├── Chapter01 ├── Chapter 1 - Building efficient models with MXNet.ipynb ├── Chapter 1 - Building state-of-the-art, production-ready models with TensorFlow.ipynb ├── Chapter 1 - Defining networks using simple and efficient code with Gluon.ipynb ├── Chapter 1 - Implementing high-performance models with CNTK.ipynb ├── Chapter 1 - Intuitively building networks with Keras .ipynb └── Chapter 1 - Using PyTorchGÇÖs dynamic computation graphs for RNNs.ipynb ├── Chapter02 ├── Chapter 2 - Adding dropout to prevent overfitting.ipynb ├── Chapter 2 - Building a multi-layer neural network.ipynb ├── Chapter 2 - Experiment with hidden layers and hidden units.ipynb ├── Chapter 2 - Experimenting with different optimizers.ipynb ├── Chapter 2 - Getting started with activation functions.ipynb ├── Chapter 2 - Implementing a single-layer neural network.ipynb ├── Chapter 2 - Implementing an autoencoder.ipynb ├── Chapter 2 - Improving generalization with regularization.ipynb ├── Chapter 2 - Tuning the loss function.ipynb └── Chapter 2 - Understanding the Perceptron.ipynb ├── Chapter03 ├── Chapter 3 - Applying a 1D CNN to text.ipynb ├── Chapter 3 - Applying pooling layers.ipynb ├── Chapter 3 - Experimenting with different types of initialization.ipynb ├── Chapter 3 - Getting started with filters and parameter sharing.ipynb ├── Chapter 3 - Implementing a convolutional autencoder.ipynb ├── Chapter 3 - Optimizing with batch normalization.ipynb └── Chapter 3 - Understanding padding and strides.ipynb ├── Chapter04 ├── Chapter 4 - Adding Long Short-Term Memory (LSTM).ipynb ├── Chapter 4 - Adding attention.ipynb ├── Chapter 4 - Character-level text generation.ipynb ├── Chapter 4 - Implementing a simple RNN.ipynb ├── Chapter 4 - Implementing bidirectional RNNs.ipynb └── Chapter 4 - Using gated recurrent units (GRUs).ipynb ├── Chapter05 ├── Chapter 5 - Implementing a deep Q-learning algorithm.ipynb └── Chapter 5 - Implementing policy gradients.ipynb ├── Chapter06 ├── Chapter 6 - Implementing Deep Convolutional GANs (DCGANs) .ipynb ├── Chapter 6 - Understanding GANs.ipynb └── Chapter 6 - Upscaling the resolution of images with Super-Resolution GANs (SRGANs).ipynb ├── Chapter07 ├── Chapter 7 - Augmenting images with computer vision techniques.ipynb ├── Chapter 7 - Classifying objects in images.ipynb ├── Chapter 7 - Finding facial key points.ipynb ├── Chapter 7 - Localizing an object in images.ipynb ├── Chapter 7 - Recognizing faces.ipynb ├── Chapter 7 - Scene understanding (semantic segmentation).ipynb ├── Chapter 7 - Segmenting classes in images with U-net.ipynb └── Chapter 7 - Transferring styles to images.ipynb ├── Chapter08 ├── Chapter 8 - Analyzing sentiment.ipynb ├── Chapter 8 - Summarizing text.ipynb └── Chapter 8 - Translating sentences.ipynb ├── Chapter09 ├── Chapter 9 - Identifying speakers with voice recognition.ipynb ├── Chapter 9 - Implementing a speech recognition pipeline from scratch.ipynb └── Chapter 9 - Understanding videos with deep learning.ipynb ├── Chapter10 ├── Chapter 10 - Predicting bike sharing demand.ipynb ├── Chapter 10 - Predicting stock prices with neural networks.ipynb └── Chapter 10 - Using a shallow neural network for binary classification.ipynb ├── Chapter11 ├── Chapter 11 - Genetic Algorithm (GA) to optimize hyperparameters.ipynb ├── Chapter 11 - Learning to drive a car with end-to-end learning.ipynb └── Chapter 11 - Learning to play games with deep reinforcement learning.ipynb ├── Chapter12 ├── Chapter 12 - Adding dropouts to prevent overfitting.ipynb ├── Chapter 12 - Comparing optimizers.ipynb ├── Chapter 12 - Learning rates and learning rate schedulers.ipynb ├── Chapter 12 - Making a model more robust with data augmentation.ipynb ├── Chapter 12 - Using grid search for parameter tuning.ipynb ├── Chapter 12 - Visualizing training with TensorBoard and Keras.ipynb └── Chapter 12 - Working with batches and mini-batches.ipynb ├── Chapter13 ├── Chapter 13 - Analyzing network weights and more.ipynb ├── Chapter 13 - Freezing layers.ipynb ├── Chapter 13 - Storing the network topology and trained weights.ipynb └── Chapter 13 - Visualizing training with TensorBoard.ipynb ├── Chapter14 ├── Chapter 14 - Extracting bottleneck features with ResNet.ipynb ├── Chapter 14 - Fine-tuning with Xception.ipynb ├── Chapter 14 - Large-scale visual recognition with GoogLeNet_Inception.ipynb └── Chapter 14 - Leveraging pretrained VGG models for new classes.ipynb ├── LICENSE └── README.md /Chapter01/Chapter 1 - Building efficient models with MXNet.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter01/Chapter 1 - Building efficient models with MXNet.ipynb -------------------------------------------------------------------------------- /Chapter01/Chapter 1 - Building state-of-the-art, production-ready models with TensorFlow.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter01/Chapter 1 - Building state-of-the-art, production-ready models with TensorFlow.ipynb -------------------------------------------------------------------------------- /Chapter01/Chapter 1 - Defining networks using simple and efficient code with Gluon.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter01/Chapter 1 - Defining networks using simple and efficient code with Gluon.ipynb -------------------------------------------------------------------------------- /Chapter01/Chapter 1 - Implementing high-performance models with CNTK.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter01/Chapter 1 - Implementing high-performance models with CNTK.ipynb -------------------------------------------------------------------------------- /Chapter01/Chapter 1 - Intuitively building networks with Keras .ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter01/Chapter 1 - Intuitively building networks with Keras .ipynb -------------------------------------------------------------------------------- /Chapter01/Chapter 1 - Using PyTorchGÇÖs dynamic computation graphs for RNNs.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter01/Chapter 1 - Using PyTorchGÇÖs dynamic computation graphs for RNNs.ipynb -------------------------------------------------------------------------------- /Chapter02/Chapter 2 - Adding dropout to prevent overfitting.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter02/Chapter 2 - Adding dropout to prevent overfitting.ipynb -------------------------------------------------------------------------------- /Chapter02/Chapter 2 - Building a multi-layer neural network.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter02/Chapter 2 - Building a multi-layer neural network.ipynb -------------------------------------------------------------------------------- /Chapter02/Chapter 2 - Experiment with hidden layers and hidden units.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter02/Chapter 2 - Experiment with hidden layers and hidden units.ipynb -------------------------------------------------------------------------------- /Chapter02/Chapter 2 - Experimenting with different optimizers.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter02/Chapter 2 - Experimenting with different optimizers.ipynb -------------------------------------------------------------------------------- /Chapter02/Chapter 2 - Getting started with activation functions.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter02/Chapter 2 - Getting started with activation functions.ipynb -------------------------------------------------------------------------------- /Chapter02/Chapter 2 - Implementing a single-layer neural network.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter02/Chapter 2 - Implementing a single-layer neural network.ipynb -------------------------------------------------------------------------------- /Chapter02/Chapter 2 - Implementing an autoencoder.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter02/Chapter 2 - Implementing an autoencoder.ipynb -------------------------------------------------------------------------------- /Chapter02/Chapter 2 - Improving generalization with regularization.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter02/Chapter 2 - Improving generalization with regularization.ipynb -------------------------------------------------------------------------------- /Chapter02/Chapter 2 - Tuning the loss function.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter02/Chapter 2 - Tuning the loss function.ipynb -------------------------------------------------------------------------------- /Chapter02/Chapter 2 - Understanding the Perceptron.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter02/Chapter 2 - Understanding the Perceptron.ipynb -------------------------------------------------------------------------------- /Chapter03/Chapter 3 - Applying a 1D CNN to text.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter03/Chapter 3 - Applying a 1D CNN to text.ipynb -------------------------------------------------------------------------------- /Chapter03/Chapter 3 - Applying pooling layers.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter03/Chapter 3 - Applying pooling layers.ipynb -------------------------------------------------------------------------------- /Chapter03/Chapter 3 - Experimenting with different types of initialization.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter03/Chapter 3 - Experimenting with different types of initialization.ipynb -------------------------------------------------------------------------------- /Chapter03/Chapter 3 - Getting started with filters and parameter sharing.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter03/Chapter 3 - Getting started with filters and parameter sharing.ipynb -------------------------------------------------------------------------------- /Chapter03/Chapter 3 - Implementing a convolutional autencoder.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter03/Chapter 3 - Implementing a convolutional autencoder.ipynb -------------------------------------------------------------------------------- /Chapter03/Chapter 3 - Optimizing with batch normalization.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter03/Chapter 3 - Optimizing with batch normalization.ipynb -------------------------------------------------------------------------------- /Chapter03/Chapter 3 - Understanding padding and strides.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter03/Chapter 3 - Understanding padding and strides.ipynb -------------------------------------------------------------------------------- /Chapter04/Chapter 4 - Adding Long Short-Term Memory (LSTM).ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter04/Chapter 4 - Adding Long Short-Term Memory (LSTM).ipynb -------------------------------------------------------------------------------- /Chapter04/Chapter 4 - Adding attention.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter04/Chapter 4 - Adding attention.ipynb -------------------------------------------------------------------------------- /Chapter04/Chapter 4 - Character-level text generation.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter04/Chapter 4 - Character-level text generation.ipynb -------------------------------------------------------------------------------- /Chapter04/Chapter 4 - Implementing a simple RNN.