GetFreeCourses.Co-Udemy-Complete Tensorflow 2 and Keras Deep Learning Bootcamp

磁力链接/BT种子名称

GetFreeCourses.Co-Udemy-Complete Tensorflow 2 and Keras Deep Learning Bootcamp

磁力链接/BT种子简介

种子哈希：3fa8e36008372b0cba53dc4c0ae7d52e92d46be5
文件大小： 6.74G
已经下载：2296次
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收录时间：2021-05-15
最近下载：2026-01-06

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文件列表

  • 1. Course Overview, Installs, and Setup/3. Course Setup and Installation.mp4 159.8 MB
  • 13. Deployment/7. Flask Front End.mp4 156.9 MB
  • 9. Recurrent Neural Networks - RNNs/14. Bonus - Multivariate Time Series - RNN and LSTMs.mp4 156.6 MB
  • 9. Recurrent Neural Networks - RNNs/13. RNN Exercise - Solutions.mp4 155.3 MB
  • 7. Basic Artificial Neural Networks - ANNs/27. Tensorboard.mp4 151.2 MB
  • 7. Basic Artificial Neural Networks - ANNs/20. Keras Project Solutions - Exploratory Data Analysis.mp4 150.6 MB
  • 7. Basic Artificial Neural Networks - ANNs/12. Keras Regression Code Along - Exploratory Data Analysis.mp4 143.7 MB
  • 12. Generative Adversarial Networks/4. Creating a GAN - Part Three - Model Training.mp4 138.0 MB
  • 9. Recurrent Neural Networks - RNNs/11. RNN on a Time Series - Part Two.mp4 137.4 MB
  • 13. Deployment/8. Live Deployment to the Web.mp4 132.7 MB
  • 7. Basic Artificial Neural Networks - ANNs/23. Keras Project Solutions - Categorical Data.mp4 131.1 MB
  • 11. AutoEncoders/3. Autoencoder for Dimensionality Reduction.mp4 123.2 MB
  • 7. Basic Artificial Neural Networks - ANNs/17. Keras Classification - Dealing with Overfitting and Evaluation.mp4 116.7 MB
  • 1. Course Overview, Installs, and Setup/3.1 FINAL_TF2_FILES.zip 104.1 MB
  • 1. Course Overview, Installs, and Setup/4.1 FINAL_TF2_FILES.zip 104.1 MB
  • 8. Convolutional Neural Networks - CNNs/7. CNN on MNIST - Part Two - Creating and Training the Model.mp4 103.7 MB
  • 7. Basic Artificial Neural Networks - ANNs/21. Keras Project Solutions - Dealing with Missing Data.mp4 101.5 MB
  • 11. AutoEncoders/4. Autoencoder for Images - Part One.mp4 98.7 MB
  • 4. Pandas Crash Course/8. Data Input and Output.mp4 98.0 MB
  • 5. Visualization Crash Course/3. Seaborn Basics.mp4 96.3 MB
  • 8. Convolutional Neural Networks - CNNs/14. CNN on Real Image Files - Part Three - Creating the Model.mp4 95.0 MB
  • 3. NumPy Crash Course/2. NumPy Arrays.mp4 92.9 MB
  • 8. Convolutional Neural Networks - CNNs/13. CNN on Real Image Files - Part Two - Data Processing.mp4 92.2 MB
  • 13. Deployment/2. Creating the Model.mp4 91.3 MB
  • 7. Basic Artificial Neural Networks - ANNs/22. Keras Project Solutions - Dealing with Missing Data - Part Two.mp4 89.5 MB
  • 7. Basic Artificial Neural Networks - ANNs/10. Keras Syntax Basics - Part Two - Creating and Training the Model.mp4 88.7 MB
  • 9. Recurrent Neural Networks - RNNs/8. RNN on a Sine Wave - Creating the Model.mp4 87.9 MB
  • 9. Recurrent Neural Networks - RNNs/9. RNN on a Sine Wave - LSTMs and Forecasting.mp4 87.5 MB
  • 6. Machine Learning Concepts Overview/4. Evaluating Performance - Classification Error Metrics.mp4 86.7 MB
