Deep_Learning_Frameworks

1. Keras and TensorFlow
Back End
By:Mohamed Essam

2. Setting Up the Environment
We will be developing DL models with the Keras stack
using TensorFlow as a back end in Python. Hence, to get
started we need to set up our playground environment by
installing Python, a few important Python packages,
TensorFlow, and finally Keras. Let’s get started.

3. Installing Keras and TensorFlow
Back End
Now that Python is set up, we need to install TensorFlow
and Keras. Installing packages in Python can be done
easily using the pip, a package manager for Python. You
can install any Python package with the command pip
install package-name in the terminal or command prompt.
So, let’s install our required packages (i.e., TensorFlow and
Keras).

4. Decomposing a DL Model
In its most basic form, DL models are designed using
neural network architecture. A neural network is a
hierarchical organization of neurons (similar to the
neurons in the brain) with connections to other neurons.
These neurons pass a message or signal to other neurons
based on the received input and form a complex network
that learns with some feedback mechanism

5. Decomposing a DL Model
In its most basic form, DL models are designed using
neural network architecture. A neural network is a
hierarchical organization of neurons (similar to the
neurons in the brain) with connections to other neurons.
These neurons pass a message or signal to other neurons
based on the received input and form a complex network
that learns with some feedback mechanism

7. Exploring the Popular DL
Frameworks

8. Low-Level DL
Frameworks
Given the level of abstraction a framework provides,
we can classify it as a low-level or high-level DL
framework. While this is by no means industry recognized
terminology, we can use this segregation for a more
intuitive understanding of the frameworks. The following
are a few of the popular low-level frameworks for DL.
EG:
Theano, Torch,PyTorch,TensorFlow

9. High-Level DL
Frameworks
The previously mentioned frameworks can be defined as
the first level of abstraction for DL models. You would still
need to write fairly long codes and scripts to get your DL
model ready, although much less so than using just Python
or C++. The advantage of using the first-level abstraction is
the flexibility it provides in designing a model.
Eg: Keras…

10. High-Level DL
Frameworks
The previously mentioned frameworks can be defined as
the first level of abstraction for DL models. You would still
need to write fairly long codes and scripts to get your DL
model ready, although much less so than using just Python
or C++. The advantage of using the first-level abstraction is
the flexibility it provides in designing a model.
Eg: Keras…

11. Keras
Keras is a high-level neural network API written in Python
and can help you in developing a fully functional DL
model with less than 15 lines of code. Since it is written in
Python, it has a larger community of users and supporters
and is extremely easy to get started with. The simplicity of
Keras is that it helps users quickly develop DL models and
provides a ton of flexibility while still being a high-level
API. This really makes Keras a special framework to work
with. Moreover, given that it supports several other
frameworks as a back end.

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