Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition
Revised for TensorFlow 2.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 ...
This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using ...
Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras
TensorFlow represents the data as tensors and the computation as graphs. This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x.
Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models
What you will learn Build machine learning models particularly focused on the MNIST digits Work with Docker and Keras to build an image classifier Understand natural language models to process text and images Prepare your dataset for ...
R Deep Learning Essentials: A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet, 2nd Edition
What you will learn Build shallow neural network prediction models Prevent models from overfitting the data to improve generalizability Explore techniques for finding the best hyperparameters for deep learning models Create NLP models using ...
Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras
This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks.
In-Depth Tutorials: Deep Learning Using Scikit-Learn, Keras, and TensorFlow with Python GUI
What you will learn Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration Explore computation graphs, data, and programming models also get an insight into an example of ...
Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras
This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks.
The Deep Learning Workshop: Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras
Develop Deep Learning Models on Theano and TensorFlow Using Keras Jason Brownlee. 6.2. Pima. Indians. Onset. of. Diabetes. Dataset. In this tutorial we are going to ... Pima Indians Onset of Diabetes Dataset 29 Pima Indians Onset of Diabetes ...
Keras 2.x Projects: 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras
... words to use: MaxWords = 10000 Let's create the Tokenizer: Tok = Tokenizer(num_words=MaxWords) Finally, we can perform the tokenization operation: XTrain = Tok.sequences_to_matrix(XTrain, mode='binary') XTest = Tok.sequences_to_matrix ...
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
... real time on a video, as seen in Redmon's demo. YOLOv3's architecture is ... Unified, Real-Time Object Detection,” Proceedings of the IEEE Conference on Computer Vision and ... Detector,”. Object Detection | 489 You Only Look Once (YOLO)
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Aurélien Géron. Using Pretrained Language Model Components The TensorFlow Hub library makes it easy to reuse pretrained model components in your own models, for text, image, audio, and more. These model components are called modules ...
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
... Sebastian Ruder demonstrated the effectiveness of unsupervised pretraining for NLP tasks: the authors trained an LSTM language model on a huge text corpus using self-supervised learning (i.e., generating the labels automatically from ...
GANs in Action: Deep learning with Generative Adversarial Networks
... tutorial, where we will build a variational autoencoder to generate handwritten digits—the same task we will be exploring in our GAN tutorials in later chapters. However, if you are already familiar with autoencoders or want to dive ...
Unsupervised Information Extraction by Text Segmentation
... IMDb4 (Previous Submissions), and from Wikipedia and Freebase (Test Submissions). To evaluate the results of the experiments the well-known metrics precision, recall and f-measure, were used. These metrics were applied to evaluate the ...
Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras
What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule ...
Hands-On Deep Learning Architectures with Python: Create deep neural networks to solve computational problems using TensorFlow and Keras
... You. Only. Look. Once. (YOLO). The object detection architectures we've discussed ... real time. To counter these challenges, a new prediction-based (regression ... Unified,. Image sourced from the paper, You Only Look Once: Unified, Real-Time ...
STROKE: Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI
... allows loading of UI files created in Qt Designer . • from matplotlib ... ____ init ____ ( ) method is the constructor for the class . It initializes the class object and sets up the GUI ... user interface ( UI ) file named " gui_stroke.ui ...
Hands-On Guide To IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And TensorFlow with PYTHON GUI
... Programming for Animation and Games; C # Programming for SMA / SMK and Students; MATLAB For Students and Researchers; Graphics in JavaScript: Quick Learning Series; JavaScript Image Processing Methods: From A to Z; Java GUI Case Study ...
Step by Step Tutorial IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And TensorFlow with PYTHON GUI
... checking the existence of the model file , this method ... ____ init __ ( ) method : 1 self.pbTrain.clicked.connect ( self.train_model ) Displaying Train, Validation ... DATA button . Then , click on TRAIN MODEL button . Quit application and ...
Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras
