Deep Learning - Artificial Neural Networks with Tensorflow

Creation Time	Sept. 19, 2024, 2:02 p.m.
Last Access Time	Feb. 23, 2025, 8:43 p.m.
File Size	1.1 GB
Keywords	Neural - Artificial Deep Learning Tensorflow with Networks
Total Requests	202
Total Files	64
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Chapter 3 Feedforward Artificial Neural Networks/004. Activation Functions.mp4 61.7 MB
Chapter 3 Feedforward Artificial Neural Networks/009. ANN for Regression.mp4 56.0 MB
Chapter 2 Machine Learning and Neurons/001. What Is Machine Learning.mp4 53.5 MB
Chapter 3 Feedforward Artificial Neural Networks/006. How to Represent Images.mp4 49.6 MB
Chapter 2 Machine Learning and Neurons/002. Code Preparation (Classification Theory).mp4 48.3 MB
Chapter 2 Machine Learning and Neurons/005. Regression Notebook.mp4 47.7 MB
Chapter 2 Machine Learning and Neurons/003. Classification Notebook.mp4 44.7 MB
Chapter 3 Feedforward Artificial Neural Networks/007. Code Preparation (Artificial Neural Networks).mp4 42.2 MB
Chapter 5 In-Depth Gradient Descent/005. Adam Optimization (Part 1).mp4 42.1 MB
Chapter 5 In-Depth Gradient Descent/006. Adam Optimization (Part 2).mp4 40.5 MB
Chapter 3 Feedforward Artificial Neural Networks/008. ANN for Image Classification.mp4 40.2 MB
Chapter 3 Feedforward Artificial Neural Networks/003. The Geometrical Picture.mp4 39.4 MB
Chapter 2 Machine Learning and Neurons/007. How Does a Model Learn .mp4 38.6 MB
Chapter 2 Machine Learning and Neurons/006. The Neuron.mp4 34.1 MB
Chapter 3 Feedforward Artificial Neural Networks/005. Multiclass Classification.mp4 33.9 MB
Chapter 3 Feedforward Artificial Neural Networks/002. Forward Propagation.mp4 33.7 MB
Chapter 5 In-Depth Gradient Descent/004. Variable and Adaptive Learning Rates.mp4 32.6 MB
Chapter 4 In-Depth Loss Functions/001. Mean Squared Error.mp4 30.3 MB
Chapter 2 Machine Learning and Neurons/008. Making Predictions.mp4 29.7 MB
Chapter 5 In-Depth Gradient Descent/003. Momentum.mp4 28.8 MB
Chapter 5 In-Depth Gradient Descent/001. Gradient Descent.mp4 28.5 MB
Chapter 2 Machine Learning and Neurons/009. Saving and Loading a Model.mp4 27.9 MB
Chapter 4 In-Depth Loss Functions/003. Categorical Cross Entropy.mp4 26.9 MB
Chapter 3 Feedforward Artificial Neural Networks/001. Artificial Neural Networks Section Introduction.mp4 26.7 MB
Chapter 3 Feedforward Artificial Neural Networks/010. How to Choose Hyperparameters.mp4 23.6 MB
Chapter 2 Machine Learning and Neurons/004. Code Preparation (Regression Theory).mp4 23.5 MB
Chapter 1 Welcome/002. Outline.mp4 21.9 MB
Chapter 5 In-Depth Gradient Descent/002. Stochastic Gradient Descent.mp4 20.6 MB
Chapter 4 In-Depth Loss Functions/002. Binary Cross Entropy.mp4 19.8 MB
Chapter 1 Welcome/001. Introduction.mp4 19.4 MB
Chapter 2 Machine Learning and Neurons/010. Why Keras.mp4 17.6 MB
Chapter 2 Machine Learning and Neurons/011. Suggestion Box.mp4 17.3 MB
Chapter 3 Feedforward Artificial Neural Networks/004. Activation Functions.en.srt 24.1 KB
Chapter 2 Machine Learning and Neurons/002. Code Preparation (Classification Theory).en.srt 22.2 KB
Chapter 2 Machine Learning and Neurons/001. What Is Machine Learning.en.srt 20.4 KB
Chapter 5 In-Depth Gradient Descent/005. Adam Optimization (Part 1).en.srt 17.7 KB
Chapter 3 Feedforward Artificial Neural Networks/007. Code Preparation (Artificial Neural Networks).en.srt 17.4 KB
Chapter 3 Feedforward Artificial Neural Networks/006. How to Represent Images.en.srt 16.9 KB
Chapter 5 In-Depth Gradient Descent/004. Variable and Adaptive Learning Rates.en.srt 16.4 KB
Chapter 5 In-Depth Gradient Descent/006. Adam Optimization (Part 2).en.srt 15.0 KB
Chapter 2 Machine Learning and Neurons/007. How Does a Model Learn .en.srt 14.9 KB
Chapter 3 Feedforward Artificial Neural Networks/009. ANN for Regression.en.srt 14.4 KB
Chapter 2 Machine Learning and Neurons/006. The Neuron.en.srt 13.9 KB
Chapter 2 Machine Learning and Neurons/005. Regression Notebook.en.srt 13.1 KB
Chapter 3 Feedforward Artificial Neural Networks/002. Forward Propagation.en.srt 13.1 KB
Chapter 3 Feedforward Artificial Neural Networks/003. The Geometrical Picture.en.srt 12.7 KB
Chapter 4 In-Depth Loss Functions/001. Mean Squared Error.en.srt 11.9 KB
Chapter 3 Feedforward Artificial Neural Networks/005. Multiclass Classification.en.srt 11.9 KB
Chapter 3 Feedforward Artificial Neural Networks/008. ANN for Image Classification.en.srt 11.1 KB
Chapter 5 In-Depth Gradient Descent/001. Gradient Descent.en.srt 10.5 KB
Chapter 4 In-Depth Loss Functions/003. Categorical Cross Entropy.en.srt 10.4 KB
Chapter 2 Machine Learning and Neurons/003. Classification Notebook.en.srt 10.1 KB
Chapter 2 Machine Learning and Neurons/004. Code Preparation (Regression Theory).en.srt 9.6 KB
Chapter 2 Machine Learning and Neurons/008. Making Predictions.en.srt 8.8 KB
Chapter 3 Feedforward Artificial Neural Networks/010. How to Choose Hyperparameters.en.srt 8.6 KB
Chapter 3 Feedforward Artificial Neural Networks/001. Artificial Neural Networks Section Introduction.en.srt 8.5 KB
Chapter 5 In-Depth Gradient Descent/003. Momentum.en.srt 8.0 KB
Chapter 4 In-Depth Loss Functions/002. Binary Cross Entropy.en.srt 7.6 KB
Chapter 1 Welcome/002. Outline.en.srt 7.5 KB
Chapter 2 Machine Learning and Neurons/010. Why Keras.en.srt 6.5 KB
Chapter 5 In-Depth Gradient Descent/002. Stochastic Gradient Descent.en.srt 5.6 KB
Chapter 2 Machine Learning and Neurons/009. Saving and Loading a Model.en.srt 5.5 KB
Chapter 2 Machine Learning and Neurons/011. Suggestion Box.en.srt 4.6 KB
Chapter 1 Welcome/001. Introduction.en.srt 3.5 KB

Deep Learning - Artificial Neural Networks with Tensorflow

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