In this blog, we will be discussing the different Deep Learning Frameworks available like Tensorflow, Keras and Pytorch. We will also see how each of these frameworks works and their pros and cons.

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## Introduction to Deep Learning

Deep learning is a subset of machine learning in artificial intelligence (AI) that has a neural network architecture as its core. Neural networks are a type of machine learning algorithm that are used to model complex patterns in data. Deep learning algorithms learn through a process of successive abstraction, whereby the algorithm learns increasingly complex representations of the data at each layer in the network.

Deep learning algorithms have been shown to be effective at a range of tasks including image classification, object detection, and natural language processing. In recent years, there has been a resurgence of interest in neural networks and deep learning due to the increasing availability of data and computational power.

There are a number of different deep learning frameworks available, each with its own strengths and weaknesses. In this course, we will be using TensorFlow Keras, which is a high-level API for building and training deep learning models. We will also be using PyTorch, which is another popular deep learning framework.

## What is TensorFlow?

TensorFlow is a free and open-source software library for data analysis and machine learning. Originally developed by Google Brain Team, TensorFlow is widely used for deep learning applications such as image classification, natural language processing, and predictive analytics.

## What is Keras?

Keras is a high-level deep learning API that is designed to make it easy to build and train deep learning models. It is written in Python and allows you to create models by stacking layers of neurons (called “layers”). Keras is free and open source, and can be used on top of TensorFlow, Theano, or Microsoft CNTK.

## What is PyTorch?

PyTorch is a python library for deep learning created by Facebook’s artificial intelligence research group. It is used for applications such as natural language processing.

## Deep Learning with TensorFlow

Deep learning is a subset of machine learning in which neural networks, algorithms inspired by the brain, learn to perform tasks by considering examples, generally without being programmed with any task-specific rules. For example, deep learning can be used to automatically identify objects in photographs or video, or to transcribe spoken words into text.

The term “deep” refers to the number of layers in the network—the more layers, the deeper the network. Deep learning is a very powerful tool for performing complex tasks, but it is also computationally intensive and can require large amounts of training data.

TensorFlow is an open source software library for numerical computation using data flow graphs. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab (FAIR).

## Deep Learning with Keras

Deep Learning is a subset of machine learning that uses algorithms inspired by the brain’s structure and function. Deep learning is a powerful tool for building complex models from large amounts of data.

Keras is a deep learning library that wrapped around existing neural network libraries such as TensorFlow and Theano. Keras makes it easy to build deep learning models without having to write a lot of code.

Pytorch is another deep learning library that is popular for its flexibility and ease of use. Pytorch also has a strong community backing and many developers have created helpful resources for getting started with deep learning using Pytorch.

## Deep Learning with PyTorch

Deep learning is a branch of machine learning that uses algorithms to model high-level abstractions in data. By using a deep learning algorithm, a computer can learn to recognize patterns of data, such as images or sound. Deep learning is a similar process to the way humans learn; we learn by example. For example, if we want to learn to identify animals, we will look at many pictures of animals and try to find common patterns.

Deep learning algorithms are able to learn from data in a way that is similar to the way humans learn. This is because deep learning algorithms are able to learn multiple layers of abstraction. The first layer of abstraction might be able to identify edges in an image. The second layer might be able to identify shapes. The third layer might be able to identify objects. And so on.

Deep learning algorithms are able to learn from data in a way that is similar to the way humans learn. This is because deep learning algorithms are able to learn multiple layers of abstraction. The first layer of abstraction might be able to identify edges in an image. The second layer might be able to identify shapes. The third layer might be able to identify objects. And so on.

Deep learning is a powerful tool for understanding and working with data. By using deep learning, we can build models that can automatically extract features from raw data and make predictions about unseen data. Deep learning is used in many different fields, such as computer vision, natural language processing, and predictive analytics

## Comparison of TensorFlow, Keras and PyTorch

TensorFlow, Keras and PyTorch are all deep learning frameworks. They are all open source and free to use.

TensorFlow is developed by Google Brain and released under the Apache 2.0 open source license. Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. PyTorch is an open source machine learning library for Python, based on Torch, used for applications such as natural language processing.

TensorFlow and PyTorch are both excellent choices for building deep learning models, but they have some important differences. TensorFlow is better suited for large-scale applications and PyTorch is better suited for smaller projects or projects that require more flexibility. Keras can be used with either TensorFlow or PyTorch, but it is most commonly used with TensorFlow.

## Advantages and Disadvantages of Deep Learning

Deep learning is a subset of machine learning that is inspired by the structure and function of the brain. Deep learning algorithms are able to learn complex patterns in data and make predictions about new data.

There are many advantages to using deep learning, including the ability to learn complex patterns, the ability to handle high-dimensional data, and the ability to make predictions about new data. However, deep learning also has some disadvantages, including the potential for overfitting, the need for large amounts of training data, and the potential for high computational costs.

## Applications of Deep Learning

Deep learning is a branch of machine learning that is inspired by the structure and function of the brain. It is a relatively new area of machine learning, but it has already had a huge impact on the field.

Deep learning algorithms are able to learn from data in a way that is similar to the way humans learn. This allows them to generalize from data in a way that is not possible with other types of machine learning algorithms.

Deep learning algorithms have been used to achieve state-of-the-art results in many different fields, including computer vision, natural language processing, and robotics.

Deep learning algorithms are also being used to develop new applications such as self-driving cars and medical diagnosis.

Keyword: Deep Learning with Tensorflow Keras and Pytorch