TensorFlow 20 Practical
What you’ll learn
Master Google’s newly released TensorFlow 2.0 to build, train, test and deploy Artificial Neural Networks (ANNs) models.
Learn how to develop ANNs models and train them in Google’s Colab while leveraging the power of GPUs and TPUs.
Deploy ANNs models in practice using TensorFlow 2.0 Serving.
Learn how to visualize models graph and assess their performance during training using Tensorboard.
Understand the underlying theory and mathematics behind Artificial Neural Networks and Convolutional Neural Networks (CNNs).
Learn how to train network weights and biases and select the proper transfer functions.
Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods.
Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance.
Apply ANNs to perform regression tasks such as house prices predictions and sales/revenue predictions.
Assess the performance of trained ANN models for regression tasks using KPI (Key Performance indicators) such as Mean Absolute error, Mean squared Error, and Root Mean Squared Error, R-Squared, and Adjusted R-Squared.
Assess the performance of trained ANN models for classification tasks using KPI such as accuracy, precision and recall.
Apply Convolutional Neural Networks to classify images.
Sample real-world, practical projects:
Project #1: Train Simple ANN to convert Celsius temperature reading to Fahrenheit
Project #2 (Exercise): Train Feedforward ANN to predict Revenue/sales
Project #3: As a real-estate consultant, predict house prices using ANNs (Regression Task)
Project #4 (Exercise): As a business owner, predict Bike rental usage (Regression Task)
Project #5: Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection (Classification task)
Project #6: Develop AI models to perform sentiment analysis and analyze online customer reviews.
Project #7: Train LeNet Deep Learning models to perform traffic signs classification.
Project #8: Train CNN to perform fashion classification
Project #9: Train CNN to perform image classification using Cifar-10 dataset
Project #10: Deploy deep learning image classification model using TF serving
Requirements
PC with internet connection
Description
Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster! TensorFlow 2.0 is Google’s most powerful, recently released open source platform to build and deploy AI models in practice.AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab.The course provides students with practical hands-on experience in training Artificial Neural Networks and Convolutional Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to:(1) Train Feed Forward Artificial Neural Networks to perform regression tasks such as sales/revenue predictions and house price predictions(2) Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection.(3) Train Deep Learning models to perform image classification tasks such as face detection, Fashion classification and traffic sign classification.(4) Develop AI models to perform sentiment analysis and analyze customer reviews.(5) Perform AI models visualization and assess their performance using Tensorboard(6) Deploy AI models in practice using Tensorflow 2.0 ServingThe course is targeted towards students wanting to gain a fundamental understanding of how to build and deploy models in Tensorflow 2.0. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master AI and Deep Learning techniques and can directly apply these skills to solve real world challenging problems using Google’s New TensorFlow 2.0.
Overview
Section 1: INTRODUCTION AND COURSE OUTLINE
Lecture 1 Introduction and Welcome Message
Lecture 2 Course Overview
Lecture 3 BONUS: Learning Path
Lecture 4 What’s AI, ML and DL
Lecture 5 Machine Learning – Big Picture
Lecture 6 Whats new in TF2 and Google Colab
Lecture 7 Whats New in TensorFlow 2.0
Lecture 8 What is Google Colab
Lecture 9 Google Colab Demo
Lecture 10 Eager Execution
Lecture 11 Keras API
Lecture 12 Get the materials
Section 2: BUILD YOUR FIRST SIMPLE PERCEPTRON (SINGLE NEURON) MODEL IN TF 2.0
Lecture 13 PROJECT #1 OVERVIEW: CONVERT CELSUIS TO FAHRENHEIT
Lecture 14 PROJECT #1 What are ANNs and How they learn?
