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Tensorflow 2.0: Deep Learning and Artificial Intelligence

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!
4.7
4.7/5
(6,082 reviews)
31,827 students
Created by

9.9

CourseMarks Score®

10.0

Freshness

9.3

Feedback

9.8

Content

Platform: Udemy
Video: 21h 58m
Language: English
Next start: On Demand

Top TensorFlow courses:

Detailed Analysis

CourseMarks Score®

9.9 / 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 10/2021 .

Course content can become outdated quite quickly.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

9.3 / 10
We analyzed factors such as the rating (4.7/5) 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: 10.0 / 10
The course includes 21h 58m 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: 9.5 / 10

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

This course contains:

2 articles.
0 resource.
0 exercise.
0 test.

Table of contents

Description

Welcome to Tensorflow 2.0!

What an exciting time. It’s been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.
Tensorflow is Google’s library for deep learning and artificial intelligence.
Deep Learning has been responsible for some amazing achievements recently, such as:
•Generating beautiful, photo-realistic images of people and things that never existed (GANs)
•Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)
•Self-driving cars (Computer Vision)
•Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)
•Even creating videos of people doing and saying things they never did (DeepFakes – a potentially nefarious application of deep learning)

Tensorflow is the world’s most popular library for deep learning, and it’s built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.
In other words, if you want to do deep learning, you gotta know Tensorflow.

This course is for beginner-level students all the way up to expert-level students. How can this be?
If you’ve just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.
Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).
Current projects include:
•Natural Language Processing (NLP)
•Recommender Systems
•Transfer Learning for Computer Vision
•Generative Adversarial Networks (GANs)
•Deep Reinforcement Learning Stock Trading Bot
Even if you’ve taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.
This course is designed for students who want to learn fast, but there are also “in-depth” sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).

Advanced Tensorflow topics include:
•Deploying a model with Tensorflow Serving (Tensorflow in the cloud)
•Deploying a model with Tensorflow Lite (mobile and embedded applications)
•Distributed Tensorflow training with Distribution Strategies
•Writing your own custom Tensorflow model
•Converting Tensorflow 1.x code to Tensorflow 2.0
•Constants, Variables, and Tensors
•Eager execution
•Gradient tape

Instructor’s Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.

Thanks for reading, and I’ll see you in class!

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
•Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)

You will learn

✓ Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
✓ Predict Stock Returns
✓ Time Series Forecasting
✓ Computer Vision
✓ How to build a Deep Reinforcement Learning Stock Trading Bot
✓ GANs (Generative Adversarial Networks)
✓ Recommender Systems
✓ Image Recognition
✓ Convolutional Neural Networks (CNNs)
✓ Recurrent Neural Networks (RNNs)
✓ Use Tensorflow Serving to serve your model using a RESTful API
✓ Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices
✓ Use Tensorflow’s Distribution Strategies to parallelize learning
✓ Low-level Tensorflow, gradient tape, and how to build your own custom models
✓ Natural Language Processing (NLP) with Deep Learning
✓ Demonstrate Moore’s Law using Code
✓ Transfer Learning to create state-of-the-art image classifiers

Requirements

• Know how to code in Python and Numpy
• For the theoretical parts (optional), understand derivatives and probability

This course is for

• Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0

How much does the Tensorflow 2.0: Deep Learning and Artificial Intelligence course cost? Is it worth it?

The course costs $23.99. And currently there is a 82% discount on the original price of the course, which was $129.99. So you save $106 if you enroll the course now.
The average price is $12.5 of 44 TensorFlow courses on Udemy.

Does the Tensorflow 2.0: Deep Learning and Artificial Intelligence course have a money back guarantee or refund policy?

YES, Tensorflow 2.0: Deep Learning and Artificial Intelligence 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 Tensorflow 2.0: Deep Learning and Artificial Intelligence course, but there is a $106 discount from the original price ($129.99). So the current price is just $23.99.

Who is the instructor? Is Lazy Programmer Inc. a SCAM or a TRUSTED instructor?

Lazy Programmer Inc. has created 29 courses that got 119,482 reviews which are generally positive. Lazy Programmer Inc. has taught 468,493 students and received a 4.6 average review out of 119,482 reviews. Depending on the information available, Lazy Programmer Inc. is a TRUSTED instructor.
Artificial intelligence and machine learning engineer
Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I’ve created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I’ve used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I’ve used MySQL, Postgres, Redis, MongoDB, and more.

9.9

CourseMarks Score®

10.0

Freshness

9.3

Feedback

9.8

Content

Platform: Udemy
Video: 21h 58m
Language: English
Next start: On Demand

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