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种子名称： [CourseClub.Me] Coursera - Deep Learning Specialization
文件类型： 视频
文件数目： 194个文件
文件大小： 3.43 GB
收录时间： 2021-9-3 05:51
已经下载： 3次
资源热度： 137
最近下载： 2024-5-21 18:39

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[CourseClub.Me] Coursera - Deep Learning Specialization.torrent

Convolutional Neural Networks/convolutional-neural-networks - 01 - Computer Vision.mp410.83MB

Convolutional Neural Networks/convolutional-neural-networks - 02 - Edge Detection Example.mp415.95MB

Convolutional Neural Networks/convolutional-neural-networks - 03 - More Edge Detection.mp412.17MB

Convolutional Neural Networks/convolutional-neural-networks - 04 - Padding.mp414.01MB

Convolutional Neural Networks/convolutional-neural-networks - 05 - Strided Convolutions.mp412.83MB

Convolutional Neural Networks/convolutional-neural-networks - 06 - Convolutions Over Volume.mp414.76MB

Convolutional Neural Networks/convolutional-neural-networks - 07 - One Layer of a Convolutional Network.mp423.79MB

Convolutional Neural Networks/convolutional-neural-networks - 08 - Simple Convolutional Network Example.mp412.35MB

Convolutional Neural Networks/convolutional-neural-networks - 09 - Pooling Layers.mp413.71MB

Convolutional Neural Networks/convolutional-neural-networks - 10 - CNN Example.mp419.79MB

Convolutional Neural Networks/convolutional-neural-networks - 11 - Why Convolutions.mp415.87MB

Convolutional Neural Networks/convolutional-neural-networks - 12 - Yann LeCun Interview.mp4166.89MB

Convolutional Neural Networks/convolutional-neural-networks - 13 - Why look at case studies.mp47.88MB

Convolutional Neural Networks/convolutional-neural-networks - 14 - Classic Networks.mp426.69MB

Convolutional Neural Networks/convolutional-neural-networks - 15 - ResNets.mp411.07MB

Convolutional Neural Networks/convolutional-neural-networks - 16 - Why ResNets Work.mp414.85MB

Convolutional Neural Networks/convolutional-neural-networks - 17 - Networks in Networks and 1x1 Convolutions.mp49.85MB

Convolutional Neural Networks/convolutional-neural-networks - 18 - Inception Network Motivation.mp415.71MB

Convolutional Neural Networks/convolutional-neural-networks - 19 - Inception Network.mp414.77MB

Convolutional Neural Networks/convolutional-neural-networks - 20 - MobileNet.mp425.42MB

Convolutional Neural Networks/convolutional-neural-networks - 21 - MobileNet Architecture.mp414.63MB

Convolutional Neural Networks/convolutional-neural-networks - 22 - EfficientNet.mp49.06MB

Convolutional Neural Networks/convolutional-neural-networks - 23 - Using Open-Source Implementation.mp413.52MB

Convolutional Neural Networks/convolutional-neural-networks - 24 - Transfer Learning.mp415.43MB

Convolutional Neural Networks/convolutional-neural-networks - 25 - Data Augmentation.mp416.52MB

Convolutional Neural Networks/convolutional-neural-networks - 26 - State of Computer Vision.mp418.52MB

Convolutional Neural Networks/convolutional-neural-networks - 27 - Object Localization.mp419.38MB

Convolutional Neural Networks/convolutional-neural-networks - 28 - Landmark Detection.mp411.66MB

Convolutional Neural Networks/convolutional-neural-networks - 29 - Object Detection.mp49.36MB

Convolutional Neural Networks/convolutional-neural-networks - 30 - Convolutional Implementation of Sliding Windows.mp417.68MB

Convolutional Neural Networks/convolutional-neural-networks - 31 - Bounding Box Predictions.mp425.81MB

Convolutional Neural Networks/convolutional-neural-networks - 32 - Intersection Over Union.mp47.21MB

Convolutional Neural Networks/convolutional-neural-networks - 33 - Non-max Suppression.mp412.44MB

Convolutional Neural Networks/convolutional-neural-networks - 34 - Anchor Boxes.mp418.61MB

Convolutional Neural Networks/convolutional-neural-networks - 35 - YOLO Algorithm.mp411.52MB

Convolutional Neural Networks/convolutional-neural-networks - 36 - Region Proposals (Optional).mp412.13MB