  • 10. Natural Language Processing/4. NLP - Part Three - Creating Batches.mp4 85.7 MB
  • 8. Convolutional Neural Networks - CNNs/12. CNN on Real Image Files - Part One - Reading in the Data.mp4 84.6 MB
  • 7. Basic Artificial Neural Networks - ANNs/19. TensorFlow 2.0 Keras Project Notebook Overview.mp4 84.5 MB
  • 11. AutoEncoders/7. Autoencoder Exercise - Solutions.mp4 81.5 MB
  • 7. Basic Artificial Neural Networks - ANNs/13. Keras Regression Code Along - Exploratory Data Analysis - Continued.mp4 79.9 MB
  • 7. Basic Artificial Neural Networks - ANNs/6. Cost Functions and Gradient Descent.mp4 79.3 MB
  • 8. Convolutional Neural Networks - CNNs/2. Image Filters and Kernels.mp4 75.9 MB
  • 12. Generative Adversarial Networks/3. Creating a GAN - Part Two - The Model.mp4 73.2 MB
  • 13. Deployment/5. Flask Postman API.mp4 72.5 MB
  • 7. Basic Artificial Neural Networks - ANNs/15. Keras Regression Code Along - Model Evaluation and Predictions.mp4 72.3 MB
  • 10. Natural Language Processing/6. NLP - Part Five - Training the Model.mp4 68.4 MB
  • 7. Basic Artificial Neural Networks - ANNs/11. Keras Syntax Basics - Part Three - Model Evaluation.mp4 68.1 MB
  • 10. Natural Language Processing/5. NLP - Part Four - Creating the Model.mp4 67.4 MB
  • 8. Convolutional Neural Networks - CNNs/9. CNN on CIFAR-10 - Part One - The Data.mp4 67.4 MB
  • 7. Basic Artificial Neural Networks - ANNs/26. Keras Project Solutions - Model Evaluation.mp4 66.3 MB
  • 7. Basic Artificial Neural Networks - ANNs/4. Activation Functions.mp4 65.6 MB
  • 13. Deployment/4. Running a Basic Flask Application.mp4 65.0 MB
  • 4. Pandas Crash Course/7. Pandas Operations.mp4 64.2 MB
  • 11. AutoEncoders/5. Autoencoder for Images - Part Two - Noise Removal.mp4 63.4 MB
  • 8. Convolutional Neural Networks - CNNs/6. CNN on MNIST - Part One - The Data.mp4 62.7 MB
  • 8. Convolutional Neural Networks - CNNs/3. Convolutional Layers.mp4 60.8 MB
  • 7. Basic Artificial Neural Networks - ANNs/7. Backpropagation.mp4 60.5 MB
  • 12. Generative Adversarial Networks/5. DCGAN - Deep Convolutional Generative Adversarial Networks.mp4 60.0 MB
  • 4. Pandas Crash Course/6. GroupBy Operations.mp4 59.1 MB
  • 7. Basic Artificial Neural Networks - ANNs/16. Keras Classification Code Along - EDA and Preprocessing.mp4 58.9 MB
  • 8. Convolutional Neural Networks - CNNs/17. CNN Exercise Solutions.mp4 58.8 MB
  • 12. Generative Adversarial Networks/1. GANs Overview.mp4 56.5 MB
  • 13. Deployment/3. Model Prediction Function.mp4 55.6 MB
  • 10. Natural Language Processing/7. NLP - Part Six - Generating Text.mp4 54.8 MB
  • 4. Pandas Crash Course/10. Pandas Exercises - Solutions.mp4 54.0 MB
  • 7. Basic Artificial Neural Networks - ANNs/9. Keras Syntax Basics - Part One - Preparing the Data.mp4 53.0 MB
  • 5. Visualization Crash Course/5. Data Visualization Exercises - Solutions.mp4 52.9 MB
  • 9. Recurrent Neural Networks - RNNs/7. RNN on a Sine Wave - Batch Generator.mp4 52.5 MB
  • 3. NumPy Crash Course/4. NumPy Operations.mp4 51.0 MB
  • 3. NumPy Crash Course/6. Numpy Exercises - Solutions.mp4 50.9 MB
