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Predicting output When a net is trained, it can be course be used for predictions. In Keras, this is very simple. The process can be described as a way of progressively correcting mistakes as soon as they are detected.
Let's see how this works. Remember that each neural network layer has an associated set of weights that determines the output values for a given set of inputs. In addition to that, remember that a neural network can have multiple hidden layers. In the beginning, all the weights have some random assignment. Then the net is activated for each input in the training set: values are propagated forward from the input stage through the hidden stages to the output stage where a prediction is made note that we have kept the following diagram simple by only representing a few values with green dotted lines, but in reality, all the values are propagated forward through the network : Since we know the true observed value in the training set, it is possible to calculate the error made in prediction.
The key intuition for backtracking is to propagate the error back and use an appropriate optimizer algorithm, such as a gradient descent, to adjust the neural network weights with the goal of reducing the error again for the sake of simplicity, only a few error values are represented : The process of forward propagation from input to output and backward propagation of errors is repeated several times until the error gets below a predefined threshold.
The model is updated in such a way that the loss function is progressively minimized. In a neural network, what really matters is not the output of a single neuron but the collective weights adjusted in each layer. Therefore, the network progressively adjusts its internal weights in such a way that the prediction increases the number of labels correctly forecasted.
Of course, using the right set features and having a quality labeled data is fundamental to minimizing the bias during the learning process. If we want to have more improvements, we definitely need a new idea. What are we missing? Think about it. The fundamental intuition is that, so far, we lost all the information related to the local spatiality of the images. Remember that our vision is based on multiple cortex levels, each one recognizing more and more structured information, still preserving the locality.
First we see single pixels, then from that, we recognize simple geometric forms and then more and more sophisticated elements such as objects, faces, human bodies, animals and so on. In Chapter 3, Deep Learning with ConvNets, we will see that a particular type of deep learning network known as convolutional neural network CNN has been developed by taking into account both the idea of preserving the spatial locality in images and, more generally, in any type of information and the idea of learning via progressive levels of abstraction: with one layer, you can only learn simple patterns; with more than one layer, you can learn multiple patterns.
Before discussing CNN, we need to discuss some aspects of Keras architecture and have a practical introduction to a few additional machine learning concepts. This will be the topic of the next chapters. Summary In this chapter, you learned the basics of neural networks, more specifically, what a perceptron is, what a multilayer perceptron is, how to define neural networks in Keras, how to progressively improve metrics once a good baseline is established, and how to fine-tune the hyperparameter's space.
In addition to that, you now also have an intuitive idea of what some useful activation functions sigmoid and ReLU are, and how to train a network with backpropagation algorithms based on either gradient descent, on stochastic gradient descent, or on more sophisticated approaches, such as Adam and RMSprop. In addition to that, we will provide an overview of Keras APIs. Keras Installation and API In the previous chapter, we discussed the basic principles of neural networks and provided a few examples of nets that are able to recognize MNIST handwritten numbers.
This chapter explains how to install Keras, Theano, and TensorFlow. Step by step, we will look at how to get the environment working and move from intuition to working nets in very little time.
In addition to that, we will present an overview of Keras APIs, and some commonly useful operations such as loading and saving neural networks' architectures and weights, early stopping, history saving, checkpointing, and interactions with TensorBoard and Quiver.
Let us start. Step 1 — install some useful dependencies First, we install the numpy package, which provides support for large, multidimensional arrays and matrices as well as high-level mathematical functions. Then we install scipy, a library used for scientific computation. After that, it might be appropriate to install scikit-learn, a package considered the Python Swiss army knife for machine learning.
In this case, we will use it for data exploration. Optionally, it could be useful to install pillow, a library useful for image processing, and h5py, a library useful for data serialization used by Keras for model saving. A single command line is enough for installing what is needed. Alternatively, one can install Anaconda Python, which will automatically install numpy, scipy, scikit-learn, h5py, pillow, and a lot of other libraries that are needed for scientific computing for more information, refer to: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, by S.
