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[FreeCoursesOnline.Me] PacktPub - Deep Learning with Real World Projects [Video]

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种子名称: [FreeCoursesOnline.Me] PacktPub - Deep Learning with Real World Projects [Video]
文件类型: 视频
文件数目: 156个文件
文件大小: 7.03 GB
收录时间: 2020-1-27 11:51
已经下载: 3
资源热度: 148
最近下载: 2024-6-28 08:51

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[FreeCoursesOnline.Me] PacktPub - Deep Learning with Real World Projects [Video].torrent
  • 1 - Introduction/Activation Functions.mp4151.3MB
  • 1 - Introduction/Code Password.mp4719.85KB
  • 1 - Introduction/History of Deep learning.mp481.44MB
  • 1 - Introduction/Introduction.mp465.6MB
  • 1 - Introduction/Multi-Level Perceptrons.mp481.52MB
  • 1 - Introduction/Neural Network Playground.mp4152.12MB
  • 1 - Introduction/Perceptrons.mp437.66MB
  • 1 - Introduction/Representations.mp4158.08MB
  • 1 - Introduction/Training Neural Network - Part 1.mp4122.56MB
  • 1 - Introduction/Training Neural Network - Part 2.mp456.79MB
  • 1 - Introduction/Training Neural Network - Part 3.mp4110.75MB
  • 10 - CNN-Industry Live Project - Find..Save Life/Introduction.mp410.86MB
  • 10 - CNN-Industry Live Project - Find..Save Life/Working with X-Ray images - Case Study - Part 1.mp49.03MB
  • 10 - CNN-Industry Live Project - Find..Save Life/Working with X-Ray images - Case Study - Part 2.mp48.82MB
  • 10 - CNN-Industry Live Project - Find..Save Life/Working with X-Ray images - Case Study - Part 3.mp414.09MB
  • 10 - CNN-Industry Live Project - Find..Save Life/Working with X-Ray images - Case Study - Part 4.mp418.31MB
  • 10 - CNN-Industry Live Project - Find..Save Life/Working with X-Ray images - Case Study - Part 5.mp426.57MB
  • 10 - CNN-Industry Live Project - Find..Save Life/Working with X-Ray images - Case Study - Part 6.mp418.34MB
  • 11 - Recurrent Neural Networks - Introduction/Architecture.mp422.48MB
  • 11 - Recurrent Neural Networks - Introduction/Batch data.mp48.52MB
  • 11 - Recurrent Neural Networks - Introduction/Introduction to RNN.mp413.47MB
  • 11 - Recurrent Neural Networks - Introduction/One-to-Many.mp417.41MB
  • 11 - Recurrent Neural Networks - Introduction/RNN - Part 1.mp49.87MB
  • 11 - Recurrent Neural Networks - Introduction/RNN Formula.mp429.21MB
  • 11 - Recurrent Neural Networks - Introduction/RNN Part 2.mp46.43MB
  • 11 - Recurrent Neural Networks - Introduction/Simplified Notations.mp429.4MB
  • 11 - Recurrent Neural Networks - Introduction/Training RNN.mp411.6MB
  • 11 - Recurrent Neural Networks - Introduction/Types of RNN - Part 1.mp47.41MB
  • 11 - Recurrent Neural Networks - Introduction/Types of RNN - Part 2.mp413.22MB
  • 11 - Recurrent Neural Networks - Introduction/Vanishing Gradient.mp421.37MB
  • 12 - Recurrent Neural Networks - LSTM/Bidirectional RNN.mp414.31MB
  • 12 - Recurrent Neural Networks - LSTM/Gated Recurrent Network (GRU).mp427.03MB
  • 12 - Recurrent Neural Networks - LSTM/Introduction.mp44.43MB
  • 12 - Recurrent Neural Networks - LSTM/LSTM - Part 1.mp47.66MB
  • 12 - Recurrent Neural Networks - LSTM/LSTM - Part 2.mp45.96MB
  • 12 - Recurrent Neural Networks - LSTM/LSTM - Part 3.mp44.17MB
  • 12 - Recurrent Neural Networks - LSTM/LSTM - Part 4.mp411.56MB
  • 12 - Recurrent Neural Networks - LSTM/LSTM - Part 5.mp419.68MB
  • 12 - Recurrent Neural Networks - LSTM/LSTM Equation.mp48.51MB
  • 12 - Recurrent Neural Networks - LSTM/Online Offline Mode.mp410.25MB
  • 13 - Recurrent Neutral Networks - Part-Of-Speech Tagger/Part-Of-Speech Tagger case- study (Part-2).mp487.66MB
  • 13 - Recurrent Neutral Networks - Part-Of-Speech Tagger/Part-Of-Speech Tagger case- study (Part-3).mp442.59MB
  • 13 - Recurrent Neutral Networks - Part-Of-Speech Tagger/Part-Of-Speech Tagger case- study (Part-4).mp457.08MB
  • 13 - Recurrent Neutral Networks - Part-Of-Speech Tagger/Part-Of-Speech Tagger case- study (Part-5).mp4109.35MB
