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A Complete Guide on TensorFlow 2.0 using Keras API

Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0
4.5
4.5/5
(1,702 reviews)
52,102 students
Created by

9.5

CourseMarks Score®

10.0

Freshness

8.2

Feedback

9.8

Content

Platform: Udemy
Video: 13h 5m
Language: English
Next start: On Demand

Top TensorFlow courses:

Detailed Analysis

CourseMarks Score®

9.5 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

10.0 / 10
This course was last updated on 8/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

8.2 / 10
We analyzed factors such as the rating (4.5/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.8 / 10
Video Score: 9.6 / 10
The course includes 13h 5m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 6 hours 00 minutes of 44 TensorFlow courses on Udemy.
Detail Score: 10.0 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.9 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

13 articles.
3 resources.
0 exercise.
0 tests or quizzes.

Table of contents

Description

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.

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.

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

Requirements

• Some maths basics like knowing what is a differentiation or a gradient
• Python basics

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

How much does the A Complete Guide on TensorFlow 2.0 using Keras API course cost? Is it worth it?

The course costs $17.99. And currently there is a 82% discount on the original price of the course, which was $99.99. So you save $82 if you enroll the course now.
The average price is $12.5 of 44 TensorFlow courses. So this course is 4% cheaper than the average TensorFlow course on Udemy.

Does the A Complete Guide on TensorFlow 2.0 using Keras API course have a money back guarantee or refund policy?

YES, A Complete Guide on TensorFlow 2.0 using Keras API has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the A Complete Guide on TensorFlow 2.0 using Keras API course, but there is a $82 discount from the original price ($99.99). So the current price is just $17.99.

Who is the instructor? Is Hadelin de Ponteves a SCAM or a TRUSTED instructor?

Hadelin de Ponteves has created 29 courses that got 261,100 reviews which are generally positive. Hadelin de Ponteves has taught 1,361,516 students and received a 4.5 average review out of 261,100 reviews. Depending on the information available, Hadelin de Ponteves is a TRUSTED instructor.
AI Entrepreneur
Hadelin is the co-founder and CEO at BlueLife AI, which leverages the power of cutting edge Artificial Intelligence to empower businesses to make massive profits by innovating, automating processes and maximizing efficiency. Hadelin is also an online entrepreneur who has created top-rated educational e-courses to the world on topics such as Machine Learning, Artificial Intelligence and Blockchain.




Summary in numbers:

2M+ online courses sold.

1.3M students.

29 online courses created.

4.5/5 instructor average rating.

2 books written.




Instagram: @hadelin2p

9.5

CourseMarks Score®

10.0

Freshness

8.2

Feedback

9.8

Content

Platform: Udemy
Video: 13h 5m
Language: English
Next start: On Demand

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