Ioffe and C. Szegedy, arXiv. Again, we simply use pip for installing the correct package, as shown in the following screenshot. First let's look at how to define the sigmoid function in Theano. As you see, it is very simple; we just write the mathematical formula and compute the function element- wise on a matrix. Just run the Python Shell and write the code as shown in the following screenshot to get the result: So, Theano works.
Let's load it with a vi session. A convenient solution is to use a predefined Docker image for deep learning created by the community that contains all the popular DL frameworks TensorFlow, Theano, Torch, Caffe, and so on.
As of January , it supports the following: TensorFlow 0. Script Bundle". This directory is added to sys. Therefore, if your zip file contains a Python file mymodule. Francois Chollet, the author of Keras, says: The library was developed with a focus on enabling fast experimentation.
Being able to go from idea to result with the least possible delay is key to doing good research. In details: Modularity: A model is either a sequence or a graph of standalone modules that can be combined together like LEGO blocks for building neural networks. Namely, the library predefines a very large number of modules implementing different types of neural layers, cost functions, optimizers, initialization schemes, activation functions, and regularization schemes.
Minimalism: The library is implemented in Python and each module is kept short and self- describing. Easy extensibility: The library can be extended with new functionalities, as we will describe in Chapter 7, Additional Deep Learning Models. Getting started with Keras architecture In this section, we review the most important Keras components used for defining neural networks. First, we define what a tensor is, then we discuss different ways of composing predefined modules, and we conclude with an overview of the ones most commonly used.
What is a tensor? Keras uses either Theano or TensorFlow to perform very efficient computations on tensors. But what is a tensor anyway? A tensor is nothing but a multidimensional array or matrix. Both the backends are capable of efficient symbolic computations on tensors, which are the fundamental building blocks for creating neural networks.
Composing models in Keras There are two ways of composing models in Keras. They are as follows: Sequential composition Functional composition Let us take a look at each one in detail.
Sequential composition The first one is the sequential composition, where different predefined models are stacked together in a linear pipeline of layers similar to a stack or a queue. In Chapter 1, Neural Networks Foundations, we saw a few examples of sequential pipelines. An overview of predefined neural network layers Keras has a number of prebuilt layers. Let us review the most commonly used ones and highlight in which chapter these layers are mostly used. Regular dense A dense model is a fully connected neural network layer.
We have already seen examples of usage in Chapter 1, Neural Networks Foundations. Here is the prototype with a definition of the parameters: keras. Such inputs could be a text, a speech, time series, and anything else where the occurrence of an element in the sequence is dependent on the elements that appeared before it.
Here you can see some prototypes with a definition of the parameters: keras. This learning via progressive abstraction resembles vision models that have evolved over millions of years inside the human brain.
Here are some prototypes with a definition of the parameters: keras. Multiple layers have parameters for regularization. We have seen a few examples of activation functions in Chapter 1, Neural Networks Foundations, and more examples will be presented in the next chapters.
Error loss, which measures the difference between the values predicted and the values actually observed. There are multiple choices: mse mean square error between predicted and target values , rmse root square error between predicted and target values , mae mean absolute error between predicted and target values , mape mean percentage error between predicted and target values , and msle mean squared logarithmic error between predicted and target values.
Hinge loss, which is generally used for training classifiers. There are two versions: hinge defined as and squared hinge defined as the the squared value of the hinge loss. Class loss is used to calculate the cross-entropy for classification problems.
We have seen a few examples of objective functions in Chapter 1, Neural Networks Foundations, and more examples will be presented in the next chapters. The only difference is that the results from evaluating a metric are not used when training the model. We have seen a few examples of metrics in Chapter 1, Neural Networks Foundations, and more examples will be presented in the next chapters.
Some useful operations Here we report some utility operations that can be carried out with Keras APIs. The goal is to facilitate the creation of networks, the training process, and the saving of intermediate results. This is useful during training of deep learning models, which can often be a time-consuming task.
The state of a deep learning model at any point in time is the weights of the model at that time. Using TensorBoard and Keras Keras provides a callback for saving your training and test metrics, as well as activation histograms for the different layers in your model: keras.
In the next chapter, we will introduce the concept of convolutional networks a fundamental innovation in deep learning which has been used with success in multiple domains from text, to video, to speech going well beyond the initial image processing domain where they were originally conceived. Deep Learning with ConvNets In previous chapters, we discussed dense nets, in which each layer is fully connected to the adjacent layers.
In that context, each pixel in the input image is assigned to a neuron for a total of 28 x 28 pixels input neurons. However, this strategy does not leverage the spatial structure and relations of each image. These nets use an ad hoc architecture inspired by biological data taken from physiological experiments done on the visual cortex.
As discussed, our vision is based on multiple cortex levels, each one recognizing more and more structured information. First, we see single pixels; then from them, we recognize simple geometric forms. And then Convolutional neural networks are indeed fascinating. Over a short period of time, they become a disruptive technology, breaking all the state-of-the-art results in multiple domains, from text, to video, to speech going well beyond the initial image processing domain where they were originally conceived.
Two different types of layers, convolutional and pooling, are typically alternated. The depth of each filter increases from left to right in the network. The last stage is typically made of one or more fully connected layers: There are three key intuitions beyond ConvNets: Local receptive fields Shared weights Pooling Let's review them. Local receptive fields If we want to preserve spatial information, then it is convenient to represent each image with a matrix of pixels.
Then, a simple way to encode the local structure is to connect a submatrix of adjacent input neurons into one single hidden neuron belonging to the next layer.
That single hidden neuron represents one local receptive field. Note that this operation is named convolution and it gives the name to this type of network. Of course, we can encode more information by having overlapping submatrices. For instance, let's suppose that the size of each single submatrix is 5 x 5 and that those submatrices are used with MNIST images of 28 x 28 pixels. Then we will be able to generate 23 x 23 local receptive field neurons in the next hidden layer.
In fact it is possible to slide the submatrices by only 23 positions before touching the borders of the images. In Keras, the size of each single submatrix is called stride length, and this is a hyperparameter that can be fine-tuned during the construction of our nets. Let's define the feature map from one layer to another layer.
Of course, we can have multiple feature maps that learn independently from each hidden layer. For instance, we can start with 28 x 28 input neurons for processing MINST images and then recall k feature maps of size 23 x 23 neurons each again with a stride of 5 x 5 in the next hidden layer. Shared weights and bias Let's suppose that we want to move away from the pixel representation in a row by gaining the ability to detect the same feature independently from the location where it is placed in the input image.
A simple intuition is to use the same set of weights and bias for all the neurons in the hidden layers. In this way, each layer will learn a set of position-independent latent features derived from the image. Assuming that the input image has shape , on three channels with tf TensorFlow ordering, this is represented as , , 3. Note that with th Theano mode, the channel's dimension the depth is at index 1; in tf TensoFlow mode, it is at index 3. Again, we can use the spatial contiguity of the output produced from a single feature map and aggregate the values of a submatrix into a single output value that synthetically describes the meaning associated with that physical region.
Max-pooling One easy and common choice is max-pooling, which simply outputs the maximum activation as observed in the region. In Keras, if we want to define a max-pooling layer of size 2 x 2, we will write: model. In short, all pooling operations are nothing more than a summary operation on a given region. ConvNets summary So far, we have described the basic concepts of ConvNets. CNNs apply convolution and pooling operations in one dimension for audio and text data along the time dimension, in two dimensions for images along the height x width dimensions, and in three dimensions for videos along the height x width x time dimensions.
For images, sliding the filter over input volume produces a map that gives the responses of the filter for each spatial position. In other words, a ConvNet has multiple filters stacked together which learn to recognize specific visual features independently of the location in the image.
Those visual features are simple in the initial layers of the network, and then more and more sophisticated deeper in the network. LeCun and Y. Bengio, brain theory neural networks, vol. The key intuition here is to have low-layers alternating convolution operations with max-pooling operations.
The convolution operations are based on carefully chosen local receptive fields with shared weights for multiple feature maps. Then, higher levels are fully connected layers based on a traditional MLP with hidden layers and softmax as the output layer. In addition, we use a MaxPooling2D module: keras. Now, let us review the code. First we import a number of modules: from keras import backend as K from keras. Our net will learn 20 convolutional filters, each one of which has a size of 5 x 5.
