Welcome to this course on TensorFlow 2.0 for Deep Learning!
In this course, you will be learning all about the TensorFlow 2.0 library open-sourced by Google. Using the library, you will also learn how to build multiple Machine Learning and Deep Learning models.
Objectives of the course
The learning objectives of the course are set out as follows:
- Learn how to install and import TensorFlow 2.0 library in Python
- Learn about Tensor and Tensor Operations
- Learn how to perform Linear and Logistic Regression using TensorFlow 2.0
- Learn how to build your own Dense Neural Network, Convolutional Neural Network and Recurrent Neural Network.
You can expect to have all of these objectives met by the time you reach the end of this course.
Pre-requisites for the course
If this is your first time working on Python, it may be hard for you to effectively grasp all the concepts. Therefore, the following pre-requisites are necessary for you to get the best out of the course:
- Solid understanding of the Python programming language
- Familiar with Pandas, Matplotlib and Numpy
- Solid understanding with the theoretical concepts of Deep Learning
If you do not have the above pre-requisites, don’t worry! You can always come back later to this course once you are ready.
Best way to work through the course
The course is not long but requires a good amount of attention from your end.
Before moving to the next lecture, we suggest you to set up your coding environment and open up your Jupyter Notebook. If you are a more advanced user of Python and have your own preferences, please feel free to choose an IDE that you prefer. However, all of the coding examples will be written for execution on Jupyter Notebook cells.
If you come across any problem, please check to see if your code matches exactly with the course or not. If you still are facing errors or have some doubts, please provide your question through the comment section of the specific chapter you are stuck on.
We also recommend you join our community and get connected to our vibrant network of data science aspirants. Once you are in the community, you can share your learnings, form a study group, or even get help building a project around Deep Learning using Tensorflow 2.0.
All good? Okay! Let us head on to the first lesson of the course.
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