Deep Learning - Artificial Neural Networks with Tensorflow

磁力链接/BT种子名称

Deep Learning - Artificial Neural Networks with Tensorflow

磁力链接/BT种子简介

种子哈希：75dd48f88bb1aa3d7be7356bf08ae454f9d3e8b1
文件大小： 1.08G
已经下载：4306次
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收录时间：2024-07-08
最近下载：2025-10-21

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文件列表

  • Chapter 3 Feedforward Artificial Neural Networks/004. Activation Functions.mp4 64.7 MB
  • Chapter 3 Feedforward Artificial Neural Networks/009. ANN for Regression.mp4 58.7 MB
  • Chapter 2 Machine Learning and Neurons/001. What Is Machine Learning.mp4 56.1 MB
  • Chapter 3 Feedforward Artificial Neural Networks/006. How to Represent Images.mp4 52.0 MB
  • Chapter 2 Machine Learning and Neurons/002. Code Preparation (Classification Theory).mp4 50.7 MB
  • Chapter 2 Machine Learning and Neurons/005. Regression Notebook.mp4 50.0 MB
  • Chapter 2 Machine Learning and Neurons/003. Classification Notebook.mp4 46.9 MB
  • Chapter 3 Feedforward Artificial Neural Networks/007. Code Preparation (Artificial Neural Networks).mp4 44.2 MB
  • Chapter 5 In-Depth Gradient Descent/005. Adam Optimization (Part 1).mp4 44.1 MB
  • Chapter 5 In-Depth Gradient Descent/006. Adam Optimization (Part 2).mp4 42.4 MB
  • Chapter 3 Feedforward Artificial Neural Networks/008. ANN for Image Classification.mp4 42.1 MB
  • Chapter 3 Feedforward Artificial Neural Networks/003. The Geometrical Picture.mp4 41.3 MB
  • Chapter 2 Machine Learning and Neurons/007. How Does a Model Learn .mp4 40.4 MB
  • Chapter 2 Machine Learning and Neurons/006. The Neuron.mp4 35.7 MB
  • Chapter 3 Feedforward Artificial Neural Networks/005. Multiclass Classification.mp4 35.6 MB
  • Chapter 3 Feedforward Artificial Neural Networks/002. Forward Propagation.mp4 35.4 MB
  • Chapter 5 In-Depth Gradient Descent/004. Variable and Adaptive Learning Rates.mp4 34.2 MB
  • Chapter 4 In-Depth Loss Functions/001. Mean Squared Error.mp4 31.8 MB
  • Chapter 2 Machine Learning and Neurons/008. Making Predictions.mp4 31.2 MB
  • Chapter 5 In-Depth Gradient Descent/003. Momentum.mp4 30.2 MB
  • Chapter 5 In-Depth Gradient Descent/001. Gradient Descent.mp4 29.9 MB
  • Chapter 2 Machine Learning and Neurons/009. Saving and Loading a Model.mp4 29.3 MB
  • Chapter 4 In-Depth Loss Functions/003. Categorical Cross Entropy.mp4 28.3 MB
  • Chapter 3 Feedforward Artificial Neural Networks/001. Artificial Neural Networks Section Introduction.mp4 28.0 MB
  • Chapter 3 Feedforward Artificial Neural Networks/010. How to Choose Hyperparameters.mp4 24.8 MB
  • Chapter 2 Machine Learning and Neurons/004. Code Preparation (Regression Theory).mp4 24.7 MB
  • Chapter 1 Welcome/002. Outline.mp4 22.9 MB
  • Chapter 5 In-Depth Gradient Descent/002. Stochastic Gradient Descent.mp4 21.6 MB
  • Chapter 4 In-Depth Loss Functions/002. Binary Cross Entropy.mp4 20.7 MB
  • Chapter 1 Welcome/001. Introduction.mp4 20.3 MB