The output dimension is the same one of the input shape, so it will be 28 x In this case, we increase the number of convolutional filters learned to 50 from the previous Increasing the number of filters in deeper layers is a common technique used in deep learning: model. Let's see how it looks visually: Now we need some additional code for training the network, but this is very similar to what we have already described in Chapter 1, Neural Network Foundations.
However, the accuracy has reached a new peak at For instance, there are many ways in which humans write a 9, one of them appearing in the following diagram. The same holds for 3, 7, 4, and 5. The number 1 in this diagram is so difficult to recognize that probably even a human will have issues with it: We can summarize all the progress made so far with our different models in the following graph.
Our simple net started with an accuracy of One way to do this is to split the training set of 50, examples into two different sets: The proper training set used for training our model will progressively reduce its size of 5,, 3,, 1,, , and examples The validation set used to estimate how well our model has been trained will consist of the remaining examples Our test set is always fixed and it consists of 10, examples.
With this setup, we compare the just-defined deep learning ConvNet against the first example of neural network defined in Chapter 1, Neural Network Foundations.
As we can see in the following graph, our deep network always outperforms the simple network and the gap is more and more evident when the number of examples provided for training is progressively reduced. With 5, training examples the deep learning net had an accuracy of More important, with only training examples our deep learning net still has an accuracy of All the experiments are run for only four training iterations.
This confirms the breakthrough progress achieved with deep learning. At first glance this could be surprising from a mathematical point of view because the deep network has many more unknowns the weights , so one would think we need many more data points. As of January, , the best result has an error rate of 0. Each class contains 6, images. The training set contains 50, images, while the test sets provides 10, images. Let us define a suitable deep net. First of all we import a number of useful modules, define a few constants, and load the dataset: from keras.
The output dimension is the same one of the input shape, so it will be 32 x 32 and activation is ReLU, which is a simple way of introducing non-linearity. In this case, we split the data and compute a validation set in addition to the training and testing sets.
The training is used to build our models, the validation is used to select the best performing approach, while the test set is to check the performance of our best models on fresh unseen data: train model. Our network reaches a test accuracy of We also print the accuracy and loss plot, and dump the network with model. All the activation functions are ReLU. You have defined a deeper network.
Let us run the code! First we dump the network, then we run for 40 iterations reaching an accuracy of Let us see the code: from keras. Using the same ConvNet defined previously we simply generate more augmented images and then we train. For efficiency, the generator runs in parallel to the model. Here is the code: fit the dataget datagen.
So let us run for 50 iterations only and see that we reach an accuracy of As of January, , the best result has an accuracy of Since we saved the model and the weights, we do not need to train every time: import numpy as np import scipy. Simonyan and A. Zisserman, The paper shows that, a significant improvement on the prior-art configurations can be achieved by pushing the depth to weight layers.
One model in the paper denoted as D or VGG has 16 deep layers. Each image is x on three channels. The model achieves 7. Test images will be presented with no initial annotation—no segmentation or labels—and algorithms will have to produce labelings specifying what objects are present in the images.
Using built-in code is very easy: from keras. If we run the code, we get result , which is the image net code for steaming train.
Recycling pre-built deep learning models for extracting features One very simple idea is to use VGG and, more generally, DCNN, for feature extraction. This code implements the idea by extracting features from a specific layer: from keras.
The key intuition is that, as the network learns to classify images into categories, each layer learns to identify the features that are necessary to do the final classification. Lower layers identify lower order features such as color and edges, and higher layers compose these lower order feature into higher order features such as shapes or objects. Hence the intermediate layer has the capability to extract important features from an image, and these features are more likely to help in different kinds of classification.
This has multiple advantages. First, we can rely on publicly available large-scale training and transfer this learning to novel domains. Second, we can save time for expensive large training.
Third, we can provide reasonable solutions even when we don't have a large number of training examples for our domain. We also get a good starting network shape for the task at hand, instead of guessing it. Very deep inception-v3 net used for transfer learning Transfer learning is a very powerful deep learning technique which has more applications in different domains.
