076 Setting Python3 and PIP3 Alias.en.srt -
024 Classification with TFIDF and SVM.en.srt -
073 FastText Model Training.en.srt -
078 Making Your Server Ready.en.srt -
080 Testing Prediction API at Local Machine.en.srt -
082 Configuring uWSGI Server.en.srt -
042 Create AWS Account.en.srt -
052 Create AWS Account.en.srt -
044 Connect EC2 Instance from Windows 10.en.srt -
061 Virtual Environment Setup.en.srt -
062 Setting Up Flask Server.en.srt -
[TGx]Downloaded from torrentgalaxy.to .txt -
054 Connect AWS Ubuntu (Linux) from Windows Computer.en.srt -
064 Installing TensorFlow 2 and KTRAIN.en.srt -
021 Most and Least Common Words.en.srt -
018 Target Class Distribution.en.srt -
004 What is BERT.en.srt -
020 Number of Words, Average Words Length, and Stop words Distribution in Tweets.en.srt -
043 Create Free Windows EC2 Instance.en.srt -
055 Install PIP3 on AWS Ubuntu.en.srt -
039 Predict Sentiment at Your Local Machine.en.srt -
074 FastText Model Evaluation and Saving at Google Drive.en.srt -
031 Notebook Setup.en.srt -
013 Testing Fine Tuned BERT Model.en.srt -
034 Save Model at Google Drive.en.srt -
036 Download Fine Tuned DistilBERT Model.en.srt -
006 Going Deep Inside ktrain Package.en.srt -
009 Installing ktrain.en.srt -
005 What is ktrain.en.srt -
060 NGINX Introduction.en.srt -
010 Loading Dataset.en.srt -
022 One-Shot Data Cleaning.en.srt -
053 Launch Ubuntu Machine on EC2.en.srt -
001 Welcome.en.srt -
048 Transfer DistilBERT Model to EC2 Flask Server.en.srt -
065 Configuring uWSGI Server.en.srt -
002 Introduction.en.srt -
023 Disaster Words Visualization with Word Cloud.en.srt -
077 Creating 4GB Extra RAM by Memory Swapping.en.srt -
017 Download Dataset.en.srt -
035 Model Evaluation.en.srt -
069 FastText-Multi-Label-Text-Classification.zip -
045 Install Python on EC2 Windows 10.en.srt -
056 Update and Upgrade Your Ubuntu Packages.en.srt -
007 Notebook Setup.en.srt -
1 -
016 BERT Intro - Disaster Tweets Dataset Understanding.mp4 -
2 -
063 Setting Up uWSGI Server.mp4 -
3 -
057 Install TensorFlow 2 and KTRAIN.mp4 -
4 -
067 Configuring NGINX with uWSGI, and Flask Server.mp4 -
5 -
068 Congrats! You Have Deployed ML Model in Production.mp4 -
6 -
058 Create Extra RAM from SSD by Memory Swapping.mp4 -
7 -
019 Number of Characters Distribution in Tweets.mp4 -
8 -
079 Preparing Prediction APIs.mp4 -
9 -
081 Testing Prediction API at AWS Ubuntu Machine.mp4 -
10 -
078 Making Your Server Ready.mp4 -
11 -
030 What is DistilBERT_.mp4 -
12 -
027 Word Embeddings and Classification with Deep Learning Part 2.mp4 -
13 -
049 Deploy ML Model on EC2 Server.mp4 -
14 -
041 Deploy DistilBERT Model at Your Local Machine.mp4 -
15 -
072 Data Preparation.mp4 -
16 -
050 Make Your ML Model Accessible to the World.mp4 -
17 -
046 Install TensorFlow 2 and KTRAIN.mp4 -
18 -
075 Creating Fresh Ubuntu Machine.mp4 -
19 -
083 Deploy FastText Model in Production with NGINX, uWSGI, and Flask.mp4 -
20 -
029 BERT Model Evaluation.mp4 -
21 -
082 Configuring uWSGI Server.mp4 -
22 -
066 Start API Services at System Startup.mp4 -
23 -
061 Virtual Environment Setup.mp4 -
24 -
012 BERT Model Training.mp4 -
25 -
040 Build Predict API.mp4 -
26 -
064 Installing TensorFlow 2 and KTRAIN.mp4 -
