Machine Learning | Deep Learning with Keras and TensorFlow | 2022
About Machine Learning
Machine learning is a subdivision of artificial intelligence. The objective of machine learning is to understand the structure of data sets and fit that into models and utilized by people. Machine learning is an evolving sector. Machine learning is a part of computer science to learn everyday and explore new things, it varies from traditional computational approaches.
About Deep Learning?
Deep Learning is a subset of Machine Learning that helps in achieving Artificial Intelligence. In other words, Deep Learning is an approach where we can make a machine emulate the network of neurons in a human brain to train while performing tasks like speech, image recognition, and Natural Language Processing (NLP). In Deep Learning a network devours a large amount of input data, then processes them all through multiple layers helps in learning complex features of the data.
Applications of Deep Learning are :
- Healthcare medicines and health management system.
- Computer vision and pattern recognition like restoring colors in B&W photos and videos.
- Self-driving cars and making robots that behaves like humans .
- Few Examples of Deep learning are Google Now, Apple’s Siri, Voice activated assistant.
TensorFlow – The open-source software library used in Machine Learning
TensorFlow is an open-source software library used in machine learning and artificial intelligence. A toolkit with low-level functionalities yet high-level operations, designed for Dataflow programming models. TensorFlow is less time consuming and also portable and scalable on a lot of platforms, which means the code runs on CPU, Graphical Processing Units, mobile devices and Tensor Processing Units.
TensorFlow uses Python as their main interface. Its applications are based on mobile and embedded devices, real world projects like numerical computations and robotics. TensorFlow narrates multi-dimensional numerical arrays as graphs without using complex mathematical interpretations.
Organizations Using TensorFlow are:
PayPal, Intel, Google, Uber, Delivery Hero, Bloomberg and many more
Applications are as follows:
- Image Recognize is one of the important parts of TensorFlow in mobile companies, social media and other telecom houses.
- Voice Recognizations have significant use in security systems, search engines for giving orders without using keyboard and mouse.
- No doubt companies are looking for more security hence video detection is being used on a daily basis.
Features of TensorFlow are:
- TensorFlow is best at visualizing each and every part of the graph.
- It is capable of distinguishing the functionalities of a program into independent and interchangeable modules.
- TensorFlow is good at training multiple neural networks and multiple GPU’s where modules are very efficient on large scale systems.
- You can also operate any change while debugging by deploying the TensorFlow.
Keras – A high level neural network
Keras is an open source machine library that provides a Python interface for artificial neural networks. that runs TensorFlow, Theano or Cognitive Toolkit (CNTK). Keras is designed to elaborate deep learning models and also Keras is only an optimal choice for deep learning applications in effective time. Keras is smooth on GPU and CPU with flexibility.
Organizations Using Keras are:
Amazon, Microsoft, Instacart, Apple, JPMorgan Chase and many more
Applications of keras are :
Keras Applications are deep learning models that are used for prediction, feature extraction, and fine-tuning.
Key Features as follows:
- Easy to use and extend with consistent interface
- Composable, fast and easy prototyping
- Multi-platform easy to access and debug
- Continuous network support
Keras vs TensorFlow:
Use Keras when:
- When there’s a need for prototyping
- When working with fresh projects and smaller data sizes
- When there is a need to understand deep learning for better features
- You want to learn quick with easy methods
User TensorFlow when:
- At the time of heavy projects and object detection
- When There’s a broader spectrum of functionalities
- When you work in an industry segment
- At time of high level of performance with good scalability
Who Will be Benefited:
Data analysts, data scientists, statisticians carrying a deep interest in deep learning, machine learning and software engineers.
Key Learning Outcomes:
- Understanding the concepts of Keras and TensorFlow, operations, and execution pipelines
- Implementing deep learning algorithms, understanding neural networks, and traversing different layers of data abstraction
- Master advanced topics such as high-level interfaces, neural networks, training deep networks
- Understand different languages , building deep learning models to interpret results
Machine learning are large areas that submerge a lot of scope in their applications. While Keras is only meant for deep neural networks and TensorFlow is for ML applications.
The choice of framework usually depends upon what kind of project is, their size of the datasets, and their level of skilled resources availability. Keras and TensorFlow together are the best in both worlds. Finally, it is the developer’s choice to finalize which one to proceed with!