The intuition is very simple and can be explained with an analogy. Suppose you want to learn a new language, say Spanish; then it could be useful to start from what you already know in a different language, say English. Following this line of thinking, computer vision researchers now commonly use pre-trained CNNs to generate representations for novel tasks, where the dataset may not be large enough to train an entire CNN from scratch. Another common tactic is to take the pre-trained ImageNet network and then to fine-tune the entire network to the novel task.
Inception-v3 net is a very deep ConvNet developed by Google. Keras implements the full network described in the following diagram and it comes pre-trained on ImageNet. We suppose to have a training dataset D in a domain, different from ImageNet.
D has 1, features in input and categories in output. Let us see a code fragment: from keras. The top level is a dense layer with 1, inputs and where the last output level is a softmax dense layer with classes of output. Then we freeze the top layers in inception and fine-tune some inception layer.
In this example, we decide to freeze the first layers an hyperparameter to tune : we chose to train the top 2 inception blocks, that is, we will freeze the first layers and unfreeze the rest: for layer in model.
We need to recompile the model for these modifications to take effect: we use SGD with a low learning rate from keras. Of course, there are many parameters to fine-tune for achieving good accuracy. However, we are now reusing a very large pre-trained network as a starting point via transfer learning. In doing so, we can save the need to train on our machines by reusing what is already available in Keras.
Then we used the CIFAR 10 dataset to build a deep learning classifier in 10 categories, and the ImageNet datasets to build an accurate classifier in 1, categories. In addition, we investigated how to use large deep learning networks such as VGG16 and very deep networks such as InceptionV3. The chapter concluded with a discussion on transfer learning in order to adapt pre-built models trained on large datasets so that they can work well on a new domain. In the next chapter, we will introduce generative adversarial networks used to reproduce synthetic data that looks like data generated by humans; and we will present WaveNet, a deep neural network used for reproducing human voice and musical instruments with high quality.
GANs are able to learn how to reproduce synthetic data that looks real. For instance, computers can learn how to paint and create realistic images. WaveNet is a deep generative network proposed by Google DeepMind to teach computers how to reproduce human voices and musical instruments, both with impressive quality. In this chapter, we will cover cover the following topics: What is GAN? GANs train two neural nets simultaneously, as shown in the next diagram.
The generator G Z makes the forgery, and the discriminator D Y can judge how realistic the reproductions based on its observations of authentic pieces of arts and copies are. D Y takes an input, Y, for instance, an image and expresses a vote to judge how real the input is--in general, a value close to zero denotes real and a value close to one denotes forgery.
G Z takes an input from a random noise, Z, and trains itself to fool D into thinking that whatever G Z produces is real.
So, the goal of training the discriminator D Y is to maximize D Y for every image from the true data distribution, and to minimize D Y for every image not from the true data distribution. So, G and D play an opposite game; hence the name adversarial training. Note that we train G and D in an alternating manner, where each of their objectives is expressed as a loss function optimized via a gradient descent.
The generative model learns how to forge more successfully, and the discriminative model learns how to recognize forgery more successfully. The discriminator network usually a standard convolutional neural network tries to classify whether an input image is real or generated. The important new idea is to backpropagate through both the discriminator and the generator to adjust the generator's parameters in such a way that the generator can learn how to fool the the discriminator for an increasing number of situations.
At the end, the generator will learn how to produce forged images that are indistinguishable from real ones: Of course, GANs require finding the equilibrium in a game with two players. For effective learning it is required that if a player successfully moves downhill in a round of updates, the same update must move the other player downhill too.
Think about it! If the forger learns how to fool the judge on every occasion, then the forger himself has nothing more to learn. Sometimes the two players eventually reach an equilibrium, but this is not always guaranteed and the two players can continue playing for a long time.
This means that it learns how to create new synthetic data, which is created by the network, that looks real and like it was created by humans. Here, a GAN has been used to synthesize forged images starting from a text description. The results are impressive. The first column is the real image in the test set, and the rest of the columns contain images generated from the same text description by Stage-I and Stage-II of StackGAN. Radford, L. Metz, and S. Chintala, arXiv: At the beginning, the generator creates nothing understandable, but after a few iterations, synthetic forged numbers are progressively clearer and clearer.
