A Complete Guide on TensorFlow 2.0 using Keras API
Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0
4.38 (1988 reviews)
55 235
students
13 hours
content
Feb 2025
last update
$84.99
regular price
What you will learn
How to use Tensorflow 2.0 in Data Science
Important differences between Tensorflow 1.x and Tensorflow 2.0
How to implement Artificial Neural Networks in Tensorflow 2.0
How to implement Convolutional Neural Networks in Tensorflow 2.0
How to implement Recurrent Neural Networks in Tensorflow 2.0
How to build your own Transfer Learning application in Tensorflow 2.0
How to build a stock market trading bot using Reinforcement Learning (Deep-Q Network)
How to build Machine Learning Pipeline in Tensorflow 2.0
How to conduct Data Validation and Dataset Preprocessing using TensorFlow Data Validation and TensorFlow Transform.
Putting a TensorFlow 2.0 model into production
How to create a Fashion API with Flask and TensorFlow 2.0
How to serve a TensorFlow model with RESTful API
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Our Verdict
The Complete Guide on TensorFlow 2.0 using Keras API course offers a detailed exploration of TensorFlow 2.0 functionality within the realm of data science applications. While rich in information and covering many essential areas, potential students should be prepared to encounter compatibility issues with outdated notebooks. Some content appears copied from other courses, diminishing credibility, and there's room for improvement in explaining code examples and providing more challenging exercises or quizzes for deeper understanding.
What We Liked
- Comprehensive coverage of TensorFlow 2.0 and its applications in data science, including ANNs, CNNs, RNNs, and Transfer Learning
- Highlights important differences between TensorFlow 1.x and 2.0, aiding learners in the transition
- Introduces building a Machine Learning Pipeline, Data Validation, and Dataset Preprocessing using TensorFlow Data Validation and Transform
- Offers guidance on putting a TensorFlow 2.0 model into production and creating RESTful API with Flask and TensorFlow 2.0
Potential Drawbacks
- Outdated notebooks and libraries causing compatibility issues, leading to errors in code snippets
- Overlap of information within sections and some content appearing to be copied from another course (particularly Pytorch), reducing credibility
- Lack of depth in Reinforcement Learning section; examples poorly explained without comments or descriptions, leaving learners confused and seeking more explanation
- Limited coding exercises and quizzes focusing primarily on syntax, not challenging learners' understanding or application skills
2400948
udemy ID
06/06/2019
course created date
31/08/2019
course indexed date
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