A Complete Guide on TensorFlow 2.0 using Keras API

Udemy

Deepen your understanding of Tensorflow 2.0's features by utilizing the Keras API for deep learning operations.

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₹ 449 3099

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                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study

                                    Mode of Delivery
                                    Video and Text Based

Introduction

Welcome to the TensorFlow 2.0 course! Discover its structure and the TF toolkit.
Course Curriculum & Colab Toolkit
BONUS: 10 advantages of TensorFlow
BONUS: Learning Path

TensorFlow 2.0 Basics

From TensorFlow 1.x to TensorFlow 2.0
Constants, Variables, Tensors
Operations with Tensors
Strings

Artificial Neural Networks

Project Setup
Data Preprocessing
Building the Artificial Neural Network
Training the Artificial Neural Network
Evaluating the Artificial Neural Network
Artificial Neural Network Quiz
HOMEWORK: Artificial Neural Networks
HOMEWORK SOLUTION: Artificial Neural Networks

Convolutional Neural Networks

Project Setup & Data Preprocessing
Building the Convolutional Neural Network
Training and Evaluating the Convolutional Neural Network
Convolutional Neural Networks Quiz
HOMEWORK: Convolutional Neural Networks
HOMEWORK SOLUTION: Convolutional Neural Networks

Recurrent Neural Networks

Project Setup & Data Preprocessing
Building the Recurrent Neural Network
Training and Evaluating the Recurrent Neural Network
Recurrent Neural Network Quiz

Transfer Learning and Fine Tuning

What is Transfer Learning?
Project Setup
Dataset preprocessing
Loading the MobileNet V2 model
Freezing the pre-trained model
Adding a custom head to the pre-trained model
Defining the transfer learning model
Compiling the Transfer Learning model
Image Data Generators
Transfer Learning
Evaluating Transfer Learning results
Fine Tuning model definition
Compiling the Fine Tuning model
Fine Tuning
Evaluating Fine Tuning results
Transfer Learning quiz

Deep Reinforcement Learning Theory

What is Reinforcement Learning?
The Bellman Equation
Markov Decision Process (MDP)
Q-Learning Intuition
Temporal Difference
Deep Q-Learning Intuition - Step 1
Deep Q-Learning Intuition - Step 2
Experience Replay
Action Selection Policies

Deep Reinforcement Learning for Stock Market trading

Project Setup
AI Trader - Step 1
AI Trader - Step 2
AI Trader - Step 3
AI Trader - Step 4
AI Trader - Step 5
Dataset Loader function
State creator function
Loading the dataset
Defining the model
Training loop - Step 1
Training loop - Step 2

Data Validation with TensorFlow Data Validation (TFDV)

Project Setup
Loading the pollution dataset
Creating dataset Schema
Computing test set statistics
Anomaly detection with TensorFlow Data Validation
Preparing Schema for production
Saving the Schema
What's next?

Dataset Preprocessing with TensorFlow Transform (TFT)

Project Setup
Initial dataset preprocessing
Dataset metadata
Preprocessing function
Dataset preprocessing pipeline
What's next?

Fashion API with Flask and TensorFlow 2.0

Project Setup
Importing project dependencies
Loading a pre-trained model
Defining the Flask application
Creating classify function
Starting the Flask application
Sending API requests over internet to the model

Image Classification API with TensorFlow Serving

What is the TensorFlow Serving?
TensorFlow Serving architecture
Project setup
Dataset preprocessing
Defining, training and evaluating a model
Saving the model for production
Serving the TensorFlow 2.0 Model
Creating a JSON object
Sending the first POST request to the model
Sending the POST request to a specific model

TensorFlow Lite: Prepare a model for a mobile device

What is the TensorFlow Lite?
Project setup
Dataset preprocessing
Building a model
Training, evaluating the model
Saving the model
TensorFlow Lite Converter
Converting the model to a TensorFlow Lite model
Saving the converted model
What's next?

Distributed Training with TensorFlow 2.0

What is the Distributed Training?
Project Setup
Dataset preprocessing
Defining a non-distributed model (normal CNN model)
Setting up a distributed strategy
Defining a distributed model
Final evaluation - Speed test: normal model vs distributed model

Annex 1 - Artificial Neural Networks Theory

Plan of Attack
The Neuron
The Activation Function
How do Neural Networks Work?
How do Neural Networks Learn?
Gradient Descent
Stochastic Gradient Descent
Backpropagation

Annex 2 - Convolutional Neural Networks Theory

Plan of Attack
What are Convolutional Neural Networks?
Step 1 - Convolution
Step 1 Bis - ReLU Layer
Step 2 - Max Pooling
Step 3 - Flattening
Step 4 - Full Connection
Summary
Softmax & Cross-Entropy

Annex 3 - Recurrent Neural Networks Theory

Plan of Attack
What are Recurrent Neural Networks?
Vanishing Gradient
LSTMs
LSTM Practical Intuition
LSTM Variations

Bonus Lectures

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A Complete Guide on TensorFlow 2.0 using Keras API

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