The results are virtually indistinguishable from the original: One of the coolest uses of GAN is arithmetic on faces in the generator's vector Z. The generator uses a dimensional, uniform distribution space, Z, which is then projected into a smaller space by a series of vis-a-vis convolution operations. However, it is possible to run it with Keras 2. The first dense layer takes a vector of dimensions as input and it produces 1, dimensions with the activation function tanh as the output.
We assume that the input is sampled from a uniform distribution in [-1, 1]. The next dense layer produces data of x 7 x 7 in the output using batch normalization for more information refer to Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, by S.
Szegedy, arXiv: Batch normalization has been empirically proven to accelerate the training in many situations, reduce the problems of poor initialization, and more generally produce more accurate results. After that, we have a convolutional layer producing 64 filters on 5 x 5 convolutional kernels with the activation tanh, followed by a new UpSampling and a final convolution with one filter, and on 5 x 5 convolutional kernels with the activation tanh.
Notice that this ConvNet has no pooling operations. This is followed by a max-pooling operation of size 2 x 2 and by a further convolution max-pooling operation.
The last two stages are dense, with the final one being the prediction for forgery, which consists of only one neuron with a sigmoid activation function. After 32 epochs, the generator learns to forge this set of handwritten numbers. No one has programmed the machine to write but it has learned how to write numbers that are indistinguishable from the ones written by humans. Note that training GANs could be very difficult because it is necessary to find the equilibrium between two players.
Since Keras just recently moved to 2. If the generator G and the discriminator D are based on the same model, M, then they can be combined into an adversarial model; it uses the same input, M, but separates targets and metrics for G and D. Note that the code uses the syntax of Keras 1. The code for legacy. First, the open source example imports a number of modules. We have seen all of them previously, with the exception of LeakyReLU, a special version of ReLU that allows a small gradient when the unit is not active.
Xu, N. Wang, T. Chen, and M. Li, arXiv However, in this case, we use the functional syntax—each module in our pipeline is simply passed as input to the following module. This initialization uses Gaussian noise scaled by the sum of the inputs plus outputs from the node. The same kind of initialization is used for all of the other modules. DataFrame history. Again, note that it uses the syntax of Keras 1. This particular net is the result of many fine-tuning experiments, but it is still essentially a sequence of convolution 2D and upsampling operations, which uses a Dense module at the beginning and a sigmoid at the end.
Again, we have a sequence of convolution 2D operations, and in this case we adopt SpatialDropout2D, which drops entire 2D feature maps instead of individual elements.
If the backend is TensorFlow, then the loss information is saved into a TensorBoard to check how the loss decreases over time. GridSpec n, m gs1. The results are truly impressive, and you can find online examples of synthetic voices where the computer learns how to talk with the voices of celebrities such as Matt Damon. So, you might wonder why learning to synthesize audio is so difficult. Well, each digital sound we hear is based on 16, samples per second sometimes, 48, or more , and building a predictive model where we learn to reproduce a sample based on all the previous ones is a very difficult challenge.
What is even cooler is that DeepMind proved that WaveNet can also be used to teach computers how to generate the sound of musical instruments such as piano music. Now it's time for some definitions. TTS systems are typically divided into two different classes: Concatenative TTS: This is where single speech voice fragments are first memorized and then recombined when the voice has to be reproduced.
However, this approach does not scale because it is only possible to reproduce the memorized voice fragments, and it is not possible to reproduce new speakers or different types of audio without memorizing the fragments from the beginning.
Parametric TTS: This is where a model is created for storing all the characteristic features of the audio to be synthesized. WaveNet improved the state-of-the-art by modeling directly the production of audio sounds, instead of using intermediate signal processing algorithms that have been used in the past.
In principle, WaveNet can be seen as a stack of 1D convolutional layers we have seen 2D convolution for images in Chapter 3, Deep Learning with ConvNets , with a constant stride of one and with no pooling layers.