  • 7. Basic Artificial Neural Networks - ANNs/2. Perceptron Model.mp4 50.1 MB
  • 8. Convolutional Neural Networks - CNNs/15. CNN on Real Image Files - Part Four - Evaluating the Model.mp4 49.4 MB
  • 7. Basic Artificial Neural Networks - ANNs/14. Keras Regression Code Along - Data Preprocessing and Creating a Model.mp4 49.3 MB
  • 3. NumPy Crash Course/3. Numpy Index Selection.mp4 48.6 MB
  • 7. Basic Artificial Neural Networks - ANNs/5. Multi-Class Classification Considerations.mp4 48.1 MB
  • 8. Convolutional Neural Networks - CNNs/10. CNN on CIFAR-10 - Part Two - Evaluating the Model.mp4 47.5 MB
  • 4. Pandas Crash Course/3. Pandas DataFrames - Part One.mp4 47.4 MB
  • 9. Recurrent Neural Networks - RNNs/10. RNN on a Time Series - Part One.mp4 47.2 MB
  • 4. Pandas Crash Course/5. Pandas Missing Data.mp4 46.2 MB
  • 11. AutoEncoders/2. Autoencoder Basics.mp4 44.7 MB
  • 9. Recurrent Neural Networks - RNNs/4. LSTMS and GRU.mp4 44.0 MB
  • 5. Visualization Crash Course/2. Matplotlib Basics.mp4 43.0 MB
  • 9. Recurrent Neural Networks - RNNs/6. RNN on a Sine Wave - The Data.mp4 42.1 MB
  • 6. Machine Learning Concepts Overview/2. Supervised Learning Overview.mp4 42.0 MB
  • 8. Convolutional Neural Networks - CNNs/8. CNN on MNIST - Part Three - Model Evaluation.mp4 40.4 MB
  • 4. Pandas Crash Course/2. Pandas Series.mp4 39.7 MB
  • 4. Pandas Crash Course/4. Pandas DataFrames - Part Two.mp4 38.8 MB
  • 7. Basic Artificial Neural Networks - ANNs/3. Neural Networks.mp4 37.5 MB
  • 10. Natural Language Processing/1. Introduction to NLP Section.mp4 36.8 MB
  • 11. AutoEncoders/6. Autoencoder Exercise Overview.mp4 35.6 MB
  • 9. Recurrent Neural Networks - RNNs/5. RNN Batches.mp4 34.3 MB
  • 9. Recurrent Neural Networks - RNNs/2. RNN Basic Theory.mp4 31.4 MB
  • 9. Recurrent Neural Networks - RNNs/12. RNN Exercise.mp4 31.4 MB
  • 7. Basic Artificial Neural Networks - ANNs/25. Keras Project Solutions - Creating and Training a Model.mp4 31.2 MB
  • 8. Convolutional Neural Networks - CNNs/11. Downloading Data Set for Real Image Lectures.mp4 29.6 MB
  • 6. Machine Learning Concepts Overview/1. What is Machine Learning.mp4 29.6 MB
  • 9. Recurrent Neural Networks - RNNs/3. Vanishing Gradients.mp4 29.5 MB
  • 8. Convolutional Neural Networks - CNNs/4. Pooling Layers.mp4 29.0 MB
  • 6. Machine Learning Concepts Overview/3. Overfitting.mp4 27.6 MB
  • 1. Course Overview, Installs, and Setup/2. Course Overview.mp4 27.4 MB
  • 4. Pandas Crash Course/1. Introduction to Pandas.mp4 26.7 MB
  • 7. Basic Artificial Neural Networks - ANNs/24. Keras Project Solutions - Data PreProcessing.mp4 25.1 MB
  • 6. Machine Learning Concepts Overview/5. Evaluating Performance - Regression Error Metrics.mp4 24.8 MB
  • 4. Pandas Crash Course/9. Pandas Exercises.mp4 24.6 MB
  • 13. Deployment/1. Introduction to Deployment.mp4 24.6 MB
  • 10. Natural Language Processing/3. NLP - Part Two - Text Processing.mp4 24.0 MB
  • 5. Visualization Crash Course/4. Data Visualization Exercises.mp4 23.9 MB
  • 10. Natural Language Processing/2. NLP - Part One - The Data.mp4 23.4 MB
