A Complete Guide on Tensor Flow 2.0 using Keras API

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Last Update February 12, 2022

About Course

Build Amazing Applications of Deep Learning and Artificial Intelligence in Tensor Flow 2.0

Description

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.

Topics for this course

120 Lessons

Introduction

001 Welcome to the TensorFlow 2.0 course! Discover it00:00:00

02 – TensorFlow 2.0 Basics

03 – Artificial Neural Networks

A Complete Guide on Tensor Flow 2.0 using Keras API

About Course

Description

Topics for this course

Introduction

001 Welcome to the TensorFlow 2.0 course! Discover it00:00:00

02 – TensorFlow 2.0 Basics

001 From TensorFlow 1.x to TensorFlow 2.000:00:00

002 Constants, Variables, Tensors00:00:00

003 Operations with Tensors00:00:00

004 Strings00:00:00

03 – Artificial Neural Networks

001 Project Setup00:00:00

002 Data Preprocessing00:00:00

003 Building the Artificial Neural Network00:00:00

004 Training the Artificial Neural Network00:00:00

005 Evaluating the Artificial Neural Network00:00:00

04 – Convolutional Neural Networks

001 Project Setup & Data Preprocessing00:00:00

002 Building the Convolutional Neural Network00:00:00

003 Training and Evaluating the Convolutional Neural Network00:00:00

05 – Recurrent Neural Networks

001 Project Setup & Data Preprocessing00:00:00

002 Building the Recurrent Neural Network00:00:00

003 Training and Evaluating the Recurrent Neural Network00:00:00

06 – Transfer Learning and Fine Tuning

001 What is Transfer Learning00:00:00

002 Project Setup00:00:00

003 Dataset preprocessing00:00:00

004 Loading the Mobile net V2 model00:00:00

005 Freezing the pre-trained model00:00:00

006 Adding a custom head to the pre-trained model00:00:00

007 Defining the transfer learning model00:00:00

008 Compiling the Transfer Learning model00:00:00

009 Image Data Generators00:00:00

010 Transfer Learning00:00:00

011 Evaluating Transfer Learning results00:00:00

012 Fine Tuning model definition00:00:00

013 Compiling the Fine Tuning model00:00:00

014 Fine Tuning00:00:00

015 Evaluating Fine Tuning results00:00:00

07 – Deep Reinforcement Learning Theory

001 What is Reinforcement Learning00:00:00

002 The Bellman Equation00:00:00

003 Markov Decision Process (MDP)00:00:00

04 Q-Learning Intuition00:00:00

005 Temporal Difference00:00:00

006 Deep Q-Learning Intuition – Step 100:00:00

007 Deep Q-Learning Intuition – Step 200:00:00

008 Experience Replay00:00:00

009 Action Selection Policies00:00:00

08 – Deep Reinforcement Learning for Stock Market trading

001 Project Setup00:00:00

002 AI Trader – Step 100:00:00

003 AI Trader – Step 200:00:00

004 AI Trader – Step 300:00:00

005 AI Trader – Step 400:00:00

006 AI Trader – Step 500:00:00

007 Dataset Loader function00:00:00

008 State creator function

009 Loading the dataset00:00:00

010 Defining the model00:00:00

011 Training loop – Step 100:00:00

012 Training loop – Step 200:00:00

09 – Data Validation with TensorFlow Data Validation (TFDV)

001 Project Setup00:00:00

002 Loading the pollution dataset00:00:00

003 Creating dataset Schema00:00:00

004 Computing test set statistics00:00:00

005 Anomaly detection with TensorFlow Data Validation00:00:00

006 Preparing Schema for production00:00:00

007 Saving the Schema00:00:00

10 – Dataset Preprocessing with TensorFlow Transform (TFT)

001 Project Setup00:00:00

002 Initial dataset preprocessing00:00:00

003 Dataset metadata00:00:00

004 Preprocessing function00:00:00

005 Dataset preprocessing pipeline00:00:00

11 – Fashion API with Flask and TensorFlow 2.0

001 Project Setup00:00:00