Intro to TensorFlow for Deep Learning

3.5

Learners: 171

Instructor: Magnus Hyttsten et al.

Duration:

This Intro to TensorFlow for Deep Learning course is the perfect way to get hands-on experience building state-of-the-art image classifiers and other deep learning models. Developed by the TensorFlow team and Udacity, you'll learn how to use TensorFlow in the real world on mobile devices, in the cloud, and in browsers. Plus, you'll gain advanced techniques and algorithms to work with large datasets. By the end, you'll be ready to create your own AI applications. ▼▲

Course Feature Course Overview Course Provider Discussion and Reviews

Go to class

Course Feature

Cost:

Free

Provider:

Udacity

Certificate:

No Information

Language:

English

Start Date:

On-Demand

Course Overview

❗The content presented here is sourced directly from Udacity platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [April 29th, 2023]

This course, Intro to TensorFlow for Deep Learning, provides an introduction to the TensorFlow library and its use for deep learning. Students will gain hands-on experience building their own state-of-the-art image classifiers and other deep learning models. They will also learn how to use their TensorFlow models in the real world on mobile devices, in the cloud, and in browsers. Finally, students will use advanced techniques and algorithms to work with large datasets. Upon completion of the course, students will have the skills necessary to start creating their own AI applications.

[Applications]
After completing this course, students can apply their knowledge of TensorFlow for Deep Learning to create their own AI applications. They can use their skills to build state-of-the-art image classifiers and other deep learning models, as well as use advanced techniques and algorithms to work with large datasets. Additionally, they can deploy their models to mobile devices, the cloud, and browsers.

[Career Paths]
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use a variety of tools and techniques to build, test, and deploy models that can be used to solve real-world problems. They must have a strong understanding of mathematics, statistics, and computer science, as well as experience with programming languages such as Python and TensorFlow. As the demand for AI applications continues to grow, the demand for Machine Learning Engineers is expected to increase.

2. Data Scientist: Data Scientists use data to uncover insights and trends. They use a variety of tools and techniques to analyze data and develop models that can be used to make predictions and decisions. They must have a strong understanding of mathematics, statistics, and computer science, as well as experience with programming languages such as Python and TensorFlow. As the demand for AI applications continues to grow, the demand for Data Scientists is expected to increase.

3. AI Developer: AI Developers are responsible for developing AI applications. They use a variety of tools and techniques to build, test, and deploy AI applications. They must have a strong understanding of mathematics, statistics, and computer science, as well as experience with programming languages such as Python and TensorFlow. As the demand for AI applications continues to grow, the demand for AI Developers is expected to increase.

4. Deep Learning Engineer: Deep Learning Engineers are responsible for developing and deploying deep learning models. They use a variety of tools and techniques to build, test, and deploy models that can be used to solve real-world problems. They must have a strong understanding of mathematics, statistics, and computer science, as well as experience with programming languages such as Python and TensorFlow. As the demand for AI applications continues to grow, the demand for Deep Learning Engineers is expected to increase.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path provides students with a comprehensive understanding of computer science fundamentals, including programming, algorithms, data structures, and software engineering. Students will also learn about the latest technologies and trends in the field, such as artificial intelligence, machine learning, and deep learning.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of artificial intelligence systems and their applications. Students will learn about the fundamentals of AI, including machine learning, deep learning, natural language processing, and computer vision. They will also gain experience in developing AI applications and systems.

3. Master of Science in Data Science: This degree path focuses on the analysis and interpretation of large datasets. Students will learn about data mining, machine learning, and predictive analytics. They will also gain experience in developing data-driven applications and systems.

4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of machine learning algorithms and their applications. Students will learn about the fundamentals of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning. They will also gain experience in developing machine learning applications and systems.

Course Syllabus

Introduction to Machine Learning

Get a high-level overview of artificial intelligence and machine learning,Learn how machine learning and deep learning have revolutionized software

Your First Model: Fashion MNIST

Build a neural network that can recognize images of articles of clothing

Introduction to Convolutional Neural Networks ("CNNs")

Use a convolutional network to build more efficient models for Fashion MNIST

Going Further with CNNs

Expand your image classifiers into models that can predict from multiple classes,Use a convolutional network to build a classifier for more detailed color images

Transfer Learning

Use a pre-trained network to build powerful state-of-the-art classifiers

Saving and Loading Models

Look at the new SAVEDMODEL format in TensorFlow 2.0 and take advantage of it for TensorFlow-Lite and TensorFlow-Serving

Time Series Forecasting

Learning from sequential data with recurrent neural networks

Natural Language Processing

Tokenize words and create embeddings for using text data with neural networks,Build recurrent neural networks, such as LSTMs, for improved NLP models,Generate new text for tasks like novel song lyrics

Introduction to TensorFlow Lite

Learn how you can use TensorFlow lite to build machine learning apps on Android, iOS and iOT devices