Lecture 15 PROJECT #1 Build our first ANN model
Lecture 16 PROJECT #1 TF Playground
Lecture 17 PROJECT #1 Coding Step 1 – Load TF and Data
Lecture 18 PROJECT #1 Coding Step 2 – Model Training
Lecture 19 PROJECT #1 Coding Step 3 – Model Evaluation
Lecture 20 PROJECT #2 Overview
Lecture 21 PROJECT#2: Google Colab Questions Overview
Lecture 22 PROJECT # 2 Coding Part 1
Lecture 23 PROJECT # 2 Coding Part 2
Lecture 24 PROJECT # 2 Coding Part 3
Section 3: BUILD A MULTI LAYER ARTIFICIAL NEURAL NETWORKS FOR REGRESSION TASKS
Lecture 25 PROJECT #3: Overview
Lecture 26 PROJECT #3 Regression basics
Lecture 27 PROJECT #3 ANN in Action
Lecture 28 PROJECT #3 Activation functions overview
Lecture 29 PROJECT #3 MultiLayer Perceptron Network
Lecture 30 PROJECT #3 ANN Training and Epochs Definition
Lecture 31 PROJECT #3 Tensorflow Playground 3
Lecture 32 PROJECT #3 Gradient Descent
Lecture 33 PROJECT #3 Back Propagation
Lecture 34 PROJECT #3 Bias Variance Tradeoff
Lecture 35 PROJECT #3 Performance Metrics
Lecture 36 PROJECT #3 Coding part 1
Lecture 37 PROJECT #3 Coding part 2
Lecture 38 PROJECT #3 Coding part 3
Lecture 39 PROJECT #3 Coding part 4
Lecture 40 PROJECT #3 Coding part 5 – Training
Lecture 41 PROJECT #3 Coding part 6
Lecture 42 PROJECT #4 Overview
Lecture 43 PROJECT #4 Google Colab Overview
Lecture 44 PROJECT #4 Coding Part 1
Lecture 45 PROJECT #4 Coding Part 2
Lecture 46 PROJECT #4 Coding Part 3
Section 4: ARTIFICIAL NEURAL NETWORKS FOR CLASSIFICATION TASKS
Lecture 47 PROJECT #5 Project Overview sentiment
Lecture 48 PROJECT #5 Tokenization and Count Vectorizer
Lecture 49 PROJECT #5 Confusion Matrix
Lecture 50 PROJECT #5 Load Dataset
Lecture 51 PROJECT #5 Data Visualization
Lecture 52 PROJECT #5 Data Tokenization
Lecture 53 PROJECT #5 Model Building and Training
Lecture 54 PROJECT #5 Model Evaluation
Lecture 55 PROJECT #6 Project Overview
Lecture 56 PROJECT #6 Google Colab Project Questions Overview
Lecture 57 PROJECT #6 Google Colab Project Questions Overview 2
Lecture 58 PROJECT #6 Project Coding Solution Part 1
Lecture 59 PROJECT #6 Project Coding Solution Part 2
Section 5: DEEP LEARNING FOR IMAGE CLASSIFICATION
Lecture 60 PROJECT #7 Overview
Lecture 61 PROJECT #7 CNN Entire Network Overview
Lecture 62 PROJECT #7 Feature Detectors
Lecture 63 PROJECT #7 RELU
Lecture 64 PROJECT #7 Pooling and Downsampling
Lecture 65 PROJECT #7 Performance Improvement
Lecture 66 PROJECT #7 Coding part 1 Import Data
Lecture 67 PROJECT #7 Coding part 2 Visualization
Lecture 68 PROJECT #7 Coding part 3 Train model
Lecture 69 PROJECT #7 Coding part 4 – Evaluate model
Lecture 70 PROJECT #8 Project Overview
Lecture 71 PROJECT #8 LeNet Architecture
Lecture 72 PROJECT #8 Coding part 1
Lecture 73 PROJECT #8 Coding part 2
Lecture 74 PROJECT #8 Coding part 3
Lecture 75 PROJECT #9 Overview
Lecture 76 PROJECT #9 Questions Overview
Lecture 77 PROJECT #9 Solution Part 1
Lecture 78 PROJECT #9 Solution Part 2
Section 6: MODEL DEPLOYMENT USING TF SERVING
Lecture 79 TF Serving Coding Part 1
Lecture 80 TF Serving Coding Part 2
Lecture 81 TF Serving Coding Part 3
Lecture 82 Tensorboard Example 1
Lecture 83 Tensorboard Example 2
Lecture 84 Distributed Strategy
Data Scientists who want to apply their knowledge on Real World Case Studies,AI Developers,AI Researchers
Course Information:
Udemy | English | 11h 45m | 7.49 GB
Created by: Dr. Ryan Ahmed, Ph.D., MBA
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