  • 13 - Recurrent Neutral Networks - Part-Of-Speech Tagger/Part-Of-Speech Tagger case- study (Part-6).mp425.65MB
  • 13 - Recurrent Neutral Networks - Part-Of-Speech Tagger/Part-Of-Speech Tagger case- study (Part-7).mp459.99MB
  • 13 - Recurrent Neutral Networks - Part-Of-Speech Tagger/Part-Of-Speech Tagger case- study (Part-8).mp4120.05MB
  • 13 - Recurrent Neutral Networks - Part-Of-Speech Tagger/Part-Of-Speech Tagger case- study (Part-9).mp430.86MB
  • 13 - Recurrent Neutral Networks - Part-Of-Speech Tagger/Part-Of-Speech Tagger case-study (Part-1).mp456.18MB
  • 14 - Text generation using RNN/Text Generation - Code generator case- study (Part-1).mp4171.29MB
  • 14 - Text generation using RNN/Text Generation - Code generator case- study (Part-2).mp4105.19MB
  • 14 - Text generation using RNN/Text Generation - Code generator case- study (Part-3).mp448.67MB
  • 14 - Text generation using RNN/Text Generation - Code generator case- study (Part-4).mp440.03MB
  • 2 - Artificial Neural Networks-Introduction/Activation Functions.mp441.88MB
  • 2 - Artificial Neural Networks-Introduction/Assumptions in Neural Networks.mp445.8MB
  • 2 - Artificial Neural Networks-Introduction/Deep Learning.mp428.97MB
  • 2 - Artificial Neural Networks-Introduction/Example for Perceptron.mp444.98MB
  • 2 - Artificial Neural Networks-Introduction/Homogeneous Co-ordinate.mp423.61MB
  • 2 - Artificial Neural Networks-Introduction/Input Layer.mp453.57MB
  • 2 - Artificial Neural Networks-Introduction/Introduction.mp423.91MB
  • 2 - Artificial Neural Networks-Introduction/Multi Classifier.mp437.87MB
  • 2 - Artificial Neural Networks-Introduction/Neural Networks.mp449.32MB
  • 2 - Artificial Neural Networks-Introduction/Output Layer.mp414.07MB
  • 2 - Artificial Neural Networks-Introduction/Perceptron for Classifiers.mp431.96MB
  • 2 - Artificial Neural Networks-Introduction/Perceptron in Depth.mp430.71MB
  • 2 - Artificial Neural Networks-Introduction/Perceptron.mp429.65MB
  • 2 - Artificial Neural Networks-Introduction/Sigmoid function.mp426.53MB
  • 2 - Artificial Neural Networks-Introduction/Training in Neural Networks.mp432.74MB
  • 2 - Artificial Neural Networks-Introduction/Understanding Human Brain.mp426.64MB
  • 2 - Artificial Neural Networks-Introduction/Understanding MNIST.mp420.44MB
  • 2 - Artificial Neural Networks-Introduction/Understanding Notations.mp499.95MB
  • 3 - ANN - Feed Forward Network/Bidirectional RNN.mp443.49MB
  • 3 - ANN - Feed Forward Network/Introduction.mp450.45MB
  • 3 - ANN - Feed Forward Network/Online Offline Mode.mp436.04MB
  • 3 - ANN - Feed Forward Network/Pseudocode for Batch.mp428.82MB
  • 3 - ANN - Feed Forward Network/Pseudocode.mp440.65MB
  • 3 - ANN - Feed Forward Network/Understanding Dimensions.mp458.63MB
  • 3 - ANN - Feed Forward Network/Vectorised Methods.mp481.39MB
  • 4 - Back Propagation/Back Propagation Training - Part 1.mp446.54MB
  • 4 - Back Propagation/Back Propagation Training - Part 10.mp423.04MB
  • 4 - Back Propagation/Back Propagation Training - Part 2.mp438.07MB
  • 4 - Back Propagation/Back Propagation Training - Part 3.mp414.74MB
  • 4 - Back Propagation/Back Propagation Training - Part 4.mp434.74MB
  • 4 - Back Propagation/Back Propagation Training - Part 5.mp434.76MB
  • 4 - Back Propagation/Back Propagation Training - Part 6.mp423.3MB
  • 4 - Back Propagation/Back Propagation Training - Part 7.mp420.59MB
  • 4 - Back Propagation/Back Propagation Training - Part 8.mp427MB
  • 4 - Back Propagation/Back Propagation Training - Part 9.mp428.96MB
  • 4 - Back Propagation/Finding Global Minima.mp410.31MB
  • 4 - Back Propagation/Introducing Loss Function.mp444.13MB
  • 4 - Back Propagation/Introduction.mp435.37MB
  • 4 - Back Propagation/Pseudocode.mp412.67MB
  • 4 - Back Propagation/SGD.mp438.95MB
  • 4 - Back Propagation/Sigmoid Function.mp425.96MB