Convolutional Neural Networks/convolutional-neural-networks - 37 - Semantic Segmentation with U-Net.mp415.11MB

Convolutional Neural Networks/convolutional-neural-networks - 38 - Transpose Convolutions.mp412.93MB

Convolutional Neural Networks/convolutional-neural-networks - 39 - U-Net Architecture Intuition.mp46.02MB

Convolutional Neural Networks/convolutional-neural-networks - 40 - U-Net Architecture.mp412.7MB

Convolutional Neural Networks/convolutional-neural-networks - 41 - What is Face Recognition.mp414.28MB

Convolutional Neural Networks/convolutional-neural-networks - 42 - One Shot Learning.mp47.44MB

Convolutional Neural Networks/convolutional-neural-networks - 43 - Siamese Network.mp47.55MB

Convolutional Neural Networks/convolutional-neural-networks - 44 - Triplet Loss.mp424.97MB

Convolutional Neural Networks/convolutional-neural-networks - 45 - Face Verification and Binary Classification.mp49.73MB

Convolutional Neural Networks/convolutional-neural-networks - 46 - What is Neural Style Transfer.mp44.57MB

Convolutional Neural Networks/convolutional-neural-networks - 47 - What are deep ConvNets learning.mp414.7MB

Convolutional Neural Networks/convolutional-neural-networks - 48 - Cost Function.mp46.7MB

Convolutional Neural Networks/convolutional-neural-networks - 49 - Content Cost Function.mp45.66MB

Convolutional Neural Networks/convolutional-neural-networks - 50 - Style Cost Function.mp419.22MB

Convolutional Neural Networks/convolutional-neural-networks - 51 - 1D and 3D Generalizations.mp414.12MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 01 - Train Dev Test sets.mp416.77MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 02 - Bias Variance.mp413.36MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 03 - Basic Recipe for Machine Learning.mp410.23MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 04 - Regularization.mp414.23MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 05 - Why Regularization Reduces Overfitting.mp410.43MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 06 - Dropout Regularization.mp413.03MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 07 - Understanding Dropout.mp410.82MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 08 - Other Regularization Methods.mp411.75MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 09 - Normalizing Inputs.mp48.89MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 10 - Vanishing Exploding Gradients.mp410.7MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 11 - Weight Initialization for Deep Networks.mp410.11MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 12 - Numerical Approximation of Gradients.mp410.58MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 13 - Gradient Checking.mp49.64MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 14 - Gradient Checking Implementation Notes.mp49.12MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 15 - Yoshua Bengio Interview.mp4114.19MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 16 - Mini-batch Gradient Descent.mp419.28MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 17 - Understanding Mini-batch Gradient Descent.mp417.71MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 18 - Exponentially Weighted Averages.mp49.65MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 19 - Understanding Exponentially Weighted Averages.mp415.59MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 20 - Bias Correction in Exponentially Weighted Averages.mp49.07MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 21 - Gradient Descent with Momentum.mp415.03MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 22 - RMSprop.mp414.13MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 23 - Adam Optimization Algorithm.mp413.03MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 24 - Learning Rate Decay.mp414.09MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 25 - The Problem of Local Optima.mp48.97MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 26 - Yuanqing Lin Interview.mp464.23MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 27 - Tuning Process.mp411.8MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 28 - Using an Appropriate Scale to pick Hyperparameters.mp415.86MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 29 - Hyperparameters Tuning in Practice Pandas vs. Caviar.mp411.58MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 30 - Normalizing Activations in a Network.mp415.63MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 31 - Fitting Batch Norm into a Neural Network.mp420.72MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 32 - Why does Batch Norm work.mp422.89MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 33 - Batch Norm at Test Time.mp49.86MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 34 - Softmax Regression.mp417.54MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 35 - Training a Softmax Classifier.mp414.27MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 36 - Deep Learning Frameworks.mp410.01MB

