Master Deep Learning with TensorFlow in Python
This course provides an in-depth look at Deep Learning with TensorFlow in Python. Learn to create your first algorithm, get acquainted with Google's TensorFlow, explore layers and activations, understand the backpropagation process, spot and prevent overfitting, and implement cutting-edge optimizations. Master the fundamentals of Deep Learning and become a TensorFlow expert. ▼
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Course Feature
Cost:
Paid
Provider:
Udemy
Certificate:
No Information
Language:
English
Start Date:
Self Paced
Course Overview
❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [June 30th, 2023]
This course provides an introduction to the fundamentals of deep learning with TensorFlow in Python. Participants will learn how to create their first algorithm, become familiar with Google's TensorFlow and its structure, and explore layers, their building blocks, and their activations. The backpropagation process will be discussed both intuitively and mathematically, and participants will learn how to spot and prevent overfitting. Additionally, the course will cover state-of-the-art initialization methods, cutting-edge optimizations such as SGD, batching, and learning rate schedules, and how to tackle the ‘Hello, world’ of machine learning.
[Applications]
After completing this course, students can apply their knowledge of TensorFlow and Python to create their own algorithms. They can use the layers, building blocks, and activations they learned about to create more complex models. They can also use the backpropagation process to optimize their models and prevent overfitting. Additionally, students can use the state-of-the-art initialization methods and cutting-edge optimizations, such as SGD, batching, and learning rate schedules, to further improve their models. Finally, they can use the knowledge they gained to tackle the ‘Hello, world’ of machine learning.
[Career Paths]
One job position path that is recommended for learners of this course is a Deep Learning Engineer. Deep Learning Engineers are responsible for developing and deploying deep learning models and algorithms to solve complex problems. They must have a strong understanding of machine learning and deep learning concepts, as well as the ability to use TensorFlow and Python to create and deploy deep learning models.
The development trend for Deep Learning Engineers is to become more specialized in their field. As deep learning technology advances, Deep Learning Engineers will need to stay up to date on the latest developments and be able to apply them to their work. Additionally, Deep Learning Engineers will need to be able to work with a variety of data sources and be able to develop models that can be used in a variety of applications.
[Education Paths]
The recommended educational path for learners of this course is to pursue a Master's degree in Deep Learning with TensorFlow in Python. This degree program would cover topics such as the fundamentals of deep learning, the basics of TensorFlow, and the application of TensorFlow in various projects. It would also cover topics such as layers, activations, backpropagation, overfitting, initialization methods, and optimization techniques.
The development trend of this degree program is to focus on the practical application of deep learning and TensorFlow. This means that students will be expected to have a good understanding of the theoretical concepts, but also be able to apply them in real-world projects. This will involve learning how to use TensorFlow to create algorithms, build models, and deploy them in production. Additionally, students will be expected to have a good understanding of the latest advancements in deep learning and TensorFlow, such as new layers, activations, and optimization techniques.
Pros & Cons
Clearly explains concepts, on point.
Excellent mix of theory and practical approach.
Provides mathematical reasoning behind neural networks.
Well-structured with concrete contents.
Great for beginners to understand theory and implementation.
Organizes the learning track effectively.
Appreciated efforts of the course creators.
Doesn't cover basics of TensorFlow.
Examples could be more easy to understand.
Some bugs in setting up environment or loading data.
Course Provider
Provider Udemy's Stats at AZClass
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