Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL GANs VAEs deep RL unsupervised learning object detection and segmentation and more 2nd Edition review ✓ 3

summary Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition

Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition review ✓ 3 å ➜ [KINDLE] ❆ AdBook features hands on projects that show you how to createeffective AI with the most up to date techniuesStarting with an overview of multi layer perceptrons MLPs convolutional neural networks CNNs and recurrent neural networks RNNs the book then introducescutting edge techniues as you explore deep neural network architectures including ResNet and Deep Learning with TensorFlow 2 ePUB #203 DenseNet and how to create autoencoders You will then learn about GANs and how they can unlock new levels of AI performanceNext you'll discover how a variational autoencoder VAE is implemented and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans You'll also learn to implement DRL such as DeepLearning and Policy Gradient Methods which are critical to many modern results in AI What you will learn Use mutual information maximization techniues to perform unsupervised learning Use segmentation to identify the pixel wise class of each object in an im.

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Learning with Epub #225 Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow and Keras Key Features Explore the most advanced deep learning techniues that drive modern AI results New coverage of Deep Learning with TensorFlow 2 ePUB #203 unsupervised deep learning using mutual information object detection and semantic segmentation Completely updated for TensorFlow x Book Description Advanced Deep Learning with TensorFlow and Keras Advanced Deep EpubSecond Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniues available today Revised for TensorFlow x this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information object detection SSD and semantic segmentation FCN and PSPNet further allowing you to create your own cutting edge AI Deep Learning with Kindle #207 projectsUsing Keras as an open source deep learning library the.

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Advanced Deep Learning with TensorFlow 2 and Keras Apply DL GANs VAEs deep RL unsupervised learning object detection and segmentation and more 2nd EditionAge Identify both the bounding box and class of objects in an image using object detection Learn the building blocks for advanced techniues MLPss CNN and RNNs Understand deep neural networks including ResNet and DenseNet Understand and build autoregressive models – autoencoders VAEs and GANs Discover and implement deep reinforcement learning methods Who this book is for This is not an introductory book so fluency with Python is reuired The reader should also be familiar with some machine learning approaches and practical experience with DL will also be helpful Knowledge of Keras or TensorFlow is not reuired but is recommendedTable of Contents Introducing Advanced Deep Learning with Keras Deep Neural Networks Autoencoders Generative Adversarial Networks GANs Improved GANs Disentangled Representation GANs Cross Domain GANs Variational Autoencoders VAEs Deep Reinforcement Learning Policy Gradient Methods Object Detection Semantic Segmentation Unsupervised Learning Using Mutual Informatio.