[Udemy] Deep Learning Recurrent Neural Networks in Python (06.2021)

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[Udemy] Deep Learning Recurrent Neural Networks in Python (06.2021)

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种子哈希：2dd76f6f5972e094ca6f0c00d5e4ebea702a46b8
文件大小： 3.66G
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收录时间：2022-02-05
最近下载：2025-05-17

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文件列表

  • 10. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.mp4 323.9 MB
  • 10. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 201.3 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4 150.0 MB
  • 12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 142.9 MB
  • 12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 141.5 MB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.mp4 113.7 MB
  • 2. Google Colab/2. Uploading your own data to Google Colab.mp4 108.3 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp4 104.2 MB
  • 4. Feedforward Artificial Neural Networks/9. ANN for Regression.mp4 104.0 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 102.7 MB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).mp4 98.1 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 92.5 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 90.4 MB
  • 3. Machine Learning and Neurons/5. Classification Notebook.mp4 81.4 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 79.9 MB
  • 6. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp4 79.2 MB
  • 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 75.0 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 69.8 MB
  • 3. Machine Learning and Neurons/11. How does a model learn.mp4 63.5 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 63.1 MB
  • 4. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 62.4 MB
  • 4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.mp4 61.7 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp4 61.1 MB
  • 8. In-Depth Gradient Descent/5. Adam (pt 1).mp4 57.8 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 57.8 MB
  • 8. In-Depth Gradient Descent/6. Adam (pt 2).mp4 55.3 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 54.5 MB
  • 3. Machine Learning and Neurons/8. Regression Notebook.mp4 51.3 MB
  • 3. Machine Learning and Neurons/2. What is Machine Learning.mp4 50.1 MB
  • 1. Welcome/3. How to Succeed in this Course.mp4 49.1 MB
  • 3. Machine Learning and Neurons/10. The Neuron.mp4 47.6 MB
  • 4. Feedforward Artificial Neural Networks/6. How to Represent Images.mp4 45.1 MB
  • 3. Machine Learning and Neurons/13. Saving and Loading a Model.mp4 43.7 MB
  • 6. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp4 42.9 MB
  • 13. Appendix FAQ Finale/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.6 MB
  • 3. Machine Learning and Neurons/3. Code Preparation (Classification Theory).mp4 39.6 MB
  • 6. Natural Language Processing (NLP)/1. Embeddings.mp4 34.5 MB
  • 1. Welcome/2. Where to get the Code.mp4 34.0 MB
  • 4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).mp4 33.9 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp4 33.5 MB
  • 1. Welcome/1. Introduction and Outline.mp4 33.0 MB
  • 4. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 32.3 MB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).mp4 30.7 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 30.7 MB
  • 4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 30.0 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp4 28.8 MB
  • 6. Natural Language Processing (NLP)/3. Text Preprocessing.mp4 27.7 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 27.5 MB
  • 8. In-Depth Gradient Descent/3. Momentum.mp4 26.8 MB
  • 4. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 26.8 MB
  • 12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 26.1 MB
  • 7. In-Depth Loss Functions/1. Mean Squared Error.mp4 25.2 MB
  • 3. Machine Learning and Neurons/12. Making Predictions.mp4 24.7 MB
  • 8. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 24.6 MB
  • 2. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 24.5 MB
  • 8. In-Depth Gradient Descent/1. Gradient Descent.mp4 21.8 MB
  • 7. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 20.5 MB
  • 3. Machine Learning and Neurons/14. Suggestion Box.mp4 20.2 MB
  • 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 19.3 MB
  • 8. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 19.1 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp4 19.0 MB
  • 3. Machine Learning and Neurons/7. Code Preparation (Regression Theory).mp4 17.7 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast.mp4 16.6 MB
  • 3. Machine Learning and Neurons/4. Beginner's Code Preamble.mp4 14.3 MB
  • 7. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 13.4 MB
  • 3. Machine Learning and Neurons/6. Exercise Predicting Diabetes Onset.mp4 13.2 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 12.2 MB
  • 4. Feedforward Artificial Neural Networks/10. Exercise E. Coli Protein Localization Sites.mp4 11.0 MB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.mp4 10.9 MB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 10.4 MB
  • 6. Natural Language Processing (NLP)/5. Exercise Sentiment Analysis.mp4 9.6 MB
  • 13. Appendix FAQ Finale/1. What is the Appendix.mp4 9.3 MB
  • 3. Machine Learning and Neurons/1. Review Section Introduction.mp4 7.9 MB
  • 3. Machine Learning and Neurons/9. Exercise Real Estate Predictions.mp4 5.8 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-en_US.srt 31.4 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks-en_US.srt 25.3 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data-en_US.srt 23.7 kB
