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-001. Lesson 15 Overview Deep Learning en.srt110142023-09-06 20:30:48
-001. Lesson 15 Overview Deep Learning.mp4609196542023-09-06 19:13:18
-002. Introduction en.srt119502023-09-06 20:30:49
-002. Introduction.mp4243433362023-09-06 19:13:21
-003. Deep Learning Applications en.srt51972023-09-06 20:30:50
-003. Deep Learning Applications.mp4120753342023-09-06 19:13:23
-004. Deep Learning Demos en.srt31852023-09-06 20:30:51
-004. Deep Learning Demos.mp482516192023-09-06 19:13:25
-005. Keras Resources en.srt26262023-09-06 20:30:52
-005. Keras Resources.mp468426902023-09-06 19:13:27
-006. Keras Built-In Datasets en.srt30932023-09-06 20:30:53
-006. Keras Built-In Datasets.mp468924142023-09-06 19:13:29
-007. Custom Anaconda Environments en.srt132592023-09-06 20:30:54
-007. Custom Anaconda Environments.mp4299737852023-09-06 19:13:32
-008. Neural Networks en.srt111322023-09-06 20:30:55
-008. Neural Networks.mp4222379362023-09-06 19:13:35
-009. Tensors en.srt69232023-09-06 20:30:56
-009. Tensors.mp4141520372023-09-06 19:13:38
-010. Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset en.txt41342023-09-06 20:30:57
-010. Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset.mp4122032462023-09-06 19:13:40
-011. Reproducibility in Keras and Deep Learning en.srt20712023-09-06 20:30:58
-011. Reproducibility in Keras and Deep Learning.mp451361782023-09-06 19:13:41
-012. Basic Keras Neural Network en.srt53432023-09-06 20:30:59
-012. Basic Keras Neural Network.mp4115507842023-09-06 19:13:43
-013. Loading the MNIST Dataset en.srt115732023-09-06 20:31:00
-013. Loading the MNIST Dataset.mp4281755842023-09-06 19:13:47
-014. Data Exploration en.srt25052023-09-06 20:31:01
-014. Data Exploration.mp440911042023-09-06 19:13:48
-015. Visualizing Digits en.srt107492023-09-06 20:31:01
-015. Visualizing Digits.mp4265274102023-09-06 19:13:52
-016. Reshaping the Image Data en.srt81262023-09-06 20:31:02
-016. Reshaping the Image Data.mp4182244992023-09-06 19:13:55
-017. Normalizing the Image Data en.srt41162023-09-06 20:31:03
-017. Normalizing the Image Data.mp497475232023-09-06 19:13:57
-018. One-Hot Encoding Converting the Labels From Integers to Categorical Data en.srt75962023-09-06 20:31:04
-018. One-Hot Encoding Converting the Labels From Integers to Categorical Data.mp4194606452023-09-06 19:13:59
-019. Creating the Neural Network en.srt21892023-09-06 20:31:05
-019. Creating the Neural Network.mp441569592023-09-06 19:14:01
-020. Adding Layers to the Network en.srt33382023-09-06 20:31:06
-020. Adding Layers to the Network.mp465326782023-09-06 19:14:03
-021. Convolution en.srt114472023-09-06 20:31:07
-021. Convolution.mp4271706902023-09-06 19:14:06
-022. Adding a Conv2D Convolution Layer to Our Model en.srt74722023-09-06 20:31:08
-022. Adding a Conv2D Convolution Layer to Our Model.mp4173187972023-09-06 19:14:09
-023. Dimensionality of the First Convolution Layer’s Output en.srt26382023-09-06 20:31:09
-023. Dimensionality of the First Convolution Layer’s Output.mp470621132023-09-06 19:14:11
-024. Overfitting en.srt47882023-09-06 20:31:10
-024. Overfitting.mp4124665242023-09-06 19:14:13
-025. Adding a Pooling Layer en.srt71312023-09-06 20:31:11
-025. Adding a Pooling Layer.mp4146454282023-09-06 19:14:15
-026. Adding Another Convolutional Layer and Pooling Layer en.srt48252023-09-06 20:31:12
-026. Adding Another Convolutional Layer and Pooling Layer.mp4138647652023-09-06 19:14:17
-027. Flattening the Results to One Dimension with a Keras Flatten Layer en.srt25122023-09-06 20:31:13
-027. Flattening the Results to One Dimension with a Keras Flatten Layer.mp454600222023-09-06 19:14:18
-028. Adding a Dense Layer to Reduce the Number of Features en.srt31642023-09-06 20:31:14
