Udemy - Deep Learning Recurrent Neural Networks in Python (1.2025)

Creation Time	Aug. 18, 2025, 11:41 a.m.
Last Access Time	Aug. 19, 2025, 8:01 p.m.
File Size	2.9 GB
Keywords	Neural Python Recurrent - Deep Udemy 1 2025 Learning in Networks
Total Requests	17
Total Files	148
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06. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp4 184.7 MB
10. Setting Up Your Environment (FAQ by Student Request)/2. Anaconda Environment Setup.mp4 167.9 MB
03. Machine Learning and Neurons/6. Regression Notebook.mp4 149.0 MB
03. Machine Learning and Neurons/4. Classification Notebook.mp4 111.1 MB
10. Setting Up Your Environment (FAQ by Student Request)/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 109.2 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4 91.0 MB
06. Natural Language Processing (NLP)/3. Text Preprocessing.mp4 87.7 MB
12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 81.2 MB
12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 75.7 MB
11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Proof that using Jupyter Notebook is the same as not using it.mp4 64.3 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 63.6 MB
02. Google Colab/2. Uploading your own data to Google Colab.mp4 62.8 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp4 62.4 MB
04. Feedforward Artificial Neural Networks/10. ANN for Regression.mp4 60.7 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 56.7 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 56.2 MB
11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 1).mp4 56.1 MB
02. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 55.6 MB
08. In-Depth Gradient Descent/5. Adam (pt 1).mp4 55.1 MB
08. In-Depth Gradient Descent/6. Adam (pt 2).mp4 52.8 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 48.6 MB
03. Machine Learning and Neurons/7. The Neuron.mp4 45.4 MB
02. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 43.4 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 42.7 MB
12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 42.4 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 41.8 MB
04. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 39.2 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp4 39.0 MB
04. Feedforward Artificial Neural Networks/9. ANN for Image Classification.mp4 38.4 MB
03. Machine Learning and Neurons/8. How does a model learn.mp4 37.7 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 35.5 MB
03. Machine Learning and Neurons/2. What is Machine Learning.mp4 34.5 MB
06. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp4 33.1 MB
11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/6. How to use Github & Extra Coding Tips (Optional).mp4 29.7 MB
03. Machine Learning and Neurons/3. Code Preparation (Classification Theory).mp4 28.3 MB
11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.mp4 28.0 MB
04. Feedforward Artificial Neural Networks/6. How to Represent Images.mp4 26.9 MB
01. Welcome/3. Where to get the code.mp4 26.9 MB
03. Machine Learning and Neurons/10. Saving and Loading a Model.mp4 25.1 MB
03. Machine Learning and Neurons/11. Suggestion Box.mp4 23.3 MB
06. Natural Language Processing (NLP)/1. Embeddings.mp4 22.4 MB
04. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).mp4 22.2 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 21.8 MB
11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code by Yourself (part 2).mp4 20.9 MB
04. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 20.8 MB
02. Google Colab/4. Temporary 403 Errors.mp4 20.8 MB
04. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 20.4 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp4 20.3 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 19.8 MB
13. Appendix FAQ Finale/2. BONUS.mp4 19.6 MB
04. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 19.2 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp4 18.0 MB
12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 17.9 MB
08. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 17.0 MB
07. In-Depth Loss Functions/1. Mean Squared Error.mp4 16.8 MB
03. Machine Learning and Neurons/9. Making Predictions.mp4 16.7 MB
01. Welcome/4. How to Succeed in this Course.mp4 16.2 MB
08. In-Depth Gradient Descent/3. Momentum.mp4 16.2 MB
08. In-Depth Gradient Descent/1. Gradient Descent.mp4 14.0 MB
07. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 13.6 MB
01. Welcome/2. Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 13.2 MB
03. Machine Learning and Neurons/5. Code Preparation (Regression Theory).mp4 12.7 MB
04. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 11.8 MB
08. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 11.6 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp4 11.6 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast.mp4 11.5 MB
07. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 9.8 MB
10. Setting Up Your Environment (FAQ by Student Request)/1. Pre-Installation Check.mp4 8.9 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 8.4 MB
11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Python 2 vs Python 3.mp4 7.6 MB
05. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 6.9 MB
01. Welcome/1. Introduction and Outline.mp4 6.5 MB
13. Appendix FAQ Finale/1. What is the Appendix.mp4 6.1 MB
03. Machine Learning and Neurons/1. Review Section Introduction.mp4 5.0 MB