  • 12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2)-en_US.srt 23.2 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem-en_US.srt 22.8 kB
  • 4. Feedforward Artificial Neural Networks/4. Activation Functions-en_US.srt 22.6 kB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1)-en_US.srt 22.2 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1)-en_US.srt 20.5 kB
  • 3. Machine Learning and Neurons/3. Code Preparation (Classification Theory)-en_US.srt 20.2 kB
  • 10. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018-en_US.srt 19.5 kB
  • 3. Machine Learning and Neurons/2. What is Machine Learning-en_US.srt 18.7 kB
  • 6. Natural Language Processing (NLP)/2. Code Preparation (NLP)-en_US.srt 16.6 kB
  • 8. In-Depth Gradient Descent/5. Adam (pt 1)-en_US.srt 16.4 kB
  • 12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1)-en_US.srt 16.4 kB
  • 6. Natural Language Processing (NLP)/1. Embeddings-en_US.srt 16.3 kB
  • 4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN)-en_US.srt 16.2 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1)-en_US.srt 15.7 kB
  • 4. Feedforward Artificial Neural Networks/6. How to Represent Images-en_US.srt 15.6 kB
  • 8. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates-en_US.srt 15.0 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2)-en_US.srt 14.5 kB
  • 12. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version)-en_US.srt 14.5 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3)-en_US.srt 14.4 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction-en_US.srt 14.3 kB
  • 8. In-Depth Gradient Descent/6. Adam (pt 2)-en_US.srt 14.3 kB
  • 3. Machine Learning and Neurons/11. How does a model learn-en_US.srt 14.1 kB
  • 10. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow-en_US.srt 14.0 kB
  • 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free-en_US.srt 14.0 kB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it-en_US.srt 13.8 kB
  • 4. Feedforward Artificial Neural Networks/9. ANN for Regression-en_US.srt 13.1 kB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2)-en_US.srt 13.1 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting-en_US.srt 12.5 kB
  • 3. Machine Learning and Neurons/10. The Neuron-en_US.srt 12.4 kB
  • 4. Feedforward Artificial Neural Networks/2. Forward Propagation-en_US.srt 12.2 kB
  • 1. Welcome/2. Where to get the Code-en_US.srt 12.2 kB
  • 3. Machine Learning and Neurons/8. Regression Notebook-en_US.srt 12.2 kB
  • 2. Google Colab/2. Uploading your own data to Google Colab-en_US.srt 11.8 kB
  • 4. Feedforward Artificial Neural Networks/3. The Geometrical Picture-en_US.srt 11.6 kB
  • 7. In-Depth Loss Functions/1. Mean Squared Error-en_US.srt 11.2 kB
  • 2. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn-en_US.srt 11.1 kB
  • 4. Feedforward Artificial Neural Networks/5. Multiclass Classification-en_US.srt 10.9 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction-en_US.srt 10.9 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes-en_US.srt 9.8 kB
  • 4. Feedforward Artificial Neural Networks/8. ANN for Image Classification-en_US.srt 9.8 kB
  • 6. Natural Language Processing (NLP)/4. Text Classification with LSTMs-en_US.srt 9.8 kB
  • 8. In-Depth Gradient Descent/1. Gradient Descent-en_US.srt 9.7 kB
  • 7. In-Depth Loss Functions/3. Categorical Cross Entropy-en_US.srt 9.7 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence-en_US.srt 9.5 kB
  • 3. Machine Learning and Neurons/5. Classification Notebook-en_US.srt 9.1 kB
  • 3. Machine Learning and Neurons/7. Code Preparation (Regression Theory)-en_US.srt 8.7 kB
  • 1. Welcome/3. How to Succeed in this Course-en_US.srt 8.1 kB
  • 3. Machine Learning and Neurons/12. Making Predictions-en_US.srt 8.0 kB
  • 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction-en_US.srt 7.9 kB
  • 13. Appendix FAQ Finale/2. BONUS Where to get Udemy coupons and FREE deep learning material-en_US.srt 7.7 kB
  • 8. In-Depth Gradient Descent/3. Momentum-en_US.srt 7.7 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast-en_US.srt 7.1 kB
  • 7. In-Depth Loss Functions/2. Binary Cross Entropy-en_US.srt 7.1 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation-en_US.srt 7.0 kB
  • 3. Machine Learning and Neurons/4. Beginner's Code Preamble-en_US.srt 6.8 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2)-en_US.srt 6.4 kB
  • 6. Natural Language Processing (NLP)/3. Text Preprocessing-en_US.srt 6.2 kB
  • 11. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3-en_US.srt 6.0 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory)-en_US.srt 5.8 kB
  • 8. In-Depth Gradient Descent/2. Stochastic Gradient Descent-en_US.srt 5.4 kB
  • 3. Machine Learning and Neurons/13. Saving and Loading a Model-en_US.srt 4.9 kB
  • 3. Machine Learning and Neurons/14. Suggestion Box-en_US.srt 4.6 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code)-en_US.srt 4.6 kB
  • 5. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works-en_US.srt 4.5 kB
  • 1. Welcome/1. Introduction and Outline-en_US.srt 4.5 kB
  • 9. Extras/Colab Notebooks.html 4.1 kB
  • 13. Appendix FAQ Finale/1. What is the Appendix-en_US.srt 3.7 kB
  • 3. Machine Learning and Neurons/1. Review Section Introduction-en_US.srt 3.6 kB
  • 3. Machine Learning and Neurons/6. Exercise Predicting Diabetes Onset-en_US.srt 3.2 kB
  • 4. Feedforward Artificial Neural Networks/10. Exercise E. Coli Protein Localization Sites-en_US.srt 2.8 kB
  • 6. Natural Language Processing (NLP)/5. Exercise Sentiment Analysis-en_US.srt 2.6 kB
  • 3. Machine Learning and Neurons/9. Exercise Real Estate Predictions-en_US.srt 1.6 kB
  • 1. Welcome/2. External URLs.txt 75 Bytes

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