TensorFlow 2.0 incorporates a number of features that enables the definition and training of state of the art models without sacrificing speed or performanceKeras Project Solutions - Creating and Training a ModelRNN on a Sine Wave - LSTMs and Forecasting Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. This explains the basics of how we can use tf.keras API in TensorFlow 2 to build and compile models. Keras Regression Code Along - Data Preprocessing and Creating a ModelEvaluating Performance - Regression Error MetricsExcellent course which provides a full overview of Perceptrons, ANNs, CNNs, RNNs, and autoencoding with applications in classification, regression, dimensionality reduction, image classification and time series forecasting.CNN on Real Image Files - Part Two - Data ProcessingEvaluating Performance - Classification Error MetricsKeras Syntax Basics - Part Two - Creating and Training the ModelPerform Image Classification with Convolutional Neural NetworksKeras Project Solutions - Dealing with Missing DataBonus - Multivariate Time Series - RNN and LSTMsAWS Certified Solutions Architect - AssociateUse Generative Adversarial Networks (GANs) to generate imagesTensorFlow 2.0 Keras Project Options OverviewKeras Project Solutions - Categorical DataThis course covers a variety of topics, includingKeras Project Solutions - Model EvaluationForecast Time Series data with Recurrent Neural NetworksKeras Regression Code Along - Exploratory Data AnalysisGet your team access to 4,000+ top Udemy courses anytime, anywhere.It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!Keras Regression Code Along - Model Evaluation and PredictionsGenerate text with RNNs and Natural Language ProcessingKeras Syntax Basics - Part One - Preparing the DataDownloading Data Set for Real Image LecturesCNN on MNIST - Part Three - Model EvaluationThis course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! {{ format_drm_information.format_name }} offThis book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. TensorFlow 2.0, recently released and open-sourced to the community, is a flexible and adaptable deep learning framework that has won back a lot of detractors. Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras.
You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. We also have plenty of exercises to test your new skills along the way!Keras Regression Code Along - Exploratory Data Analysis - ContinuedWe'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand.Leverage the Keras API to quickly build models that run on Tensorflow 2Keras Syntax Basics - Part Three - Model EvaluationMulti-Class Classification ConsiderationsCNN on MNIST - Part Two - Creating and Training the ModelCNN on Real Image Files - Part Three - Creating the ModelKeras, a user-friendly API standard for machine learning, will be the central high-level API used to build and train models. 深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework. We'll see you inside the course! He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Highest Rated Rating: 4.7 out of 5 4.7 (2,951 ratings) 18,856 students Created by Jose Portilla.
I love the ease with which even beginners can pick up TensorFlow 2.0 and start executing deep learning tasks.
Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today.
Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. Feel free to contact him on LinkedIn for more information on in-person training sessions or group training sessions in Las Vegas, NV.Keras Project Solutions - Dealing with Missing Data - Part TwoThis course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes.
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!Keras Classification - Dealing with Overfitting and EvaluationCNN on Real Image Files - Part One - Reading in the DataCNN on Real Image Files - Part Four - Evaluating the ModelKeras Classification Code Along - EDA and PreprocessingTensorFlow 2.0 Keras Project Notebook OverviewBecome a deep learning guru today! {{ format_drm_information.format_name }} unrestricted DRM-Free Books