A Complete Guide on TensorFlow 2.0 using Keras API
Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0
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
- Some maths basics like knowing what is a differentiation or a gradient
- Python basics
Welcome to Tensorflow 2.0!
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.
The course is structured in a way to cover all topics from neural network modeling and training to put it in production.
In Part 1 of the course, you will learn about the technology stack that we will use throughout the course (Section 1) and the TensorFlow 2.0 library basics and syntax (Section 2).
In Part 2 of the course, we will dig into the exciting world of deep learning. Through this part of the course, you will implement several types of neural networks (Fully Connected Neural Network (Section 3), Convolutional Neural Network (Section 4), Recurrent Neural Network (Section 5)). At the end of this part, Section 6, you will learn and build their own Transfer Learning application that achieves state of the art (SOTA) results on the Dogs vs. Cats dataset.
After passing the part 2 of the course and ultimately learning how to implement neural networks, in Part 3 of the course, you will learn how to make your own Stock Market trading bot using Reinforcement Learning, specifically Deep-Q Network.
Part 4 is all about TensorFlow Extended (TFX). In this part of the course, you will learn how to work with data and create your own data pipelines for production. In Section 8 we will check if the dataset has any anomalies using the TensorFlow Data Validation library and after learn how to check a dataset for anomalies, in Section 9, we will make our own data preprocessing pipeline using the TensorFlow Transform library.
Who this course is for:
- Deep Learning Engineers who want to learn Tensorflow 2.0
- Artificial Intelligence Engineers who want to expand their Deep Learning stack skills
- Computer Scientists who want to enter the exciting area of Deep Learning and Artificial Intelligence
- Data Scientists who want to take their AI Skills to the next level
- AI experts who want to expand on the field of applications
- Python Developers who want to enter the exciting area of Deep Learning and Artificial Intelligence
- Engineers who work in technology and automation
- Businessmen and companies who want to get ahead of the game
- Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence
- Anyone passionate about Artificial Intelligence