TensorFlow 2.0 Practical Free Download


Free Download TensorFlow 2.0 Practical, with this course you will have the option to Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 10 practical projects. It contains 84 recordings. Remember that numerous understudies are tried out this course TensorFlow 2.0 Practical, so don't hold on to download it presently, it's totally free. You can start learning TensorFlow 2.0 Practical by clicking the download link below.

Before you can start learning from this course explained in English (US) you need to have PC with internet connection.

TensorFlow 2.0 Practical is targeting for people that have interset in Data Scientists who want to apply their knowledge on Real World Case Studies, AI Developers, AI Researchers.

Finally you will learn how to Master Google’s newly released TensorFlow 20 to build, train, test and deploy Artificial Neural Networks (ANNs) models, Learn how to develop ANNs models and train them in Google’s Colab while leveraging the power of GPUs and TPUs, Deploy ANNs models in practice using TensorFlow 20 Serving, Learn how to visualize models graph and assess their performance during training using Tensorboard, Understand the underlying theory and mathematics behind Artificial Neural Networks and Convolutional Neural Networks (CNNs), Learn how to train network weights and biases and select the proper transfer functions, Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods, Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance, Apply ANNs to perform regression tasks such as house prices predictions and sales/revenue predictions, Assess the performance of trained ANN models for regression tasks using KPI (Key Performance indicators) such as Mean Absolute error, Mean squared Error, and Root Mean Squared Error, R-Squared, and Adjusted R-Squared, Assess the performance of trained ANN models for classification tasks using KPI such as accuracy, precision and recall, Apply Convolutional Neural Networks to classify images, Sample real-world, practical projects:, Project #1: Train Simple ANN to convert Celsius temperature reading to Fahrenheit, Project #2 (Exercise): Train Feedforward ANN to predict Revenue/sales, Project #3: As a real-estate consultant, predict house prices using ANNs (Regression Task), Project #4 (Exercise): As a business owner, predict Bike rental usage (Regression Task), Project #5: Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection (Classification task), Project #6: Develop AI models to perform sentiment analysis and analyze online customer reviews, Project #7: Train LeNet Deep Learning models to perform traffic signs classification, Project #8: Train CNN to perform fashion classification, Project #9: Train CNN to perform image classification using Cifar-10 dataset, Project #10: Deploy deep learning image classification model using TF serving.

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