TensorFlow 2.0 Practical
Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 10 practical projects
4.50 (942 reviews)
8 644
students
12 hours
content
Jan 2025
last update
$84.99
regular price
What you will 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
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Comidoc Review
Our Verdict
This course is a valuable resource for beginners looking to familiarize themselves with TensorFlow 2.0 and its practical implementations in machine learning projects. However, potential learners should be aware of their own background preparation regarding some basic concepts in calculus and machine learning. Enthusiastic instructors often inspire and motivate students, yet clearer explanations in specific areas will benefit overall understanding. Despite minor concerns about occasional imprecise language or deprecated content in later projects, course participants will undoubtedly appreciate the opportunity to enhance their skillset through hands-on exercises and exposure to tools like TensorBoard.
What We Liked
- Comprehensive coverage of TensorFlow 2.0 and ANNs, with 10 practical projects that offer useful references for future model training.
- Passionate and knowledgeable instructor enhances learning experience.
- Well-paced modules and good balance between theory and practice.
- Excellent introduction to Google Colab and TensorBoard.
Potential Drawbacks
- Prerequisites should include some background in machine learning, calculus, data cleaning, and visualization for better preparedness.
- Instructor's language could be more precise and less convoluted.
- Later projects lack updates to new libraries, causing potential issue with completion.
- Some concepts are not explained in-depth, leaving learners desiring more details on decision-making processes.
2517884
udemy ID
20/08/2019
course created date
03/11/2019
course indexed date
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