  • Chapter 2 Machine Learning and Neurons/010. Why Keras.mp4 18.4 MB
  • Chapter 2 Machine Learning and Neurons/011. Suggestion Box.mp4 18.1 MB
  • Chapter 3 Feedforward Artificial Neural Networks/004. Activation Functions.en.srt 24.6 kB
  • Chapter 2 Machine Learning and Neurons/002. Code Preparation (Classification Theory).en.srt 22.7 kB
  • Chapter 2 Machine Learning and Neurons/001. What Is Machine Learning.en.srt 20.9 kB
  • Chapter 5 In-Depth Gradient Descent/005. Adam Optimization (Part 1).en.srt 18.1 kB
  • Chapter 3 Feedforward Artificial Neural Networks/007. Code Preparation (Artificial Neural Networks).en.srt 17.8 kB
  • Chapter 3 Feedforward Artificial Neural Networks/006. How to Represent Images.en.srt 17.3 kB
  • Chapter 5 In-Depth Gradient Descent/004. Variable and Adaptive Learning Rates.en.srt 16.8 kB
  • Chapter 5 In-Depth Gradient Descent/006. Adam Optimization (Part 2).en.srt 15.3 kB
  • Chapter 2 Machine Learning and Neurons/007. How Does a Model Learn .en.srt 15.2 kB
  • Chapter 3 Feedforward Artificial Neural Networks/009. ANN for Regression.en.srt 14.7 kB
  • Chapter 2 Machine Learning and Neurons/006. The Neuron.en.srt 14.2 kB
  • Chapter 2 Machine Learning and Neurons/005. Regression Notebook.en.srt 13.4 kB
  • Chapter 3 Feedforward Artificial Neural Networks/002. Forward Propagation.en.srt 13.4 kB
  • Chapter 3 Feedforward Artificial Neural Networks/003. The Geometrical Picture.en.srt 13.0 kB
  • Chapter 4 In-Depth Loss Functions/001. Mean Squared Error.en.srt 12.2 kB
  • Chapter 3 Feedforward Artificial Neural Networks/005. Multiclass Classification.en.srt 12.1 kB
  • Chapter 3 Feedforward Artificial Neural Networks/008. ANN for Image Classification.en.srt 11.3 kB
  • Chapter 5 In-Depth Gradient Descent/001. Gradient Descent.en.srt 10.7 kB
  • Chapter 4 In-Depth Loss Functions/003. Categorical Cross Entropy.en.srt 10.6 kB
  • Chapter 2 Machine Learning and Neurons/003. Classification Notebook.en.srt 10.3 kB
  • Chapter 2 Machine Learning and Neurons/004. Code Preparation (Regression Theory).en.srt 9.9 kB
  • Chapter 2 Machine Learning and Neurons/008. Making Predictions.en.srt 9.0 kB
  • Chapter 3 Feedforward Artificial Neural Networks/010. How to Choose Hyperparameters.en.srt 8.8 kB
  • Chapter 3 Feedforward Artificial Neural Networks/001. Artificial Neural Networks Section Introduction.en.srt 8.7 kB
  • Chapter 5 In-Depth Gradient Descent/003. Momentum.en.srt 8.2 kB
  • Chapter 4 In-Depth Loss Functions/002. Binary Cross Entropy.en.srt 7.7 kB
  • Chapter 1 Welcome/002. Outline.en.srt 7.7 kB
  • Chapter 2 Machine Learning and Neurons/010. Why Keras.en.srt 6.6 kB
  • Chapter 5 In-Depth Gradient Descent/002. Stochastic Gradient Descent.en.srt 5.8 kB
  • Chapter 2 Machine Learning and Neurons/009. Saving and Loading a Model.en.srt 5.6 kB
  • Chapter 2 Machine Learning and Neurons/011. Suggestion Box.en.srt 4.7 kB
  • Chapter 1 Welcome/001. Introduction.en.srt 3.5 kB

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