  • 8. Convolutional Neural Networks - CNNs/5. MNIST Data Set Overview.mp4 22.1 MB
  • 11. AutoEncoders/1. Introduction to Autoencoders.mp4 21.9 MB
  • 13. Deployment/6. Flask API - Using Requests Programmatically.mp4 20.9 MB
  • 12. Generative Adversarial Networks/2. Creating a GAN - Part One- The Data.mp4 20.1 MB
  • 6. Machine Learning Concepts Overview/6. Unsupervised Learning.mp4 19.7 MB
  • 8. Convolutional Neural Networks - CNNs/16. CNN Exercise Overview.mp4 18.7 MB
  • 3. NumPy Crash Course/5. NumPy Exercises.mp4 12.1 MB
  • 3. NumPy Crash Course/1. Introduction to NumPy.mp4 11.9 MB
  • 9. Recurrent Neural Networks - RNNs/1. RNN Section Overview.mp4 11.4 MB
  • 7. Basic Artificial Neural Networks - ANNs/8. TensorFlow vs. Keras Explained.mp4 11.0 MB
  • 7. Basic Artificial Neural Networks - ANNs/1. Introduction to ANN Section.mp4 10.2 MB
  • 7. Basic Artificial Neural Networks - ANNs/18. TensorFlow 2.0 Keras Project Options Overview.mp4 8.2 MB
  • 8. Convolutional Neural Networks - CNNs/1. CNN Section Overview.mp4 7.9 MB
  • 5. Visualization Crash Course/1. Introduction to Python Visualization.mp4 7.1 MB
  • 1. Course Overview, Installs, and Setup/3. Course Setup and Installation.srt 35.4 kB
  • 12. Generative Adversarial Networks/4. Creating a GAN - Part Three - Model Training.srt 35.0 kB
  • 9. Recurrent Neural Networks - RNNs/13. RNN Exercise - Solutions.srt 33.0 kB
  • 9. Recurrent Neural Networks - RNNs/11. RNN on a Time Series - Part Two.srt 32.2 kB
  • 7. Basic Artificial Neural Networks - ANNs/27. Tensorboard.srt 29.4 kB
  • 11. AutoEncoders/3. Autoencoder for Dimensionality Reduction.srt 28.7 kB
  • 7. Basic Artificial Neural Networks - ANNs/20. Keras Project Solutions - Exploratory Data Analysis.srt 28.5 kB
  • 3. NumPy Crash Course/2. NumPy Arrays.srt 28.1 kB
  • 7. Basic Artificial Neural Networks - ANNs/6. Cost Functions and Gradient Descent.srt 27.7 kB
  • 13. Deployment/7. Flask Front End.srt 26.9 kB
  • 9. Recurrent Neural Networks - RNNs/14. Bonus - Multivariate Time Series - RNN and LSTMs.srt 26.5 kB
  • 7. Basic Artificial Neural Networks - ANNs/12. Keras Regression Code Along - Exploratory Data Analysis.srt 26.5 kB
  • 7. Basic Artificial Neural Networks - ANNs/23. Keras Project Solutions - Categorical Data.srt 25.6 kB
  • 6. Machine Learning Concepts Overview/4. Evaluating Performance - Classification Error Metrics.srt 25.3 kB
  • 5. Visualization Crash Course/3. Seaborn Basics.srt 25.0 kB
  • 13. Deployment/8. Live Deployment to the Web.srt 24.9 kB
  • 7. Basic Artificial Neural Networks - ANNs/17. Keras Classification - Dealing with Overfitting and Evaluation.srt 24.5 kB
  • 11. AutoEncoders/4. Autoencoder for Images - Part One.srt 24.0 kB
  • 8. Convolutional Neural Networks - CNNs/7. CNN on MNIST - Part Two - Creating and Training the Model.srt 23.9 kB
  • 8. Convolutional Neural Networks - CNNs/13. CNN on Real Image Files - Part Two - Data Processing.srt 23.5 kB
  • 13. Deployment/2. Creating the Model.srt 22.7 kB