27 -
028 BERT Model Building and Training.mp4 -
28 -
032 Data Preparation.mp4 -
29 -
025 Classification with Word2Vec and SVM.mp4 -
30 -
026 Word Embeddings and Classification with Deep Learning Part 1.mp4 -
31 -
044 Connect EC2 Instance from Windows 10.mp4 -
32 -
011 Train-Test Split and Preprocess with BERT.mp4 -
33 -
062 Setting Up Flask Server.mp4 -
34 -
076 Setting Python3 and PIP3 Alias.mp4 -
35 -
043 Create Free Windows EC2 Instance.mp4 -
36 -
071 Notebook Setup.mp4 -
37 -
004 What is BERT.mp4 -
38 -
055 Install PIP3 on AWS Ubuntu.mp4 -
39 -
059 Deploy DistilBERT ML Model on EC2 Ubuntu Machine.mp4 -
40 -
024 Classification with TFIDF and SVM.mp4 -
41 -
021 Most and Least Common Words.mp4 -
42 -
001 Welcome.mp4 -
43 -
023 Disaster Words Visualization with Word Cloud.mp4 -
44 -
033 DistilBERT Model Training.mp4 -
45 -
020 Number of Words, Average Words Length, and Stop words Distribution in Tweets.mp4 -
46 -
051 Install Git Bash and Commander Terminal on Local Computer.mp4 -
47 -
080 Testing Prediction API at Local Machine.mp4 -
48 -
073 FastText Model Training.mp4 -
49 -
077 Creating 4GB Extra RAM by Memory Swapping.mp4 -
50 -
052 Create AWS Account.mp4 -
51 -
042 Create AWS Account.mp4 -
52 -
060 NGINX Introduction.mp4 -
53 -
002 Introduction.mp4 -
54 -
065 Configuring uWSGI Server.mp4 -
55 -
005 What is ktrain.mp4 -
56 -
069 What is Multi-Label Classification_.mp4 -
57 -
054 Connect AWS Ubuntu (Linux) from Windows Computer.mp4 -
58 -
038 Run Your First Flask Application.mp4 -
59 -
022 One-Shot Data Cleaning.mp4 -
60 -
018 Target Class Distribution.mp4 -
61 -
053 Launch Ubuntu Machine on EC2.mp4 -
62 -
006 Going Deep Inside ktrain Package.mp4 -
63 -
009 Installing ktrain.mp4 -
64 -
017 Download Dataset.mp4 -
65 -
047 Run Your First Flask Application on AWS EC2.mp4 -
66 -
014 Saving and Loading Fine Tuned Model.mp4 -
67 -
048 Transfer DistilBERT Model to EC2 Flask Server.mp4 -
68 -
031 Notebook Setup.mp4 -
69 -
034 Save Model at Google Drive.mp4 -
70 -
039 Predict Sentiment at Your Local Machine.mp4 -
71 -
013 Testing Fine Tuned BERT Model.mp4 -
72 -
010 Loading Dataset.mp4 -
73 -
074 FastText Model Evaluation and Saving at Google Drive.mp4 -
74 -
056 Update and Upgrade Your Ubuntu Packages.mp4 -
75 -
069 FastText-App.zip -
76 -
045 Install Python on EC2 Windows 10.mp4 -
77 -
035 Model Evaluation.mp4 -
78 -
007 Notebook Setup.mp4 -
79 -
037 Flask App Preparation.mp4 -
80 -
036 Download Fine Tuned DistilBERT Model.mp4 -
81 -
015 Fine-Tuning-BERT-for-Disaster-Tweets-Classification.zip -
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030 DistilBERT-App.zip -
235.2 MB
TutsNode.com.txt -
63 bytes
003 Sentiment-Classification-using-BERT.zip -
326.9 KB
069 NGINX-uWSGI-and-Flask-Installation-Guide-Jupyter-Notebook.zip -
95.4 KB
060 NGINX-uWSGI-and-Flask-Installation-Guide-Jupyter-Notebook.zip -
86.6 KB
068 Congrats! You Have Deployed ML Model in Production.en.srt -
24.5 KB
003 DO NOT SKIP IT _ Download Working Files.html -
1.8 KB
070 FastText Research Paper Review.en.srt -
20.5 KB
041 Deploy DistilBERT Model at Your Local Machine.en.srt -
20.1 KB
079 Preparing Prediction APIs.en.srt -
20.0 KB
