TensorFlow 2.0 Practical
4.0 /5- Offered byUDEMY
TensorFlow 2.0 Practical at UDEMY Overview
Duration | 12 hours |
Total fee | ₹499 |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
TensorFlow 2.0 Practical at UDEMY Highlights
- Compatible on Mobile and TV
- Earn a Cerificate on successful completion
- Get Full Lifetime Access
TensorFlow 2.0 Practical at UDEMY Course details
- Data Scientists who want to apply their knowledge on Real World Case Studies
- AI Developers
- AI Researchers
- 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
- 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 Serving The 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.
TensorFlow 2.0 Practical at UDEMY Curriculum
INTRODUCTION AND COURSE OUTLINE
Introduction and Welcome Message
Course Overview
What's AI, ML and DL
Machine Learning - Big Picture
Whats new in TF2 and Google Colab
Whats New in TensorFlow 2.0
What is Google Colab
Google Colab Demo
Eager Execution
Keras API
BUILD YOUR FIRST SIMPLE PERCEPTRON (SINGLE NEURON) MODEL IN TF 2.0
PROJECT #1 OVERVIEW: CONVERT CELSUIS TO FAHRENHEIT
PROJECT #1 What are ANNs and How they learn?
PROJECT #1 Build our first ANN model
PROJECT #1 TF Playground
PROJECT #1 Coding Step 1 - Load TF and Data
PROJECT #1 Coding Step 2 - Model Training
PROJECT #1 Coding Step 3 - Model Evaluation
PROJECT #2 Overview
PROJECT#2: Google Colab Questions Overview
PROJECT # 2 Coding Part 1
PROJECT # 2 Coding Part 2
PROJECT # 2 Coding Part 3
BUILD A MULTI LAYER ARTIFICIAL NEURAL NETWORKS FOR REGRESSION TASKS
PROJECT #3: Overview
PROJECT #3 Regression basics
PROJECT #3 ANN in Action
PROJECT #3 Activation functions overview
PROJECT #3 MultiLayer Perceptron Network
PROJECT #3 ANN Training and Epochs Definition
PROJECT #3 Tensorflow Playground 3
PROJECT #3 Gradient Descent
PROJECT #3 Back Propagation
PROJECT #3 Bias Variance Tradeoff
PROJECT #3 Performance Metrics
PROJECT #3 Coding part 1
PROJECT #3 Coding part 2
PROJECT #3 Coding part 3
PROJECT #3 Coding part 4
PROJECT #3 Coding part 5 - Training
PROJECT #3 Coding part 6
PROJECT #4 Overview
PROJECT #4 Google Colab Overview
PROJECT #4 Coding Part 1
PROJECT #4 Coding Part 2
PROJECT #4 Coding Part 3
ARTIFICIAL NEURAL NETWORKS FOR CLASSIFICATION TASKS
PROJECT #5 Project Overview sentiment
PROJECT #5 Tokenization and Count Vectorizer
PROJECT #5 Confusion Matrix
PROJECT #5 Load Dataset
PROJECT #5 Data Visualization
PROJECT #5 Data Tokenization
PROJECT #5 Model Building and Training
PROJECT #5 Model Evaluation
PROJECT #6 Project Overview
PROJECT #6 Google Colab Project Questions Overview
PROJECT #6 Google Colab Project Questions Overview 2
PROJECT #6 Project Coding Solution Part 1
PROJECT #6 Project Coding Solution Part 2
DEEP LEARNING FOR IMAGE CLASSIFICATION
PROJECT #7 Overview
PROJECT #7 CNN Entire Network Overview
PROJECT #7 Feature Detectors
PROJECT #7 RELU
PROJECT #7 Pooling and Downsampling
PROJECT #7 Performance Improvement
PROJECT #7 Coding part 1 Import Data
PROJECT #7 Coding part 2 Visualization
PROJECT #7 Coding part 3 Train model
PROJECT #7 Coding part 4 - Evaluate model
PROJECT #8 Project Overview
PROJECT #8 LeNet Architecture
PROJECT #8 Coding part 1
PROJECT #8 Coding part 2
PROJECT #8 Coding part 3
PROJECT #9 Overview
PROJECT #9 Questions Overview
PROJECT #9 Solution Part 1
PROJECT #9 Solution Part 2
MODEL DEPLOYMENT USING TF SERVING
TF Serving Coding Part 1
TF Serving Coding Part 2
TF Serving Coding Part 3
Tensorboard Example 1
Tensorboard Example 2
Distributed Strategy
BONUSES Get them fast, content will be removed on Friday, 13 SEP. 12PM NY time
Bonus #1
Bonus #2
BONUS #3 - HAPPY FACES CLASSIFICATION USING DEEP LEARNING
BONUS #4 - DEEP LEARNING MODEL DEPLOYMENT USING TF 2.0 SERVING
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