  • 8. Convolutional Neural Networks - CNNs/3. Convolutional Layers.srt 21.3 kB
  • 9. Recurrent Neural Networks - RNNs/8. RNN on a Sine Wave - Creating the Model.srt 21.1 kB
  • 7. Basic Artificial Neural Networks - ANNs/21. Keras Project Solutions - Dealing with Missing Data.srt 20.9 kB
  • 7. Basic Artificial Neural Networks - ANNs/7. Backpropagation.srt 20.8 kB
  • 7. Basic Artificial Neural Networks - ANNs/10. Keras Syntax Basics - Part Two - Creating and Training the Model.srt 20.4 kB
  • 8. Convolutional Neural Networks - CNNs/12. CNN on Real Image Files - Part One - Reading in the Data.srt 20.4 kB
  • 8. Convolutional Neural Networks - CNNs/14. CNN on Real Image Files - Part Three - Creating the Model.srt 20.1 kB
  • 4. Pandas Crash Course/7. Pandas Operations.srt 19.1 kB
  • 7. Basic Artificial Neural Networks - ANNs/13. Keras Regression Code Along - Exploratory Data Analysis - Continued.srt 18.8 kB
  • 9. Recurrent Neural Networks - RNNs/9. RNN on a Sine Wave - LSTMs and Forecasting.srt 18.4 kB
  • 8. Convolutional Neural Networks - CNNs/2. Image Filters and Kernels.srt 18.4 kB
  • 10. Natural Language Processing/4. NLP - Part Three - Creating Batches.srt 18.4 kB
  • 8. Convolutional Neural Networks - CNNs/6. CNN on MNIST - Part One - The Data.srt 18.1 kB
  • 4. Pandas Crash Course/3. Pandas DataFrames - Part One.srt 17.7 kB
  • 12. Generative Adversarial Networks/3. Creating a GAN - Part Two - The Model.srt 17.7 kB
  • 7. Basic Artificial Neural Networks - ANNs/22. Keras Project Solutions - Dealing with Missing Data - Part Two.srt 17.5 kB
  • 4. Pandas Crash Course/8. Data Input and Output.srt 17.3 kB
  • 7. Basic Artificial Neural Networks - ANNs/11. Keras Syntax Basics - Part Three - Model Evaluation.srt 17.1 kB
  • 9. Recurrent Neural Networks - RNNs/4. LSTMS and GRU.srt 17.1 kB
  • 8. Convolutional Neural Networks - CNNs/9. CNN on CIFAR-10 - Part One - The Data.srt 16.9 kB
  • 7. Basic Artificial Neural Networks - ANNs/4. Activation Functions.srt 16.4 kB
  • 7. Basic Artificial Neural Networks - ANNs/5. Multi-Class Classification Considerations.srt 16.3 kB
  • 7. Basic Artificial Neural Networks - ANNs/15. Keras Regression Code Along - Model Evaluation and Predictions.srt 16.2 kB
  • 13. Deployment/4. Running a Basic Flask Application.srt 15.5 kB
  • 3. NumPy Crash Course/3. Numpy Index Selection.srt 15.4 kB
  • 4. Pandas Crash Course/5. Pandas Missing Data.srt 15.4 kB
  • 13. Deployment/5. Flask Postman API.srt 15.3 kB
  • 7. Basic Artificial Neural Networks - ANNs/9. Keras Syntax Basics - Part One - Preparing the Data.srt 15.1 kB
  • 7. Basic Artificial Neural Networks - ANNs/2. Perceptron Model.srt 14.9 kB
  • 10. Natural Language Processing/5. NLP - Part Four - Creating the Model.srt 14.6 kB
  • 4. Pandas Crash Course/6. GroupBy Operations.srt 14.3 kB
  • 11. AutoEncoders/7. Autoencoder Exercise - Solutions.srt 14.3 kB
  • 10. Natural Language Processing/6. NLP - Part Five - Training the Model.srt 14.1 kB
  • 4. Pandas Crash Course/4. Pandas DataFrames - Part Two.srt 13.9 kB
  • 9. Recurrent Neural Networks - RNNs/10. RNN on a Time Series - Part One.srt 13.9 kB