050 Make Your ML Model Accessible to the World.en.srt -
17.7 KB
049 Deploy ML Model on EC2 Server.en.srt -
17.7 KB
072 Data Preparation.en.srt -
17.3 KB
057 Install TensorFlow 2 and KTRAIN.en.srt -
16.5 KB
012 BERT Model Training.en.srt -
15.1 KB
046 Install TensorFlow 2 and KTRAIN.en.srt -
14.7 KB
008 Must Read.html -
1.7 KB
019 Number of Characters Distribution in Tweets.en.srt -
14.6 KB
016 BERT Intro - Disaster Tweets Dataset Understanding.en.srt -
14.2 KB
027 Word Embeddings and Classification with Deep Learning Part 2.en.srt -
14.1 KB
058 Create Extra RAM from SSD by Memory Swapping.en.srt -
13.7 KB
059 Deploy DistilBERT ML Model on EC2 Ubuntu Machine.en.srt -
13.7 KB
037 Flask App Preparation.en.srt -
2.1 KB
015 Resources Folder.html -
926 bytes
0 -
153 bytes
070 FastText Research Paper Review.mp4 -
160.1 MB
040 Build Predict API.en.srt -
13.6 KB
081 Testing Prediction API at AWS Ubuntu Machine.en.srt -
13.5 KB
067 Configuring NGINX with uWSGI, and Flask Server.en.srt -
13.5 KB
029 BERT Model Evaluation.en.srt -
13.1 KB
075 Creating Fresh Ubuntu Machine.en.srt -
13.0 KB
032 Data Preparation.en.srt -
12.7 KB
030 What is DistilBERT_.en.srt -
12.5 KB
063 Setting Up uWSGI Server.en.srt -
12.5 KB
011 Train-Test Split and Preprocess with BERT.en.srt -
11.9 KB
083 Deploy FastText Model in Production with NGINX, uWSGI, and Flask.en.srt -
11.6 KB
033 DistilBERT Model Training.en.srt -
11.6 KB
069 What is Multi-Label Classification_.en.srt -
11.6 KB
026 Word Embeddings and Classification with Deep Learning Part 1.en.srt -
11.3 KB
025 Classification with Word2Vec and SVM.en.srt -
11.1 KB
038 Run Your First Flask Application.en.srt -
11.0 KB
028 BERT Model Building and Training.en.srt -
10.9 KB
051 Install Git Bash and Commander Terminal on Local Computer.en.srt -
10.7 KB
014 Saving and Loading Fine Tuned Model.en.srt -
10.5 KB
030 Sentiment-Classification-using-DistilBERT.zip -
10.5 KB
047 Run Your First Flask Application on AWS EC2.en.srt -
10.5 KB
066 Start API Services at System Startup.en.srt -
10.0 KB
071 Notebook Setup.en.srt -
9.9 KB
076 Setting Python3 and PIP3 Alias.en.srt -
9.8 KB
024 Classification with TFIDF and SVM.en.srt -
9.8 KB
073 FastText Model Training.en.srt -
9.8 KB
078 Making Your Server Ready.en.srt -
9.7 KB
080 Testing Prediction API at Local Machine.en.srt -
9.6 KB
082 Configuring uWSGI Server.en.srt -
9.6 KB
042 Create AWS Account.en.srt -
9.4 KB
052 Create AWS Account.en.srt -
9.4 KB
044 Connect EC2 Instance from Windows 10.en.srt -
9.3 KB
061 Virtual Environment Setup.en.srt -
9.2 KB
062 Setting Up Flask Server.en.srt -
9.1 KB
[TGx]Downloaded from torrentgalaxy.to .txt -
585 bytes
054 Connect AWS Ubuntu (Linux) from Windows Computer.en.srt -
9.1 KB
064 Installing TensorFlow 2 and KTRAIN.en.srt -
8.9 KB
021 Most and Least Common Words.en.srt -
8.7 KB
018 Target Class Distribution.en.srt -
8.6 KB
004 What is BERT.en.srt -
8.5 KB
020 Number of Words, Average Words Length, and Stop words Distribution in Tweets.en.srt -
8.4 KB
043 Create Free Windows EC2 Instance.en.srt -
7.9 KB
055 Install PIP3 on AWS Ubuntu.en.srt -
7.6 KB
039 Predict Sentiment at Your Local Machine.en.srt -
7.2 KB