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter04/Chapter 4 - Implementing a simple RNN.ipynb -------------------------------------------------------------------------------- /Chapter04/Chapter 4 - Implementing bidirectional RNNs.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter04/Chapter 4 - Implementing bidirectional RNNs.ipynb -------------------------------------------------------------------------------- /Chapter04/Chapter 4 - Using gated recurrent units (GRUs).ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter04/Chapter 4 - Using gated recurrent units (GRUs).ipynb -------------------------------------------------------------------------------- /Chapter05/Chapter 5 - Implementing a deep Q-learning algorithm.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter05/Chapter 5 - Implementing a deep Q-learning algorithm.ipynb -------------------------------------------------------------------------------- /Chapter05/Chapter 5 - Implementing policy gradients.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter05/Chapter 5 - Implementing policy gradients.ipynb -------------------------------------------------------------------------------- /Chapter06/Chapter 6 - Implementing Deep Convolutional GANs (DCGANs) .ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter06/Chapter 6 - Implementing Deep Convolutional GANs (DCGANs) .ipynb -------------------------------------------------------------------------------- /Chapter06/Chapter 6 - Understanding GANs.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter06/Chapter 6 - Understanding GANs.ipynb -------------------------------------------------------------------------------- /Chapter06/Chapter 6 - Upscaling the resolution of images with Super-Resolution GANs (SRGANs).ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter06/Chapter 6 - Upscaling the resolution of images with Super-Resolution GANs (SRGANs).ipynb -------------------------------------------------------------------------------- /Chapter07/Chapter 7 - Augmenting images with computer vision techniques.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter07/Chapter 7 - Augmenting images with computer vision techniques.ipynb -------------------------------------------------------------------------------- /Chapter07/Chapter 7 - Classifying objects in images.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter07/Chapter 7 - Classifying objects in images.ipynb -------------------------------------------------------------------------------- /Chapter07/Chapter 7 - Finding facial key points.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter07/Chapter 7 - Finding facial key points.ipynb -------------------------------------------------------------------------------- /Chapter07/Chapter 7 - Localizing an object in images.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter07/Chapter 7 - Localizing an object in images.ipynb -------------------------------------------------------------------------------- /Chapter07/Chapter 7 - Recognizing faces.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter07/Chapter 7 - Recognizing faces.ipynb -------------------------------------------------------------------------------- /Chapter07/Chapter 7 - Scene understanding (semantic segmentation).ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter07/Chapter 7 - Scene understanding (semantic segmentation).ipynb -------------------------------------------------------------------------------- /Chapter07/Chapter 7 - Segmenting classes in images with U-net.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter07/Chapter 7 - Segmenting classes in images with U-net.ipynb -------------------------------------------------------------------------------- /Chapter07/Chapter 7 - Transferring styles to images.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter07/Chapter 7 - Transferring styles to images.ipynb -------------------------------------------------------------------------------- /Chapter08/Chapter 8 - Analyzing sentiment.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter08/Chapter 8 - Analyzing sentiment.ipynb -------------------------------------------------------------------------------- /Chapter08/Chapter 8 - Summarizing text.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter08/Chapter 8 - Summarizing text.ipynb -------------------------------------------------------------------------------- /Chapter08/Chapter 8 - Translating sentences.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter08/Chapter 8 - Translating sentences.ipynb -------------------------------------------------------------------------------- /Chapter09/Chapter 9 - Identifying speakers with voice recognition.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter09/Chapter 9 - Identifying speakers with voice recognition.ipynb -------------------------------------------------------------------------------- /Chapter09/Chapter 9 - Implementing a speech recognition pipeline from scratch.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter09/Chapter 9 - Implementing a speech recognition pipeline from scratch.ipynb -------------------------------------------------------------------------------- /Chapter09/Chapter 9 - Understanding videos with deep learning.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter09/Chapter 9 - Understanding videos with deep learning.ipynb -------------------------------------------------------------------------------- /Chapter10/Chapter 10 - Predicting bike sharing demand.