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/deep-neural-network - 37 - TensorFlow.mp424.97MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 01 - Welcome.mp410.21MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 02 - What is a Neural Network.mp49.97MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 03 - Supervised Learning with Neural Networks.mp412.9MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 04 - Why is Deep Learning taking off.mp418.64MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 05 - About this Course.mp44.66MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 06 - Geoffrey Hinton Interview.mp4191.76MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 07 - Binary Classification.mp415.24MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 08 - Logistic Regression.mp48.48MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 09 - Logistic Regression Cost Function.mp413.19MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 10 - Gradient Descent.mp417.05MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 11 - Derivatives.mp413.41MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 12 - More Derivative Examples.mp416.76MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 13 - Computation Graph.mp45.66MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 14 - Derivatives with a Computation Graph.mp421.69MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 15 - Logistic Regression Gradient Descent.mp411.15MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 16 - Gradient Descent on m Examples.mp412.17MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 17 - Vectorization.mp412.6MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 18 - More Vectorization Examples.mp410.34MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 19 - Vectorizing Logistic Regression.mp411.46MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 20 - Vectorizing Logistic Regression's Gradient Output.mp415.55MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 21 - Broadcasting in Python.mp416.17MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 22 - A Note on Python Numpy Vectors.mp412.36MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 23 - Quick tour of Jupyter iPython Notebooks.mp49.23MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 24 - Explanation of Logistic Regression Cost Function (Optional).mp410.47MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 25 - Pieter Abbeel Interview.mp480.04MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 26 - Neural Networks Overview.mp47.23MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 27 - Neural Network Representation.mp48.26MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 28 - Computing a Neural Network's Output.mp416.32MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 29 - Vectorizing Across Multiple Examples.mp413.86MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 30 - Explanation for Vectorized Implementation.mp411.97MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 31 - Activation Functions.mp419.93MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 32 - Why do you need Non-Linear Activation Functions.mp49.29MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 33 - Derivatives of Activation Functions.mp411.38MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 34 - Gradient Descent for Neural Networks.mp416.01MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 35 - Backpropagation Intuition (Optional).mp426.04MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 36 - Random Initialization.mp411.96MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 37 - Ian Goodfellow Interview.mp454.53MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 38 - Deep L-layer Neural Network.mp410.35MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 39 - Forward Propagation in a Deep Network.mp413.02MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 40 - Getting your Matrix Dimensions Right.mp417.35MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 41 - Why Deep Representations.mp417.59MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 42 - Building Blocks of Deep Neural Networks.mp412.81MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 43 - Forward and Backward Propagation.mp419.8MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 44 - Parameters vs Hyperparameters.mp410.21MB

Neural Networks and Deep Learning/neural-networks-deep-learning - 45 - What does this have to do with the brain.mp46MB

Sequence Models/nlp-sequence-models - 01 - Why Sequence Models.mp45.25MB

Sequence Models/nlp-sequence-models - 02 - Notation.mp413.05MB

Sequence Models/nlp-sequence-models - 03 - Recurrent Neural Network Model.mp423.34MB

Sequence Models/nlp-sequence-models - 04 - Backpropagation Through Time.mp410.68MB

Sequence Models/nlp-sequence-models - 05 - Different Types of RNNs.mp414.87MB

Sequence Models/nlp-sequence-models - 06 - Language Model and Sequence Generation.mp417.67MB

Sequence Models/nlp-sequence-models - 07 - Sampling Novel Sequences.mp413.7MB

Sequence Models/nlp-sequence-models - 08 - Vanishing Gradients with RNNs.mp411.96MB

Sequence Models/nlp-sequence-models - 09 - Gated Recurrent Unit (GRU).mp426.99MB

Sequence Models/nlp-sequence-models - 10 - Long Short Term Memory (LSTM).mp418.81MB

Sequence Models/nlp-sequence-models - 11 - Bidirectional RNN.mp414.25MB

Sequence Models/nlp-sequence-models - 12 - Deep RNNs.mp48.58MB

Sequence Models/nlp-sequence-models - 13 - Word Representation.mp415.23MB

Sequence Models/nlp-sequence-models - 14 - Using Word Embeddings.mp413.76MB

Sequence Models/nlp-sequence-models - 15 - Properties of Word Embeddings.mp417.39MB

Sequence Models/nlp-sequence-models - 16 - Embedding Matrix.mp48.71MB

Sequence Models/nlp-sequence-models - 17 - Learning Word Embeddings.mp416.05MB

Sequence Models/nlp-sequence-models - 18 - Word2Vec.mp418.42MB

Sequence Models/nlp-sequence-models - 19 - Negative Sampling.mp417.91MB

Sequence Models/nlp-sequence-models - 20 - GloVe Word Vectors.mp415.57MB

Sequence Models/nlp-sequence-models - 21 - Sentiment Classification.mp410.9MB

Sequence Models/nlp-sequence-models - 22 - Debiasing Word Embeddings.mp416.69MB

Sequence Models/nlp-sequence-models - 23 - Basic Models.mp410.2MB

Sequence Models/nlp-sequence-models - 24 - Picking the Most Likely Sentence.mp414.14MB