074 FastText Model Evaluation and Saving at Google Drive.en.srt -
7.1 KB
031 Notebook Setup.en.srt -
7.1 KB
013 Testing Fine Tuned BERT Model.en.srt -
7.0 KB
034 Save Model at Google Drive.en.srt -
7.0 KB
036 Download Fine Tuned DistilBERT Model.en.srt -
2.0 KB
006 Going Deep Inside ktrain Package.en.srt -
6.9 KB
009 Installing ktrain.en.srt -
6.8 KB
005 What is ktrain.en.srt -
6.8 KB
060 NGINX Introduction.en.srt -
6.7 KB
010 Loading Dataset.en.srt -
6.5 KB
022 One-Shot Data Cleaning.en.srt -
6.2 KB
053 Launch Ubuntu Machine on EC2.en.srt -
6.2 KB
001 Welcome.en.srt -
6.2 KB
048 Transfer DistilBERT Model to EC2 Flask Server.en.srt -
6.0 KB
065 Configuring uWSGI Server.en.srt -
6.0 KB
002 Introduction.en.srt -
6.0 KB
023 Disaster Words Visualization with Word Cloud.en.srt -
5.9 KB
077 Creating 4GB Extra RAM by Memory Swapping.en.srt -
5.6 KB
017 Download Dataset.en.srt -
5.5 KB
035 Model Evaluation.en.srt -
4.6 KB
069 FastText-Multi-Label-Text-Classification.zip -
4.5 KB
045 Install Python on EC2 Windows 10.en.srt -
4.3 KB
056 Update and Upgrade Your Ubuntu Packages.en.srt -
3.5 KB
007 Notebook Setup.en.srt -
3.2 KB
1 -
385.5 KB
016 BERT Intro - Disaster Tweets Dataset Understanding.mp4 -
109.8 MB
2 -
203.9 KB
063 Setting Up uWSGI Server.mp4 -
101.7 MB
3 -
262.3 KB
057 Install TensorFlow 2 and KTRAIN.mp4 -
93.6 MB
4 -
425.4 KB
067 Configuring NGINX with uWSGI, and Flask Server.mp4 -
91.8 MB
5 -
224.5 KB
068 Congrats! You Have Deployed ML Model in Production.mp4 -
84.9 MB
6 -
98.7 KB
058 Create Extra RAM from SSD by Memory Swapping.mp4 -
83.7 MB
7 -
287.2 KB
019 Number of Characters Distribution in Tweets.mp4 -
83.5 MB
8 -
483.7 KB
079 Preparing Prediction APIs.mp4 -
80.8 MB
9 -
244.5 KB
081 Testing Prediction API at AWS Ubuntu Machine.mp4 -
77.5 MB
10 -
562.3 KB
078 Making Your Server Ready.mp4 -
76.5 MB
11 -
524.5 KB
030 What is DistilBERT_.mp4 -
74.1 MB
12 -
969.5 KB
027 Word Embeddings and Classification with Deep Learning Part 2.mp4 -
73.6 MB
13 -
438.7 KB
049 Deploy ML Model on EC2 Server.mp4 -
71.0 MB
14 -
1.9 KB
041 Deploy DistilBERT Model at Your Local Machine.mp4 -
69.5 MB
15 -
544.1 KB
072 Data Preparation.mp4 -
67.4 MB
16 -
594.7 KB
050 Make Your ML Model Accessible to the World.mp4 -
66.8 MB
17 -
197.7 KB
046 Install TensorFlow 2 and KTRAIN.mp4 -
66.6 MB
18 -
436.3 KB
075 Creating Fresh Ubuntu Machine.mp4 -
59.3 MB
19 -
717.0 KB
083 Deploy FastText Model in Production with NGINX, uWSGI, and Flask.mp4 -
58.6 MB
20 -
384.8 KB
029 BERT Model Evaluation.mp4 -
58.4 MB
21 -
581.3 KB
082 Configuring uWSGI Server.mp4 -
58.3 MB
22 -
743.9 KB
066 Start API Services at System Startup.mp4 -
58.1 MB
23 -
880.0 KB
061 Virtual Environment Setup.mp4 -
57.7 MB
24 -
311.1 KB
012 BERT Model Training.mp4 -
56.8 MB
25 -
166.5 KB
040 Build Predict API.mp4 -
56.2 MB
26 -
838.5 KB
064 Installing TensorFlow 2 and KTRAIN.mp4 -
56.1 MB
27 -
944.4 KB
028 BERT Model Building and Training.mp4 -
55.1 MB
28 -
875.2 KB
032 Data Preparation.mp4 -
54.6 MB
29 -
402.6 KB
025 Classification with Word2Vec and SVM.mp4 -
52.9 MB
30 -
108.0 KB
026 Word Embeddings and Classification with Deep Learning Part 1.mp4 -
52.9 MB
31 -
130.7 KB
044 Connect EC2 Instance from Windows 10.mp4 -
