A Complete Guide on TensorFlow 2.0 using Keras API
4.3 (1,369 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
50,158 students enrolled

A Complete Guide on TensorFlow 2.0 using Keras API

Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0
4.3 (1,369 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
50,158 students enrolled
Last updated 8/2020
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This course includes
  • 13 hours on-demand video
  • 13 articles
  • 3 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Expand all 133 lectures 13:05:19
+ Introduction
4 lectures 18:44
Course Curriculum & Colab Toolkit
00:10
BONUS: 10 advantages of TensorFlow
00:15
BONUS: Learning Path
00:32
+ TensorFlow 2.0 Basics
4 lectures 32:43
Constants, Variables, Tensors
08:52
Operations with Tensors
06:20
Strings
06:04
+ Artificial Neural Networks
7 lectures 36:39
Project Setup
06:07
Data Preprocessing
07:48
Building the Artificial Neural Network
10:30
Training the Artificial Neural Network
07:08
Evaluating the Artificial Neural Network
04:37
Artificial Neural Network Quiz
3 questions
HOMEWORK: Artificial Neural Networks
00:16

Hello everyone,


Here you can find a solution to the homework for Ar


https://colab.research.google.com/drive/1mRAiRVlF80j_m2igy5tms5plDxQhMnRg


In the next lesson I will provide the link to the solution notebook.


Good luck!

HOMEWORK SOLUTION: Artificial Neural Networks
00:12
+ Convolutional Neural Networks
5 lectures 30:54
Project Setup & Data Preprocessing
07:55
Building the Convolutional Neural Network
14:34
Training and Evaluating the Convolutional Neural Network
07:49
Convolutional Neural Networks Quiz
4 questions
HOMEWORK: Convolutional Neural Networks
00:17
HOMEWORK SOLUTION: Convolutional Neural Networks
00:19
+ Recurrent Neural Networks
3 lectures 21:20
Project Setup & Data Preprocessing
07:19
Building the Recurrent Neural Network
06:41
Training and Evaluating the Recurrent Neural Network
07:20
Recurrent Neural Network Quiz
3 questions
+ Transfer Learning and Fine Tuning
15 lectures 45:47

This is the command to download the Dogs vs. Cats dataset:

!wget --no-check-certificate \

    https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip \

    -O ./cats_and_dogs_filtered.zip

Project Setup
03:47
Dataset preprocessing
05:51
Loading the MobileNet V2 model
03:16
Freezing the pre-trained model
01:22
Adding a custom head to the pre-trained model
03:33
Defining the transfer learning model
01:59
Compiling the Transfer Learning model
02:55
Image Data Generators
05:30
Transfer Learning
02:27
Evaluating Transfer Learning results
01:29
Fine Tuning model definition
04:01
Compiling the Fine Tuning model
01:09
Fine Tuning
02:03
Evaluating Fine Tuning results
01:31
Transfer Learning quiz
3 questions
+ Deep Reinforcement Learning Theory
9 lectures 02:13:59
What is Reinforcement Learning?
11:26
The Bellman Equation
18:25
Markov Decision Process (MDP)
16:27
Q-Learning Intuition
14:45
Temporal Difference
19:27
Deep Q-Learning Intuition - Step 1
15:15
Deep Q-Learning Intuition - Step 2
06:06
Experience Replay
15:45
Action Selection Policies
16:23
+ Deep Reinforcement Learning for Stock Market trading
12 lectures 54:05
Project Setup
02:28
AI Trader - Step 1
06:19
AI Trader - Step 2
02:17
AI Trader - Step 3
02:26
AI Trader - Step 4
02:58
AI Trader - Step 5
05:34
Dataset Loader function
06:28
State creator function
08:00
Loading the dataset
01:35
Defining the model
02:39
Training loop - Step 1
05:42
Training loop - Step 2
07:39
+ Data Validation with TensorFlow Data Validation (TFDV)
8 lectures 23:15
Project Setup
03:30
Loading the pollution dataset
04:04
Creating dataset Schema
04:59
Computing test set statistics
00:36
Anomaly detection with TensorFlow Data Validation
04:18
Preparing Schema for production
03:34
Saving the Schema
01:22
What's next?
00:52
+ Dataset Preprocessing with TensorFlow Transform (TFT)
6 lectures 27:45
Project Setup
02:07
Initial dataset preprocessing
06:29
Dataset metadata
04:06
Preprocessing function
05:12
Dataset preprocessing pipeline
09:08
What's next?
00:43
Requirements
  • Some maths basics like knowing what is a differentiation or a gradient
  • Python basics
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.



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