Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to TensorFlow: Introduction

The video tutorial introduces TensorFlow, a Python package for deep learning, and compares it with PyTorch. It discusses the installation of TensorFlow using Anaconda and Pip, highlighting potential conflicts. The tutorial suggests using Google Colab as an alternative to avoid installation issues, offering a cloud-based environment with pre-installed TensorFlow and GPU support.

What is one of the main advantages of using TensorFlow for deep learning?

• It requires no coding knowledge.

• It provides a high-level interface for complex models.

• It is only available for GPU usage.

• It is the only package for deep learning.

Which of the following is a common use of PyTorch?

• In industry for production systems

• For mobile app development

• In academia for research purposes

• For web development

What is a recommended method to install TensorFlow to avoid conflicts?

• Install directly on the main system

• Use a virtual environment with Anaconda

• Use a third-party installer

• Install using a custom script

What is a potential issue when installing TensorFlow using pip?

• It always installs the wrong version.

• It may cause conflicts with Python variants.

• It requires a paid license.

• It only works on Windows systems.

What is a key benefit of using Google Colab for TensorFlow?

• It provides pre-installed packages and GPU support.

• It is only available for TensorFlow 1.x.

• It requires no internet connection.

• It does not support Python code.

How can you check the TensorFlow version in Google Colab?

• By running a system command

• By looking at the notebook title

• By using the command TF.version

• By checking the browser console

What is a limitation of the free version of Google Colab?

• It does not support TensorFlow.

• It has limited GPU resources.

• It cannot run Python code.

• It requires a subscription for basic features.