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A Complete Guide on TensorFlow 2.0 using Keras API

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A Complete Guide on TensorFlow 2.0 using Keras API, available at $84.99, has an average rating of 4.46, with 138 lectures, 4 quizzes, based on 1963 reviews, and has 54961 subscribers.

You will learn about 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 This course is ideal for individuals who are Deep Learning Engineers who want to learn Tensorflow 2.0 or Artificial Intelligence Engineers who want to expand their Deep Learning stack skills or Computer Scientists who want to enter the exciting area of Deep Learning and Artificial Intelligence or Data Scientists who want to take their AI Skills to the next level or AI experts who want to expand on the field of applications or Python Developers who want to enter the exciting area of Deep Learning and Artificial Intelligence or Engineers who work in technology and automation or Businessmen and companies who want to get ahead of the game or Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence or Anyone passionate about Artificial Intelligence It is particularly useful for Deep Learning Engineers who want to learn Tensorflow 2.0 or Artificial Intelligence Engineers who want to expand their Deep Learning stack skills or Computer Scientists who want to enter the exciting area of Deep Learning and Artificial Intelligence or Data Scientists who want to take their AI Skills to the next level or AI experts who want to expand on the field of applications or Python Developers who want to enter the exciting area of Deep Learning and Artificial Intelligence or Engineers who work in technology and automation or Businessmen and companies who want to get ahead of the game or Students in tech-related programs who want to pursue a career in Data Science, Machine Learning, or Artificial Intelligence or Anyone passionate about Artificial Intelligence.

Enroll now: A Complete Guide on TensorFlow 2.0 using Keras API

Summary

Title: A Complete Guide on TensorFlow 2.0 using Keras API

Price: $84.99

Average Rating: 4.46

Number of Lectures: 138

Number of Quizzes: 4

Number of Published Lectures: 133

Number of Published Quizzes: 4

Number of Curriculum Items: 142

Number of Published Curriculum Objects: 137

Original Price: $199.99

Quality Status: approved

Status: Live

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

Who Should Attend

  • 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

Target Audiences

  • 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

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 3of 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.

In Section 10 of the course, you will learn and create your own Fashion API using the Flask Python library and a pre-trained model. Throughout this section, you will get a better picture of how to send a request to a model over the internet. However, at this stage, the architecture around the model is not scalable to millions of request. Enter the Section 11. In this section of the course, you will learn how to improve solution from the previous section by using the TensorFlow Serving library. In a very easy way, you will learn and create your own Image Classification API that can support millions of requests per day!

These days it is becoming more and more popular to have a Deep Learning model inside an Android or iOS application, but neural networks require a lot of power and resources! That’s where the TensorFlow Lite library comes into play. In Section 12 of the course, you will learn how to optimize and convert any neural network to be suitable for a mobile device.

To conclude with the learning process and the Part 5 of the course, in Section 13 you will learn how to distribute the training of any Neural Network to multiple GPUs or even Servers using the TensorFlow 2.0 library.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit.

Lecture 2: Course Curriculum & Colab Toolkit

Lecture 3: 10 advantages of TensorFlow

Lecture 4: Learning Path

Chapter 2: TensorFlow 2.0 Basics

Lecture 1: From TensorFlow 1.x to TensorFlow 2.0

Lecture 2: Constants, Variables, Tensors

Lecture 3: Operations with Tensors

Lecture 4: Strings

Chapter 3: Artificial Neural Networks

Lecture 1: Project Setup

Lecture 2: Data Preprocessing

Lecture 3: Building the Artificial Neural Network

Lecture 4: Training the Artificial Neural Network

Lecture 5: Evaluating the Artificial Neural Network

Lecture 6: HOMEWORK: Artificial Neural Networks

Lecture 7: HOMEWORK SOLUTION: Artificial Neural Networks

Chapter 4: Convolutional Neural Networks

Lecture 1: Project Setup & Data Preprocessing

Lecture 2: Building the Convolutional Neural Network

Lecture 3: Training and Evaluating the Convolutional Neural Network

Lecture 4: HOMEWORK: Convolutional Neural Networks

Lecture 5: HOMEWORK SOLUTION: Convolutional Neural Networks

Chapter 5: Recurrent Neural Networks

Lecture 1: Project Setup & Data Preprocessing

Lecture 2: Building the Recurrent Neural Network

Lecture 3: Training and Evaluating the Recurrent Neural Network

Chapter 6: Transfer Learning and Fine Tuning

Lecture 1: What is Transfer Learning?