  • 7. Basic Artificial Neural Networks - ANNs/26. Keras Project Solutions - Model Evaluation.srt 13.7 kB
  • 5. Visualization Crash Course/2. Matplotlib Basics.srt 13.7 kB
  • 4. Pandas Crash Course/2. Pandas Series.srt 12.8 kB
  • 13. Deployment/3. Model Prediction Function.srt 12.7 kB
  • 7. Basic Artificial Neural Networks - ANNs/19. TensorFlow 2.0 Keras Project Notebook Overview.srt 12.7 kB
  • 6. Machine Learning Concepts Overview/2. Supervised Learning Overview.srt 12.5 kB
  • 9. Recurrent Neural Networks - RNNs/6. RNN on a Sine Wave - The Data.srt 12.5 kB
  • 8. Convolutional Neural Networks - CNNs/15. CNN on Real Image Files - Part Four - Evaluating the Model.srt 12.3 kB
  • 9. Recurrent Neural Networks - RNNs/5. RNN Batches.srt 12.2 kB
  • 6. Machine Learning Concepts Overview/3. Overfitting.srt 12.1 kB
  • 7. Basic Artificial Neural Networks - ANNs/14. Keras Regression Code Along - Data Preprocessing and Creating a Model.srt 12.1 kB
  • 12. Generative Adversarial Networks/1. GANs Overview.srt 12.0 kB
  • 3. NumPy Crash Course/4. NumPy Operations.srt 11.9 kB
  • 10. Natural Language Processing/7. NLP - Part Six - Generating Text.srt 11.9 kB
  • 8. Convolutional Neural Networks - CNNs/17. CNN Exercise Solutions.srt 11.9 kB
  • 9. Recurrent Neural Networks - RNNs/2. RNN Basic Theory.srt 11.7 kB
  • 11. AutoEncoders/2. Autoencoder Basics.srt 11.6 kB
  • 11. AutoEncoders/5. Autoencoder for Images - Part Two - Noise Removal.srt 11.6 kB
  • 9. Recurrent Neural Networks - RNNs/7. RNN on a Sine Wave - Batch Generator.srt 11.5 kB
  • 7. Basic Artificial Neural Networks - ANNs/16. Keras Classification Code Along - EDA and Preprocessing.srt 11.4 kB
  • 9. Recurrent Neural Networks - RNNs/3. Vanishing Gradients.srt 11.1 kB
  • 5. Visualization Crash Course/5. Data Visualization Exercises - Solutions.srt 11.0 kB
  • 7. Basic Artificial Neural Networks - ANNs/3. Neural Networks.srt 11.0 kB
  • 3. NumPy Crash Course/6. Numpy Exercises - Solutions.srt 10.9 kB
  • 8. Convolutional Neural Networks - CNNs/10. CNN on CIFAR-10 - Part Two - Evaluating the Model.srt 10.7 kB
  • 4. Pandas Crash Course/10. Pandas Exercises - Solutions.srt 10.2 kB
  • 8. Convolutional Neural Networks - CNNs/4. Pooling Layers.srt 10.2 kB
  • 12. Generative Adversarial Networks/5. DCGAN - Deep Convolutional Generative Adversarial Networks.srt 9.8 kB
  • 8. Convolutional Neural Networks - CNNs/8. CNN on MNIST - Part Three - Model Evaluation.srt 9.8 kB
  • 10. Natural Language Processing/1. Introduction to NLP Section.srt 9.0 kB
  • 8. Convolutional Neural Networks - CNNs/11. Downloading Data Set for Real Image Lectures.srt 8.8 kB
  • 6. Machine Learning Concepts Overview/5. Evaluating Performance - Regression Error Metrics.srt 8.6 kB
  • 6. Machine Learning Concepts Overview/1. What is Machine Learning.srt 8.3 kB
  • 1. Course Overview, Installs, and Setup/2. Course Overview.srt 7.7 kB
  • 6. Machine Learning Concepts Overview/6. Unsupervised Learning.srt 7.2 kB
  • 8. Convolutional Neural Networks - CNNs/5. MNIST Data Set Overview.srt 7.1 kB
