Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0
InstructorHadelin de PontevesThe curriculum includes workflows for model serving via Flask and TensorFlow Serving, as well as mobile optimization using TensorFlow Lite.
Instruction covers various neural network types including Artificial, Convolutional, and Recurrent architectures.
Editorial course preview
These 2 complementary views highlight concrete, legible examples from the course presentation.
The mission plan outlines the course structure, covering foundation techniques with TensorFlow 2.0, building various neural networks like ANN and CNN, and creating a deep Q-learning model for an AI trading bot.
An introductory graphic displays the mission to learn implementing deep learning with TensorFlow 2.0 alongside a presenter looking upward.
Learners have reported difficulty running notebooks in Google Colab due to outdated library versions and compatibility issues. These signals originated from reviews prior to the February 2025 update; while the update may have addressed these issues, its effectiveness is not confirmed.
Certain advanced sections, such as Reinforcement Learning and TFDV, are described as lacking sufficient detail or intuitive explanation. These signals were recorded prior to the February 2025 update, which may have introduced corrections.
There are notable concerns regarding the compatibility of provided notebooks with current library versions and Google Colab environments in reviews predating the latest update; while the update may have addressed these issues, its effectiveness is not confirmed.
The course aligns with its target of introducing TensorFlow 2.0, though the actual difficulty may lean more toward an intermediate level. These signals originated from reviews prior to the February 2025 update; while the update may have addressed these issues, its effectiveness is not confirmed.