  • 4 - Back Propagation/Training for Batches.mp422.82MB
  • 5 - Regularisation/Batch Normalisation - Part 1.mp436.08MB
  • 5 - Regularisation/Batch Normalisation - Part 2.mp437.55MB
  • 5 - Regularisation/Batch Normalisation - Part 3.mp450.64MB
  • 5 - Regularisation/Dropouts Part 1.mp424.18MB
  • 5 - Regularisation/Dropouts Part 2.mp413.46MB
  • 5 - Regularisation/Introducing Keras.mp4122.91MB
  • 5 - Regularisation/Introducing TensorFlow.mp445.42MB
  • 5 - Regularisation/Introduction to Regularisation.mp449.04MB
  • 6 - Convolution Neural Networks/Applications for CNN.mp454.5MB
  • 6 - Convolution Neural Networks/Combining Network.mp453.18MB
  • 6 - Convolution Neural Networks/Convolution - Part 1.mp443.4MB
  • 6 - Convolution Neural Networks/Convolution - Part 2.mp479.49MB
  • 6 - Convolution Neural Networks/Feature Map.mp4127.57MB
  • 6 - Convolution Neural Networks/Formulas.mp418.56MB
  • 6 - Convolution Neural Networks/Idea behind CNN - Part 1.mp445.92MB
  • 6 - Convolution Neural Networks/Idea behind CNN - Part 2.mp475.88MB
  • 6 - Convolution Neural Networks/Images.mp4161.11MB
  • 6 - Convolution Neural Networks/Introduction.mp442.93MB
  • 6 - Convolution Neural Networks/Padding.mp415.13MB
  • 6 - Convolution Neural Networks/Pooling.mp470.59MB
  • 6 - Convolution Neural Networks/Stride and Padding.mp434.19MB
  • 6 - Convolution Neural Networks/Video.mp440MB
  • 6 - Convolution Neural Networks/Weight and Bias.mp482.52MB
  • 7 - CNN-Keras/Introduction.mp47.73MB
  • 7 - CNN-Keras/Practical on CNN - Case Study - Part 1.mp410.29MB
  • 7 - CNN-Keras/Practical on CNN - Case Study - Part 2.mp428.31MB
  • 7 - CNN-Keras/Practical on CNN - Case Study - Part 3.mp436MB
  • 7 - CNN-Keras/Practical on CNN - Case Study - Part 4.mp413.22MB
  • 7 - CNN-Keras/Practical on CNN - Case Study - Part 5.mp47.5MB
  • 7 - CNN-Keras/VGG16 (Visual Geometry Group).mp444.63MB
  • 8 - CNN-Transfer Learning/AlexNet.mp473.82MB
  • 8 - CNN-Transfer Learning/Analysis - Part 1.mp4109.77MB
  • 8 - CNN-Transfer Learning/Analysis - Part 2.mp446.01MB
  • 8 - CNN-Transfer Learning/Case Study - Part 1.mp4194.08MB
  • 8 - CNN-Transfer Learning/Case Study - Part 2.mp495.9MB
  • 8 - CNN-Transfer Learning/Case Study - Part 3.mp439.14MB
  • 8 - CNN-Transfer Learning/GoogleNet.mp448.7MB
  • 8 - CNN-Transfer Learning/Introduction.mp444.61MB
  • 8 - CNN-Transfer Learning/ResNet - Part 1.mp432.3MB
  • 8 - CNN-Transfer Learning/ResNet - Part 2.mp428.15MB
  • 8 - CNN-Transfer Learning/Transfer Learning - Part 1.mp47.42MB
  • 8 - CNN-Transfer Learning/Transfer Learning - Part 2.mp419.86MB
  • 8 - CNN-Transfer Learning/Transfer Learning - Part 3.mp435.32MB
  • 8 - CNN-Transfer Learning/Transfer Learning - Part 4.mp439.9MB
  • 8 - CNN-Transfer Learning/Transfer Learning - Part 5.mp427.1MB
  • 8 - CNN-Transfer Learning/Transfer Learning - Part 6.mp436.34MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Introduction.mp419.69MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 1.mp449.56MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 10.mp456.74MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 11.mp461.74MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 12.mp4159.46MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 13.mp431.75MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 14.mp475.11MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 2.mp4118.7MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 3.mp450.13MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 4.mp448.57MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 5.mp439.33MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 6.mp472.1MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 7.mp426.29MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 8.mp491.39MB
  • 9 - CNN-Industry Live Project - Playing..Natural Images/Working with Flower Images - Case Study - Part 9.mp481.05MB