  • 10. Natural Language Processing/2. NLP - Part One - The Data.srt 7.0 kB
  • 9. Recurrent Neural Networks - RNNs/12. RNN Exercise.srt 6.9 kB
  • 12. Generative Adversarial Networks/2. Creating a GAN - Part One- The Data.srt 6.7 kB
  • 4. Pandas Crash Course/1. Introduction to Pandas.srt 6.3 kB
  • 10. Natural Language Processing/3. NLP - Part Two - Text Processing.srt 6.0 kB
  • 7. Basic Artificial Neural Networks - ANNs/25. Keras Project Solutions - Creating and Training a Model.srt 6.0 kB
  • 13. Deployment/6. Flask API - Using Requests Programmatically.srt 5.8 kB
  • 13. Deployment/1. Introduction to Deployment.srt 5.5 kB
  • 1. Course Overview, Installs, and Setup/4. FAQ - Frequently Asked Questions.html 5.5 kB
  • 11. AutoEncoders/6. Autoencoder Exercise Overview.srt 5.3 kB
  • 5. Visualization Crash Course/4. Data Visualization Exercises.srt 5.2 kB
  • 11. AutoEncoders/1. Introduction to Autoencoders.srt 5.1 kB
  • 7. Basic Artificial Neural Networks - ANNs/24. Keras Project Solutions - Data PreProcessing.srt 5.1 kB
  • 4. Pandas Crash Course/9. Pandas Exercises.srt 4.4 kB
  • 9. Recurrent Neural Networks - RNNs/1. RNN Section Overview.srt 4.1 kB
  • 8. Convolutional Neural Networks - CNNs/16. CNN Exercise Overview.srt 3.9 kB
  • 3. NumPy Crash Course/1. Introduction to NumPy.srt 3.6 kB
  • 7. Basic Artificial Neural Networks - ANNs/1. Introduction to ANN Section.srt 3.4 kB
  • 7. Basic Artificial Neural Networks - ANNs/8. TensorFlow vs. Keras Explained.srt 3.0 kB
  • 7. Basic Artificial Neural Networks - ANNs/18. TensorFlow 2.0 Keras Project Options Overview.srt 2.6 kB
  • 8. Convolutional Neural Networks - CNNs/1. CNN Section Overview.srt 2.5 kB
  • 3. NumPy Crash Course/5. NumPy Exercises.srt 2.2 kB
  • 5. Visualization Crash Course/1. Introduction to Python Visualization.srt 2.0 kB
  • 1. Course Overview, Installs, and Setup/1. Auto-Welcome Message.html 1.1 kB
  • 10. Natural Language Processing/How you can help GetFreeCourses.Co.txt 182 Bytes
  • 4. Pandas Crash Course/How you can help GetFreeCourses.Co.txt 182 Bytes
  • 7. Basic Artificial Neural Networks - ANNs/How you can help GetFreeCourses.Co.txt 182 Bytes
  • How you can help GetFreeCourses.Co.txt 182 Bytes
  • 2. COURSE OVERVIEW CONFIRMATION/1. PLEASE WATCH COURSE OVERVIEW LECTURE.html 165 Bytes
  • 9. Recurrent Neural Networks - RNNs/4.2 How to choose between LSTM vs GRU.html 140 Bytes
  • 1. Course Overview, Installs, and Setup/3.2 requirements.txt 138 Bytes
  • 8. Convolutional Neural Networks - CNNs/11.1 Direct Link to Download cell_images.zip (Note You can't preview a zip file) Just download it..html 127 Bytes
  • 10. Natural Language Processing/Download Paid Udemy Courses For Free.url 116 Bytes
  • 10. Natural Language Processing/GetFreeCourses.Co.url 116 Bytes
  • 4. Pandas Crash Course/Download Paid Udemy Courses For Free.url 116 Bytes
  • 4. Pandas Crash Course/GetFreeCourses.Co.url 116 Bytes
  • 7. Basic Artificial Neural Networks - ANNs/Download Paid Udemy Courses For Free.url 116 Bytes
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