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

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

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种子哈希：5d69f6866d2d31c8f62274f653fa27118853037d
文件大小： 2.85G
已经下载：75次
下载速度：极快
收录时间：2025-08-11
最近下载：2025-10-13

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文件列表

  • 06. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp4 193.6 MB
  • 10. Setting Up Your Environment (FAQ by Student Request)/2. Anaconda Environment Setup.mp4 176.0 MB
  • 03. Machine Learning and Neurons/6. Regression Notebook.mp4 156.2 MB
  • 03. Machine Learning and Neurons/4. Classification Notebook.mp4 116.5 MB
  • 10. Setting Up Your Environment (FAQ by Student Request)/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 114.5 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4 95.5 MB
  • 06. Natural Language Processing (NLP)/3. Text Preprocessing.mp4 91.9 MB
  • 12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 85.2 MB
  • 12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 79.4 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 67.4 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 66.7 MB
  • 02. Google Colab/2. Uploading your own data to Google Colab.mp4 65.8 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp4 65.4 MB
  • 04. Feedforward Artificial Neural Networks/10. ANN for Regression.mp4 63.7 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 59.5 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 58.9 MB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 1).mp4 58.9 MB
  • 02. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 58.3 MB
  • 08. In-Depth Gradient Descent/5. Adam (pt 1).mp4 57.8 MB
  • 08. In-Depth Gradient Descent/6. Adam (pt 2).mp4 55.3 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 51.0 MB
  • 03. Machine Learning and Neurons/7. The Neuron.mp4 47.6 MB
  • 02. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 45.5 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 44.8 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 44.5 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 43.9 MB
  • 04. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 41.1 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp4 40.9 MB
  • 04. Feedforward Artificial Neural Networks/9. ANN for Image Classification.mp4 40.2 MB
  • 03. Machine Learning and Neurons/8. How does a model learn.mp4 39.5 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 37.3 MB
  • 03. Machine Learning and Neurons/2. What is Machine Learning.mp4 36.1 MB
  • 06. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp4 34.7 MB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/6. How to use Github & Extra Coding Tips (Optional).mp4 31.1 MB
  • 03. Machine Learning and Neurons/3. Code Preparation (Classification Theory).mp4 29.7 MB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.mp4 29.3 MB
  • 04. Feedforward Artificial Neural Networks/6. How to Represent Images.mp4 28.2 MB
  • 01. Welcome/3. Where to get the code.mp4 28.2 MB
  • 03. Machine Learning and Neurons/10. Saving and Loading a Model.mp4 26.4 MB
  • 03. Machine Learning and Neurons/11. Suggestion Box.mp4 24.4 MB
  • 06. Natural Language Processing (NLP)/1. Embeddings.mp4 23.5 MB
  • 04. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).mp4 23.3 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 22.9 MB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code by Yourself (part 2).mp4 21.9 MB
  • 04. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 21.8 MB
  • 02. Google Colab/4. Temporary 403 Errors.mp4 21.8 MB
  • 04. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 21.4 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp4 21.3 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 20.7 MB
  • 13. Appendix FAQ Finale/2. BONUS.mp4 20.5 MB
  • 04. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 20.1 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp4 18.9 MB
  • 12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 18.7 MB
  • 08. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 17.9 MB
  • 07. In-Depth Loss Functions/1. Mean Squared Error.mp4 17.6 MB
  • 03. Machine Learning and Neurons/9. Making Predictions.mp4 17.5 MB
  • 01. Welcome/4. How to Succeed in this Course.mp4 17.0 MB
  • 08. In-Depth Gradient Descent/3. Momentum.mp4 17.0 MB
  • 08. In-Depth Gradient Descent/1. Gradient Descent.mp4 14.7 MB
  • 07. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 14.2 MB
  • 01. Welcome/2. Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 13.8 MB
  • 03. Machine Learning and Neurons/5. Code Preparation (Regression Theory).mp4 13.3 MB
  • 04. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 12.4 MB
  • 08. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 12.2 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp4 12.1 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast.mp4 12.1 MB
  • 07. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 10.3 MB
  • 10. Setting Up Your Environment (FAQ by Student Request)/1. Pre-Installation Check.mp4 9.4 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 8.8 MB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Python 2 vs Python 3.mp4 8.0 MB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 7.3 MB
  • 01. Welcome/1. Introduction and Outline.mp4 6.8 MB
  • 13. Appendix FAQ Finale/1. What is the Appendix.mp4 6.4 MB