52.5 MB
32 -
525.2 KB
011 Train-Test Split and Preprocess with BERT.mp4 -
51.4 MB
33 -
585.2 KB
062 Setting Up Flask Server.mp4 -
50.7 MB
34 -
269.4 KB
076 Setting Python3 and PIP3 Alias.mp4 -
49.3 MB
35 -
700.7 KB
043 Create Free Windows EC2 Instance.mp4 -
47.7 MB
36 -
332.1 KB
071 Notebook Setup.mp4 -
45.8 MB
37 -
248.1 KB
004 What is BERT.mp4 -
45.3 MB
38 -
738.3 KB
055 Install PIP3 on AWS Ubuntu.mp4 -
44.6 MB
39 -
397.9 KB
059 Deploy DistilBERT ML Model on EC2 Ubuntu Machine.mp4 -
44.2 MB
40 -
819.9 KB
024 Classification with TFIDF and SVM.mp4 -
44.2 MB
41 -
835.9 KB
021 Most and Least Common Words.mp4 -
43.4 MB
42 -
628.8 KB
001 Welcome.mp4 -
42.6 MB
43 -
414.7 KB
023 Disaster Words Visualization with Word Cloud.mp4 -
42.2 MB
44 -
863.5 KB
033 DistilBERT Model Training.mp4 -
41.6 MB
45 -
413.5 KB
020 Number of Words, Average Words Length, and Stop words Distribution in Tweets.mp4 -
41.0 MB
46 -
24 bytes
051 Install Git Bash and Commander Terminal on Local Computer.mp4 -
40.9 MB
47 -
83.2 KB
080 Testing Prediction API at Local Machine.mp4 -
40.2 MB
48 -
802.8 KB
073 FastText Model Training.mp4 -
38.6 MB
49 -
387.5 KB
077 Creating 4GB Extra RAM by Memory Swapping.mp4 -
37.0 MB
50 -
993.2 KB
052 Create AWS Account.mp4 -
36.6 MB
51 -
382.2 KB
042 Create AWS Account.mp4 -
36.6 MB
52 -
387.7 KB
060 NGINX Introduction.mp4 -
36.6 MB
53 -
390.2 KB
002 Introduction.mp4 -
35.8 MB
54 -
253.5 KB
065 Configuring uWSGI Server.mp4 -
32.9 MB
55 -
145.2 KB
005 What is ktrain.mp4 -
32.8 MB
56 -
167.7 KB
069 What is Multi-Label Classification_.mp4 -
32.7 MB
57 -
265.8 KB
054 Connect AWS Ubuntu (Linux) from Windows Computer.mp4 -
32.5 MB
58 -
462.7 KB
038 Run Your First Flask Application.mp4 -
32.4 MB
59 -
633.6 KB
022 One-Shot Data Cleaning.mp4 -
32.0 MB
60 -
993.3 KB
018 Target Class Distribution.mp4 -
31.5 MB
61 -
536.5 KB
053 Launch Ubuntu Machine on EC2.mp4 -
31.4 MB
62 -
625.6 KB
006 Going Deep Inside ktrain Package.mp4 -
31.3 MB
63 -
700.1 KB
009 Installing ktrain.mp4 -
29.9 MB
64 -
57.0 KB
017 Download Dataset.mp4 -
29.7 MB
65 -
278.8 KB
047 Run Your First Flask Application on AWS EC2.mp4 -
29.1 MB
66 -
889.3 KB
014 Saving and Loading Fine Tuned Model.mp4 -
25.5 MB
67 -
552.9 KB
048 Transfer DistilBERT Model to EC2 Flask Server.mp4 -
24.4 MB
68 -
571.1 KB
031 Notebook Setup.mp4 -
24.4 MB
69 -
639.2 KB
034 Save Model at Google Drive.mp4 -
22.8 MB
70 -
246.9 KB
039 Predict Sentiment at Your Local Machine.mp4 -
21.9 MB
71 -
125.8 KB
013 Testing Fine Tuned BERT Model.mp4 -
21.0 MB
72 -
980.1 KB
010 Loading Dataset.mp4 -
20.2 MB
73 -
792.1 KB
074 FastText Model Evaluation and Saving at Google Drive.mp4 -
19.9 MB
74 -
73.3 KB
056 Update and Upgrade Your Ubuntu Packages.mp4 -
19.9 MB
75 -
131.4 KB
069 FastText-App.zip -
18.5 MB
76 -
475.0 KB
045 Install Python on EC2 Windows 10.mp4 -
15.8 MB
77 -
223.7 KB
035 Model Evaluation.mp4 -
14.9 MB
78 -
95.4 KB
007 Notebook Setup.mp4 -
7.2 MB
79 -
868.3 KB
037 Flask App Preparation.mp4 -
6.2 MB
80 -
776.8 KB
036 Download Fine Tuned DistilBERT Model.mp4 -
4.9 MB
81 -
110.0 KB
015 Fine-Tuning-BERT-for-Disaster-Tweets-Classification.zip -
2.5 MB
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