Download Udemy – TensorFlow 2.0 Practical Advanced 2022-1

Description

TensorFlow 2.0 Practical Advanced course. Google has just released TensorFlow 2.0, Google’s most powerful open source platform for building and deploying AI models in action. The release of Tensorflow 2.0 is a big win for developers and AI enthusiasts as it enables the development of ultra-advanced AI techniques in a much easier and faster way. The purpose of this course is to provide students with practical knowledge in the field of building, training, testing and deploying advanced artificial neural networks and deep learning models using TensorFlow 2.0 and Google Colab. This course covers advanced and advanced implementation of artificial intelligence models in TensorFlow 2.0 such as DeepDream, AutoEncoders, Generative Adversarial Networks (GANs), transfer training using TensorFlow Hub, long short term memory (LSTM) recurrent neural networks and many more. cover. More. The applications of these advanced AI models are endless, including generating novel human-like images, text translation, image denoising, image compression, text-to-image translation, image segmentation, and image captioning. The global AI and machine learning technology sectors are expected to grow from $1.4 billion to $8.8 billion by 2022, and the AI technology sector is expected to create about 2.3 million jobs by 2020. This technology is being developed on a large scale and is being used almost every part of this course provides students with hands-on hands-on experience in teaching advanced artificial neural networks using real-world datasets using TensorFlow 2.0 and Google Colab. This course covers several techniques in a hands-on, projects including but not limited to:

Develop, train, and test DeepDream’s advanced algorithm to create AI-powered artistic masterpieces!
Implement revolutionary generative adversarial networks called GANs to generate completely new images.
Develop short-term memory (LSTM) networks to generate new Shakespeare-style text!
Implement AI models in action using the TensorFlow 2.0 service.
Use Auto-Encoders for image compression and noise removal.
Using transfer learning to transfer knowledge from pre-trained networks to classify new images using TensorFlow 2.0 Hub.

This course is aimed at students who want to gain a basic understanding of how to build, train, test, and deploy advanced models in Tensorflow 2.0. Basic knowledge of programming and artificial neural networks is recommended. Students enrolled in this course will master advanced artificial intelligence and deep learning techniques and can directly apply these skills to solving challenging real-world problems.

What you will learn in the TensorFlow 2.0 Practical Advanced course

Build, train, test, and deploy advanced artificial neural network (ANN) models using the newly released TensorFlow 2.0.
Understand the basic theory and mathematics behind Generative Adversarial Neural Networks (GAN).
Use revolutionary GANs to generate completely new images using the Keras API in TF 2.0.
Understand the basic math and theory behind autoencoders and variable autoencoders (VAEs).
Train and test autoencoders for compression and denoising using the Keras API in TF 2.0.
Understand the basic theory and mathematics behind the DeepDream algorithm. Develop, train and test advanced DeepDream algorithm for creating AI-based artistic masterpieces using Keras API in TF 2.0!
Understanding the intuition behind recurrent short-term memory (LSTM) neural networks (RNNs).
Train short-term memory (LSTM) networks to generate new Shakespeare-style text using the Keras API in TF 2.0!
Apply transfer learning to transfer knowledge from pre-trained MobileNet and ResNet networks to classify new images using TensorFlow 2.0 Hub.
Develop ANN models and train them in Google’s Colab while leveraging the power of GPUs and TPUs.
Implement AI models in action using the TensorFlow 2.0 service.