Summary of "Pytorch vs Tensorflow vs Keras | Deep Learning Tutorial 6 (Tensorflow Tutorial, Keras & Python)"

Summary of Main Ideas

Overview of Frameworks
- The video discusses the differences between three popular deep learning frameworks: PyTorch, TensorFlow, and Keras.
- PyTorch is developed by Facebook, while TensorFlow is developed by Google.
- A lesser-known framework, CNTK (Microsoft Cognitive Toolkit), is mentioned but is not as popular as the first two.
Understanding Keras
- Keras is not a full-fledged deep learning framework like PyTorch and TensorFlow; instead, it serves as a high-level wrapper around these frameworks (as well as CNTK and Theano).
- The purpose of Keras is to simplify the coding experience for users who find direct programming in TensorFlow or CNTK challenging.
Integration of Keras with TensorFlow
- With the introduction of TensorFlow 2.0, Keras has been integrated into the TensorFlow library, allowing users to access Keras functionalities directly from TensorFlow.
- There is no need to install Keras separately; it is included within TensorFlow.
Code Snippets
- The video includes code snippets demonstrating how to import and use Keras within TensorFlow.
- Previously, Keras allowed users to specify different backends (like CNTK or Theano), but the tutorial focuses on using TensorFlow as the backend.

Installation
- To install Keras, use the command: pip install Keras.
Using Keras with TensorFlow
- Import Keras directly from TensorFlow instead of installing it separately.
- Use the convenient APIs provided by Keras integrated within TensorFlow for deep learning tasks.