  • 03. Machine Learning and Neurons/1. Review Section Introduction.mp4 5.3 MB
  • 04. Feedforward Artificial Neural Networks/7. Color Mixing Clarification.mp4 2.0 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 34.2 kB
  • 03. Machine Learning and Neurons/6. Regression Notebook.vtt 33.7 kB
  • 06. Natural Language Processing (NLP)/4. Text Classification with LSTMs.vtt 27.5 kB
  • 03. Machine Learning and Neurons/4. Classification Notebook.vtt 27.4 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.vtt 26.6 kB
  • 12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).vtt 25.0 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.vtt 25.0 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.vtt 24.7 kB
  • 04. Feedforward Artificial Neural Networks/4. Activation Functions.vtt 24.1 kB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 1).vtt 23.7 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).vtt 23.5 kB
  • 03. Machine Learning and Neurons/3. Code Preparation (Classification Theory).vtt 21.7 kB
  • 10. Setting Up Your Environment (FAQ by Student Request)/2. Anaconda Environment Setup.vtt 20.6 kB
  • 03. Machine Learning and Neurons/2. What is Machine Learning.vtt 20.3 kB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.vtt 19.8 kB
  • 08. In-Depth Gradient Descent/5. Adam (pt 1).vtt 17.8 kB
  • 12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).vtt 17.2 kB
  • 06. Natural Language Processing (NLP)/1. Embeddings.vtt 17.1 kB
  • 04. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).vtt 17.0 kB
  • 04. Feedforward Artificial Neural Networks/6. How to Represent Images.vtt 16.9 kB
  • 06. Natural Language Processing (NLP)/2. Code Preparation (NLP).vtt 16.8 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).vtt 16.6 kB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/6. How to use Github & Extra Coding Tips (Optional).vtt 16.6 kB
  • 08. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.vtt 15.9 kB
  • 12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).vtt 15.8 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).vtt 15.6 kB
  • 03. Machine Learning and Neurons/8. How does a model learn.vtt 15.3 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).vtt 15.1 kB
  • 02. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.vtt 15.0 kB
  • 02. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.vtt 15.0 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.vtt 15.0 kB
  • 10. Setting Up Your Environment (FAQ by Student Request)/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 14.6 kB
  • 08. In-Depth Gradient Descent/6. Adam (pt 2).vtt 14.6 kB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code by Yourself (part 2).vtt 14.6 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 14.5 kB
  • 04. Feedforward Artificial Neural Networks/10. ANN for Regression.vtt 14.0 kB
  • 03. Machine Learning and Neurons/7. The Neuron.vtt 13.5 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.vtt 13.3 kB
  • 04. Feedforward Artificial Neural Networks/2. Forward Propagation.vtt 13.0 kB
  • 01. Welcome/2. Get Your Hands Dirty, Practical Coding Experience, Data Links.vtt 12.8 kB
  • 04. Feedforward Artificial Neural Networks/3. The Geometrical Picture.vtt 12.4 kB
  • 07. In-Depth Loss Functions/1. Mean Squared Error.vtt 11.9 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.vtt 11.6 kB
  • 04. Feedforward Artificial Neural Networks/9. ANN for Image Classification.vtt 10.4 kB
  • 08. In-Depth Gradient Descent/1. Gradient Descent.vtt 10.4 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.vtt 10.4 kB
  • 07. In-Depth Loss Functions/3. Categorical Cross Entropy.vtt 10.3 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.vtt 10.1 kB
  • 03. Machine Learning and Neurons/5. Code Preparation (Regression Theory).vtt 9.6 kB
  • 04. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.vtt 8.6 kB
  • 03. Machine Learning and Neurons/9. Making Predictions.vtt 8.6 kB
  • 08. In-Depth Gradient Descent/3. Momentum.vtt 8.3 kB
  • 07. In-Depth Loss Functions/2. Binary Cross Entropy.vtt 7.9 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.vtt 7.7 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast.vtt 7.7 kB
  • 13. Appendix FAQ Finale/2. BONUS.vtt 7.3 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).vtt 6.6 kB
  • 10. Setting Up Your Environment (FAQ by Student Request)/1. Pre-Installation Check.vtt 6.6 kB
  • 01. Welcome/3. Where to get the code.vtt 6.6 kB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Python 2 vs Python 3.vtt 6.5 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).vtt 6.3 kB
  • 08. In-Depth Gradient Descent/2. Stochastic Gradient Descent.vtt 5.8 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.vtt 5.0 kB
  • 03. Machine Learning and Neurons/10. Saving and Loading a Model.vtt 5.0 kB
  • 03. Machine Learning and Neurons/11. Suggestion Box.vtt 4.9 kB
  • 01. Welcome/1. Introduction and Outline.vtt 4.8 kB
  • 01. Welcome/4. How to Succeed in this Course.vtt 4.7 kB
  • 05. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).vtt 4.6 kB
  • 13. Appendix FAQ Finale/1. What is the Appendix.vtt 4.0 kB
  • 03. Machine Learning and Neurons/1. Review Section Introduction.vtt 3.9 kB
  • 02. Google Colab/4. Temporary 403 Errors.vtt 3.8 kB
  • 04. Feedforward Artificial Neural Networks/7. Color Mixing Clarification.vtt 1.2 kB
  • 09. Extras/1. Data Links.html 256 Bytes

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