-028. Adding a Dense Layer to Reduce the Number of Features.mp496648372023-09-06 19:14:20
-029. Adding Another Dense Layer to Produce the Final Output en.srt22952023-09-06 20:31:15
-029. Adding Another Dense Layer to Produce the Final Output.mp454542092023-09-06 19:14:22
-030. Printing the Model's Summary en.srt63022023-09-06 20:31:16
-030. Printing the Model's Summary.mp4138985722023-09-06 19:14:24
-031. Visualizing a Model’s Structure en.srt55392023-09-06 20:31:16
-031. Visualizing a Model’s Structure.mp4109193302023-09-06 19:14:26
-032. Compiling the Model en.srt45392023-09-06 20:31:17
-032. Compiling the Model.mp489122082023-09-06 19:14:28
-033. Training and Evaluating the Model en.srt116512023-09-06 20:31:18
-033. Training and Evaluating the Model.mp4275713132023-09-06 19:14:31
-034. Evaluating the Model on Unseen Data en.srt30862023-09-06 20:31:19
-034. Evaluating the Model on Unseen Data.mp462950502023-09-06 19:14:33
-035. Making Predictions en.srt33802023-09-06 20:31:20
-035. Making Predictions.mp473381152023-09-06 19:14:34
-036. Locating the Incorrect Predictions en.srt54412023-09-06 20:31:21
-036. Locating the Incorrect Predictions.mp4123519332023-09-06 19:14:37
-037. Visualizing Incorrect Predictions en.srt65672023-09-06 20:31:22
-037. Visualizing Incorrect Predictions.mp4158249402023-09-06 19:14:39
-038. Displaying the Probabilities for Several Incorrect Predictions en.srt69762023-09-06 20:31:23
-039. Saving and Loading a Model en.srt35742023-09-06 20:31:24
-039. Saving and Loading a Model.mp477662912023-09-06 19:14:42
-040. Visualizing Neural Network Training with TensorBoard en.srt327822023-09-06 20:31:25
-040. Visualizing Neural Network Training with TensorBoard.mp4792109612023-09-06 19:14:50
-041. ConvnetJS Browser-Based Deep-Learning Training and Visualization en.srt71592023-09-06 20:31:26
-041. ConvnetJS Browser-Based Deep-Learning Training and Visualization.mp4208054522023-09-06 19:14:53
-042. Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset en.txt87532023-09-06 20:31:27
-042. Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset.mp4213545852023-09-06 19:14:56
-043. Loading the IMDb Movie Reviews Dataset en.srt77922023-09-06 20:31:28
-043. Loading the IMDb Movie Reviews Dataset.mp4204077702023-09-06 19:14:59
-044. Data Exploration en.srt40342023-09-06 20:31:29
-044. Data Exploration.mp488949532023-09-06 19:15:01
-045. Movie Review Encodings and Decoding a Review en.srt151472023-09-06 20:31:30
-045. Movie Review Encodings and Decoding a Review.mp4337699502023-09-06 19:15:05
-046. Data Preparation en.srt85592023-09-06 20:31:31
-046. Data Preparation.mp4186562632023-09-06 19:15:08
-047. Creating the Neural Network en.srt11982023-09-06 20:31:32
-047. Creating the Neural Network.mp421788512023-09-06 19:15:09
-048. Adding an Embedding Layer en.srt59562023-09-06 20:31:32
-048. Adding an Embedding Layer.mp4136327542023-09-06 19:15:12
-049. Adding an LSTM Layer en.srt50922023-09-06 20:31:33
-049. Adding an LSTM Layer.mp4155925062023-09-06 19:15:15
-050. Adding a Dense Output Layer en.srt10722023-09-06 20:31:34
-050. Adding a Dense Output Layer.mp424684142023-09-06 19:15:16
-051. Compiling the Model and Displaying the Summary en.srt35972023-09-06 20:31:35
-051. Compiling the Model and Displaying the Summary.mp482020472023-09-06 19:15:18
-052. Training and Evaluating the Model (1 of 2) en.srt61852023-09-06 20:31:36
-052. Training and Evaluating the Model (1 of 2).mp4189869352023-09-06 19:15:20
-053. Training and Evaluating the Model (2 of 2) en.srt34222023-09-06 20:31:37
-053. Training and Evaluating the Model (2 of 2).mp485708442023-09-06 19:15:22
-054. Tuning Deep Learning Models en.srt59642023-09-06 20:31:38
-054. Tuning Deep Learning Models.mp4136138682023-09-06 19:15:24

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