04. Feedforward Artificial Neural Networks/7. Color Mixing Clarification.mp4 1.9 MB
12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 33.4 KB
03. Machine Learning and Neurons/6. Regression Notebook.vtt 32.9 KB
06. Natural Language Processing (NLP)/4. Text Classification with LSTMs.vtt 26.9 KB
03. Machine Learning and Neurons/4. Classification Notebook.vtt 26.8 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.vtt 26.0 KB
12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).vtt 24.4 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.vtt 24.4 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.vtt 24.1 KB
04. Feedforward Artificial Neural Networks/4. Activation Functions.vtt 23.5 KB
11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 1).vtt 23.1 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).vtt 22.9 KB
03. Machine Learning and Neurons/3. Code Preparation (Classification Theory).vtt 21.2 KB
10. Setting Up Your Environment (FAQ by Student Request)/2. Anaconda Environment Setup.vtt 20.1 KB
03. Machine Learning and Neurons/2. What is Machine Learning.vtt 19.8 KB
11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.vtt 19.3 KB
08. In-Depth Gradient Descent/5. Adam (pt 1).vtt 17.3 KB
12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).vtt 16.8 KB
06. Natural Language Processing (NLP)/1. Embeddings.vtt 16.7 KB
04. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).vtt 16.6 KB
04. Feedforward Artificial Neural Networks/6. How to Represent Images.vtt 16.5 KB
06. Natural Language Processing (NLP)/2. Code Preparation (NLP).vtt 16.4 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).vtt 16.2 KB
11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/6. How to use Github & Extra Coding Tips (Optional).vtt 16.2 KB
08. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.vtt 15.6 KB
12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).vtt 15.4 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).vtt 15.3 KB
03. Machine Learning and Neurons/8. How does a model learn.vtt 15.0 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).vtt 14.8 KB
02. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.vtt 14.7 KB
02. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.vtt 14.7 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.vtt 14.7 KB
10. Setting Up Your Environment (FAQ by Student Request)/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 14.3 KB
08. In-Depth Gradient Descent/6. Adam (pt 2).vtt 14.3 KB
11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code by Yourself (part 2).vtt 14.2 KB
11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Proof that using Jupyter Notebook is the same as not using it.vtt 14.1 KB
04. Feedforward Artificial Neural Networks/10. ANN for Regression.vtt 13.7 KB
03. Machine Learning and Neurons/7. The Neuron.vtt 13.2 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.vtt 13.0 KB
04. Feedforward Artificial Neural Networks/2. Forward Propagation.vtt 12.7 KB
01. Welcome/2. Get Your Hands Dirty, Practical Coding Experience, Data Links.vtt 12.5 KB
04. Feedforward Artificial Neural Networks/3. The Geometrical Picture.vtt 12.1 KB
07. In-Depth Loss Functions/1. Mean Squared Error.vtt 11.6 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.vtt 11.3 KB
04. Feedforward Artificial Neural Networks/9. ANN for Image Classification.vtt 10.2 KB
08. In-Depth Gradient Descent/1. Gradient Descent.vtt 10.2 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.vtt 10.1 KB
07. In-Depth Loss Functions/3. Categorical Cross Entropy.vtt 10.1 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.vtt 9.8 KB
03. Machine Learning and Neurons/5. Code Preparation (Regression Theory).vtt 9.4 KB
04. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.vtt 8.4 KB
03. Machine Learning and Neurons/9. Making Predictions.vtt 8.4 KB
08. In-Depth Gradient Descent/3. Momentum.vtt 8.1 KB
07. In-Depth Loss Functions/2. Binary Cross Entropy.vtt 7.7 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.vtt 7.6 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast.vtt 7.5 KB
13. Appendix FAQ Finale/2. BONUS.vtt 7.1 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).vtt 6.5 KB
10. Setting Up Your Environment (FAQ by Student Request)/1. Pre-Installation Check.vtt 6.5 KB
01. Welcome/3. Where to get the code.vtt 6.4 KB
11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Python 2 vs Python 3.vtt 6.3 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).vtt 6.1 KB
08. In-Depth Gradient Descent/2. Stochastic Gradient Descent.vtt 5.7 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.vtt 4.9 KB
03. Machine Learning and Neurons/10. Saving and Loading a Model.vtt 4.9 KB
03. Machine Learning and Neurons/11. Suggestion Box.vtt 4.8 KB
01. Welcome/1. Introduction and Outline.vtt 4.7 KB
01. Welcome/4. How to Succeed in this Course.vtt 4.6 KB
05. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).vtt 4.4 KB
13. Appendix FAQ Finale/1. What is the Appendix.vtt 3.9 KB
03. Machine Learning and Neurons/1. Review Section Introduction.vtt 3.8 KB
02. Google Colab/4. Temporary 403 Errors.vtt 3.7 KB
04. Feedforward Artificial Neural Networks/7. Color Mixing Clarification.vtt 1.2 KB
09. Extras/1. Data Links.html 256 bytes

Udemy - Deep Learning Recurrent Neural Networks in Python (1.2025)

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