Lecture 2: Project Setup

Lecture 3: Dataset preprocessing

Lecture 4: Loading the MobileNet V2 model

Lecture 5: Freezing the pre-trained model

Lecture 6: Adding a custom head to the pre-trained model

Lecture 7: Defining the transfer learning model

Lecture 8: Compiling the Transfer Learning model

Lecture 9: Image Data Generators

Lecture 10: Transfer Learning

Lecture 11: Evaluating Transfer Learning results

Lecture 12: Fine Tuning model definition

Lecture 13: Compiling the Fine Tuning model

Lecture 14: Fine Tuning

Lecture 15: Evaluating Fine Tuning results

Chapter 7: Deep Reinforcement Learning Theory

Lecture 1: What is Reinforcement Learning?

Lecture 2: The Bellman Equation

Lecture 3: Markov Decision Process (MDP)

Lecture 4: Q-Learning Intuition

Lecture 5: Temporal Difference

Lecture 6: Deep Q-Learning Intuition – Step 1

Lecture 7: Deep Q-Learning Intuition – Step 2

Lecture 8: Experience Replay

Lecture 9: Action Selection Policies

Chapter 8: Deep Reinforcement Learning for Stock Market trading

Lecture 1: Project Setup

Lecture 2: AI Trader – Step 1

Lecture 3: AI Trader – Step 2

Lecture 4: AI Trader – Step 3

Lecture 5: AI Trader – Step 4

Lecture 6: AI Trader – Step 5

Lecture 7: Dataset Loader function

Lecture 8: State creator function

Lecture 9: Loading the dataset

Lecture 10: Defining the model

Lecture 11: Training loop – Step 1

Lecture 12: Training loop – Step 2

Chapter 9: Data Validation with TensorFlow Data Validation (TFDV)

Lecture 1: Project Setup

Lecture 2: Loading the pollution dataset

Lecture 3: Creating dataset Schema

Lecture 4: Computing test set statistics

Lecture 5: Anomaly detection with TensorFlow Data Validation

Lecture 6: Preparing Schema for production

Lecture 7: Saving the Schema

Lecture 8: What's next?

Chapter 10: Dataset Preprocessing with TensorFlow Transform (TFT)

Lecture 1: Project Setup

Lecture 2: Initial dataset preprocessing

Lecture 3: Dataset metadata

Lecture 4: Preprocessing function

Lecture 5: Dataset preprocessing pipeline

Lecture 6: What's next?

Chapter 11: Fashion API with Flask and TensorFlow 2.0

Lecture 1: Project Setup

Lecture 2: Importing project dependencies

Lecture 3: Loading a pre-trained model

Lecture 4: Defining the Flask application

Lecture 5: Creating classify function

Lecture 6: Starting the Flask application

Lecture 7: Sending API requests over internet to the model

Chapter 12: Image Classification API with TensorFlow Serving

Lecture 1: What is the TensorFlow Serving?

Lecture 2: TensorFlow Serving architecture

Lecture 3: Project setup

Lecture 4: Dataset preprocessing

Instructors

Rating Distribution

  • 1 stars: 40 votes
  • 2 stars: 56 votes
  • 3 stars: 236 votes
  • 4 stars: 664 votes
  • 5 stars: 967 votes

Frequently Asked Questions


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You can view and review the lecture materials indefinitely, like an on-demand channel.


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Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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