Sequence Models/nlp-sequence-models - 25 - Beam Search.mp417.9MB

Sequence Models/nlp-sequence-models - 26 - Refinements to Beam Search.mp415.6MB

Sequence Models/nlp-sequence-models - 27 - Error Analysis in Beam Search.mp414.98MB

Sequence Models/nlp-sequence-models - 28 - Bleu Score (Optional).mp428.21MB

Sequence Models/nlp-sequence-models - 29 - Attention Model Intuition.mp414.76MB

Sequence Models/nlp-sequence-models - 30 - Attention Model.mp418.3MB

Sequence Models/nlp-sequence-models - 31 - Speech Recognition.mp413.35MB

Sequence Models/nlp-sequence-models - 32 - Trigger Word Detection.mp47.06MB

Sequence Models/nlp-sequence-models - 33 - Transformer Network Intuition.mp410.27MB

Sequence Models/nlp-sequence-models - 34 - Self-Attention.mp423.56MB

Sequence Models/nlp-sequence-models - 35 - Multi-Head Attention.mp415.19MB

Sequence Models/nlp-sequence-models - 36 - Transformer Network.mp421.16MB

Sequence Models/nlp-sequence-models - 37 - Conclusion and Thank You!.mp45.2MB

Structuring Machine Learning Projects/machine-learning-projects - 01 - Why ML Strategy.mp48.25MB

Structuring Machine Learning Projects/machine-learning-projects - 02 - Orthogonalization.mp420.22MB

Structuring Machine Learning Projects/machine-learning-projects - 03 - Single Number Evaluation Metric.mp413.59MB

Structuring Machine Learning Projects/machine-learning-projects - 04 - Satisficing and Optimizing Metric.mp412.45MB

Structuring Machine Learning Projects/machine-learning-projects - 05 - Train Dev Test Distributions.mp410.96MB

Structuring Machine Learning Projects/machine-learning-projects - 06 - Size of the Dev and Test Sets.mp411.27MB

Structuring Machine Learning Projects/machine-learning-projects - 07 - When to Change Dev Test Sets and Metrics.mp419.99MB

Structuring Machine Learning Projects/machine-learning-projects - 08 - Why Human-level Performance.mp410.92MB

Structuring Machine Learning Projects/machine-learning-projects - 09 - Avoidable Bias.mp411.21MB

Structuring Machine Learning Projects/machine-learning-projects - 10 - Understanding Human-level Performance.mp418.86MB

Structuring Machine Learning Projects/machine-learning-projects - 11 - Surpassing Human-level Performance.mp410.41MB

Structuring Machine Learning Projects/machine-learning-projects - 12 - Improving your Model Performance.mp49.19MB

Structuring Machine Learning Projects/machine-learning-projects - 13 - Andrej Karpathy Interview.mp484.01MB

Structuring Machine Learning Projects/machine-learning-projects - 14 - Carrying Out Error Analysis.mp419MB

Structuring Machine Learning Projects/machine-learning-projects - 15 - Cleaning Up Incorrectly Labeled Data.mp426.64MB

Structuring Machine Learning Projects/machine-learning-projects - 16 - Build your First System Quickly, then Iterate.mp412.17MB

Structuring Machine Learning Projects/machine-learning-projects - 17 - Training and Testing on Different Distributions.mp418.86MB

Structuring Machine Learning Projects/machine-learning-projects - 18 - Bias and Variance with Mismatched Data Distributions.mp428MB

Structuring Machine Learning Projects/machine-learning-projects - 19 - Addressing Data Mismatch.mp417.95MB

Structuring Machine Learning Projects/machine-learning-projects - 20 - Transfer Learning.mp422.18MB

Structuring Machine Learning Projects/machine-learning-projects - 21 - Multi-task Learning.mp429MB

Structuring Machine Learning Projects/machine-learning-projects - 22 - What is End-to-end Deep Learning.mp419.04MB

Structuring Machine Learning Projects/machine-learning-projects - 23 - Whether to use End-to-end Deep Learning.mp417.56MB

Structuring Machine Learning Projects/machine-learning-projects - 24 - Ruslan Salakhutdinov Interview.mp4103.53MB