A Complete Guide on Tensor Flow 2.0 using Keras API

About Course

Welcome to Tensor flow 2.0!

Tensor Flow 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.

Course Content

Introduction

001 Welcome to the TensorFlow 2.0 course! Discover it

00:00

02 – TensorFlow 2.0 Basics

03 – Artificial Neural Networks

04 – Convolutional Neural Networks

05 – Recurrent Neural Networks

06 – Transfer Learning and Fine Tuning

001 What is Transfer Learning

00:00
002 Project Setup

00:00
003 Dataset preprocessing

00:00
004 Loading the Mobile net V2 model

00:00
005 Freezing the pre-trained model

00:00
006 Adding a custom head to the pre-trained model

00:00
007 Defining the transfer learning model

00:00
008 Compiling the Transfer Learning model

00:00
009 Image Data Generators

00:00
010 Transfer Learning

00:00
011 Evaluating Transfer Learning results

00:00
012 Fine Tuning model definition

00:00
013 Compiling the Fine Tuning model

00:00
014 Fine Tuning

00:00
015 Evaluating Fine Tuning results

00:00

07 – Deep Reinforcement Learning Theory

001 What is Reinforcement Learning

00:00
002 The Bellman Equation

00:00
003 Markov Decision Process (MDP)

00:00
04 Q-Learning Intuition

00:00
005 Temporal Difference

00:00
006 Deep Q-Learning Intuition – Step 1

00:00
007 Deep Q-Learning Intuition – Step 2

00:00
008 Experience Replay

00:00
009 Action Selection Policies

00:00

08 – Deep Reinforcement Learning for Stock Market trading

001 Project Setup

00:00
002 AI Trader – Step 1

00:00
003 AI Trader – Step 2

00:00
004 AI Trader – Step 3

00:00
005 AI Trader – Step 4

00:00
006 AI Trader – Step 5

00:00
007 Dataset Loader function

00:00
008 State creator function
009 Loading the dataset

00:00
010 Defining the model

00:00
011 Training loop – Step 1

00:00
012 Training loop – Step 2

00:00

09 – Data Validation with TensorFlow Data Validation (TFDV)

10 – Dataset Preprocessing with TensorFlow Transform (TFT)

11 – Fashion API with Flask and TensorFlow 2.0

12 – Image Classification API with TensorFlow Serving

001 What is the TensorFlow Serving

00:00
002 TensorFlow Serving architecture

00:00
003 Project setup

00:00
004 Dataset preprocessing

00:00
005 Defining, training and evaluating a model

00:00
006 Saving the model for production

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07 Serving the TensorFlow 2.0 Model

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008 Creating a JSON object

00:00
09 Sending the first POST request to the model

00:00
010 Sending the POST request to a specific model

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13 – TensorFlow Lite Prepare a model for a mobile device

001 What is the TensorFlow Lite

00:00
002 Project setup

00:00
03 Dataset preprocessing

00:00
04 Building a model

00:00
005 Training, evaluating the model

00:00
006 Saving the model

00:00
007 TensorFlow Lite Converter

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08 Converting the model to a TensorFlow Lite model

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09 Saving the converted model

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14 – Distributed Training with TensorFlow 2.0

15 – Annex 1 – Artificial Neural Networks Theory

16 – Annex 2 – Convolutional Neural Networks Theory

001 Plan of Attack

00:00
002 What are Convolutional Neural Networks

00:00
003 Step 1 – Convolution

00:00
004 Step 1 Bis – ReLU Layer

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005 Step 2 – Max Pooling

00:00
006 Step 3 – Flattening

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007 Step 4 – Full Connection

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008 Summary

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009 Softmax & Cross-Entropy

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About Course

Course Content

Introduction

001 Welcome to the TensorFlow 2.0 course! Discover it

02 – TensorFlow 2.0 Basics

001 From TensorFlow 1.x to TensorFlow 2.0

002 Constants, Variables, Tensors

003 Operations with Tensors

004 Strings

03 – Artificial Neural Networks

001 Project Setup

002 Data Preprocessing

003 Building the Artificial Neural Network

004 Training the Artificial Neural Network

005 Evaluating the Artificial Neural Network

04 – Convolutional Neural Networks

001 Project Setup & Data Preprocessing

002 Building the Convolutional Neural Network

003 Training and Evaluating the Convolutional Neural Network

05 – Recurrent Neural Networks

001 Project Setup & Data Preprocessing

002 Building the Recurrent Neural Network

003 Training and Evaluating the Recurrent Neural Network

06 – Transfer Learning and Fine Tuning

001 What is Transfer Learning

002 Project Setup

003 Dataset preprocessing

004 Loading the Mobile net V2 model

005 Freezing the pre-trained model

006 Adding a custom head to the pre-trained model

007 Defining the transfer learning model

008 Compiling the Transfer Learning model

009 Image Data Generators

010 Transfer Learning

011 Evaluating Transfer Learning results

012 Fine Tuning model definition

013 Compiling the Fine Tuning model

014 Fine Tuning

015 Evaluating Fine Tuning results

07 – Deep Reinforcement Learning Theory

001 What is Reinforcement Learning

002 The Bellman Equation

003 Markov Decision Process (MDP)

04 Q-Learning Intuition

005 Temporal Difference

006 Deep Q-Learning Intuition – Step 1

007 Deep Q-Learning Intuition – Step 2

008 Experience Replay

009 Action Selection Policies

08 – Deep Reinforcement Learning for Stock Market trading

001 Project Setup

002 AI Trader – Step 1

003 AI Trader – Step 2

004 AI Trader – Step 3

005 AI Trader – Step 4

006 AI Trader – Step 5

007 Dataset Loader function

008 State creator function

009 Loading the dataset

010 Defining the model

011 Training loop – Step 1

012 Training loop – Step 2

09 – Data Validation with TensorFlow Data Validation (TFDV)

001 Project Setup

002 Loading the pollution dataset

003 Creating dataset Schema

004 Computing test set statistics

005 Anomaly detection with TensorFlow Data Validation

006 Preparing Schema for production

007 Saving the Schema

10 – Dataset Preprocessing with TensorFlow Transform (TFT)

001 Project Setup

002 Initial dataset preprocessing

003 Dataset metadata

004 Preprocessing function

005 Dataset preprocessing pipeline

11 – Fashion API with Flask and TensorFlow 2.0

001 Project Setup

002 Importing project dependencies

003 Loading a pre-trained model