Note that the input and the output have by construction the same dimension, so ConvNet is well-suited to model sequential data such as audio. However, it has been shown that in order to reach a large size for the receptive field remember that the receptive field of a neuron in a layer is the cross section of the previous layer from which neurons provide inputs in the output neuron it is necessary to either use a massive number of large filters or prohibitively increase the the depth of the network.
For this reason, pure ConvNets are not so effective in learning how to synthesize audio. As an example, in one dimension, a filter, w, of size 3 with dilatation 1 would compute the following sum: Thanks to this simple idea of introducing holes, it is possible to stack multiple dilated convolutional layers with exponentially increasing filters, and learn long range input dependencies without having an excessively deep network.
Downloading the example code for this book. Skip to main content. Start your free trial. Book Description Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Who This Book Is For If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras.
What You Will Learn Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm Fine-tune a neural network to improve the quality of results Use deep learning for image and audio processing Use Recursive Neural Tensor Networks RNTNs to outperform standard word embedding in special cases Identify problems for which Recurrent Neural Network RNN solutions are suitable Explore the process required to implement Autoencoders Evolve a deep neural network using reinforcement learning In Detail This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks.
Style and approach This book is an easy-to-follow guide full of examples and real-world applications to help you gain an in-depth understanding of Keras. Show and hide more. Preview Download. Load more similar PDF files. PDF Drive investigated dozens of problems and listed the biggest global issues facing the world today. Let's Change The World Together. Pdfdrive:hope Give books away.As of today we have 83, eBooks for you to download for free. No annoying ads, no download limitsenjoy it and don't forget to bookmark and share the love! Can't find what you're looking for? Try pdfdrive:hope to deep learning with keras pdf free download a book. Previous 1 2 3 4 5 6 … 20 Next. Pdfdrive:hope Deep learning with keras pdf free download books away. Get books you want. Ask yourself: What is one failure leraning you have turned into your greatest lesson? Nick has also authored TensorFlow Machine Learning Cookbook by Packt We award our regular reviewers with free eBooks and videos in exchange for their Keras example — deep dreaming Keras example — style transfer Summary 8. Artificial intelligence; Machine Learning; Learning representations from data; The "deep" in deep learning; As of today we have 83,, eBooks for you to download for free. Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning. Get started with a FREE account. Preview Download. “ Keep your face always toward the sunshine - and shadows will fall behind you. Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python. Explore a preview version of Deep Learning with Keras right now. Start your free trial Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games; See how various Download Example Code. Learn Keras for Deep Neural Networks PDF all Free Download. 1 Learn Keras for Deep Neural Networks Pdf for Free Download. Learn Keras for Deep Neural. Deep Learning with TensorFlow 2 and Keras PDF Free Download, Reviews, Read Online, ISBN: , By. impotenzberatung.com‐learning‐with‐mxnet‐dmitry‐larko. • TensorFlow – Introduction to Loss functions and Optimizers in Keras  AlexNet, impotenzberatung.com‐imagenet‐classification‐with‐deep‐convolutional‐neural‐impotenzberatung.com Transfer the style of painting from one image to other. Working knowledge of Python programming and basic statistics is a must to help you grasp the concepts in the book. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more! What you will learn Understand how to use machine learning algorithms for regression and classification problems Implement ensemble techniques such as averaging, weighted averaging, and max-voting Get to grips with advanced ensemble methods, such as bootstrapping, bagging, and stacking Use Random Forest for tasks such as classification and regression Implement an ensemble of homogeneous and heterogeneous machine learning algorithms Learn and implement various boosting techniques, such as AdaBoost, Gradient Boosting Machine, and XGBoost Who this book is for This book is designed for data scientists, machine learning developers, and deep learning enthusiasts who want to delve into machine learning algorithms to build powerful ensemble models. Learn how restricted Boltzmann Machines can be used to recommend movies. By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter. This deep learning book will also guide you through performing anomaly detection on unsupervised data and help you set up neural networks in distributed systems effectively. Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game. Facebook Twitter WhatsApp Telegram. FCU July 13, 0. This gives students an incomplete knowledge of the subject. Adblock Detected Please consider supporting us by disabling your ad blocker.