A Complete Guide on TensorFlow 2.0 using Keras API

$0.99
Instructor:
Hadelin of Ponteves, SuperDataScience Team, Anicin Wound, Ligency Team
Category:

TensorFlow 2.0 has just been released, and it introduced many features that simplify the model development and maintenance processes. From the educational side, it boosts people’s understanding by simplifying many complex concepts. From the industry point of view, models are much easier to understand, maintain, and develop.

Deep Learning is one of the fastest growing areas of Artificial Intelligence. In the past few years, we have proven that Deep Learning models, even the simplest ones, can solve very hard and complex tasks. Now, that the buzz-word period of Deep Learning has, partially, passed, people are releasing its power and potential for their product improvements.

 

What you’ll 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
Course content
19

19 sections • 133 lectures • Total duration 13 h 5 min

Introduction
TensorFlow 2.0 Basics
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Transfer Learning and Fine Tuning
Deep Reinforcement Learning Theory
Deep Reinforcement Learning for Stock Market trading
Data Validation with TensorFlow Data Validation (TFDV)
Dataset Preprocessing with TensorFlow Transform (TFT)
Fashion API with Flask and TensorFlow 2.0
Image Classification API with TensorFlow Serving
TensorFlow Lite: Prepare a model for a mobile device
Distributed Training with TensorFlow 2.0
Annex 1 - Artificial Neural Networks Theory
Annex 2 - Convolutional Neural Networks Theory
Annex 3 - Recurrent Neural Networks Theory
Extra Lectures
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