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter10/Chapter 10 - Predicting bike sharing demand.ipynb -------------------------------------------------------------------------------- /Chapter10/Chapter 10 - Predicting stock prices with neural networks.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter10/Chapter 10 - Predicting stock prices with neural networks.ipynb -------------------------------------------------------------------------------- /Chapter10/Chapter 10 - Using a shallow neural network for binary classification.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter10/Chapter 10 - Using a shallow neural network for binary classification.ipynb -------------------------------------------------------------------------------- /Chapter11/Chapter 11 - Genetic Algorithm (GA) to optimize hyperparameters.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter11/Chapter 11 - Genetic Algorithm (GA) to optimize hyperparameters.ipynb -------------------------------------------------------------------------------- /Chapter11/Chapter 11 - Learning to drive a car with end-to-end learning.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter11/Chapter 11 - Learning to drive a car with end-to-end learning.ipynb -------------------------------------------------------------------------------- /Chapter11/Chapter 11 - Learning to play games with deep reinforcement learning.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter11/Chapter 11 - Learning to play games with deep reinforcement learning.ipynb -------------------------------------------------------------------------------- /Chapter12/Chapter 12 - Adding dropouts to prevent overfitting.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter12/Chapter 12 - Adding dropouts to prevent overfitting.ipynb -------------------------------------------------------------------------------- /Chapter12/Chapter 12 - Comparing optimizers.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter12/Chapter 12 - Comparing optimizers.ipynb -------------------------------------------------------------------------------- /Chapter12/Chapter 12 - Learning rates and learning rate schedulers.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter12/Chapter 12 - Learning rates and learning rate schedulers.ipynb -------------------------------------------------------------------------------- /Chapter12/Chapter 12 - Making a model more robust with data augmentation.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter12/Chapter 12 - Making a model more robust with data augmentation.ipynb -------------------------------------------------------------------------------- /Chapter12/Chapter 12 - Using grid search for parameter tuning.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter12/Chapter 12 - Using grid search for parameter tuning.ipynb -------------------------------------------------------------------------------- /Chapter12/Chapter 12 - Visualizing training with TensorBoard and Keras.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter12/Chapter 12 - Visualizing training with TensorBoard and Keras.ipynb -------------------------------------------------------------------------------- /Chapter12/Chapter 12 - Working with batches and mini-batches.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter12/Chapter 12 - Working with batches and mini-batches.ipynb -------------------------------------------------------------------------------- /Chapter13/Chapter 13 - Analyzing network weights and more.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter13/Chapter 13 - Analyzing network weights and more.ipynb -------------------------------------------------------------------------------- /Chapter13/Chapter 13 - Freezing layers.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter13/Chapter 13 - Freezing layers.ipynb -------------------------------------------------------------------------------- /Chapter13/Chapter 13 - Storing the network topology and trained weights.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter13/Chapter 13 - Storing the network topology and trained weights.ipynb -------------------------------------------------------------------------------- /Chapter13/Chapter 13 - Visualizing training with TensorBoard.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter13/Chapter 13 - Visualizing training with TensorBoard.ipynb -------------------------------------------------------------------------------- /Chapter14/Chapter 14 - Extracting bottleneck features with ResNet.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter14/Chapter 14 - Extracting bottleneck features with ResNet.ipynb -------------------------------------------------------------------------------- /Chapter14/Chapter 14 - Fine-tuning with Xception.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter14/Chapter 14 - Fine-tuning with Xception.ipynb -------------------------------------------------------------------------------- /Chapter14/Chapter 14 - Large-scale visual recognition with GoogLeNet_Inception.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter14/Chapter 14 - Large-scale visual recognition with GoogLeNet_Inception.ipynb -------------------------------------------------------------------------------- /Chapter14/Chapter 14 - Leveraging pretrained VGG models for new classes.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/Chapter14/Chapter 14 - Leveraging pretrained VGG models for new classes.ipynb -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/LICENSE -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Deep-Learning-Cookbook/HEAD/README.md --------------------------------------------------------------------------------