Prior to frequent years when data and computing were so sparse, each data point generated by an organization was not saved and no such data-driven results were accounted for in application design.
But time changes certainly, we have now the plethora of computing and storage assets, the thinking to prioritize the data first and growing volume of data is available for various business applications.
Huge enterprises are developed on a sustainable business model consisting of revenue formation from meaningful insights, dragged out from data. Most encouraging augment in the flood of data and the opportunity of computer power is how we intellectualize complex business problems individually.
Various tools and strategies are in existence for many decades, now highly implemented for addressing complicated business issues (read the article how business analysts use techniques for issues), one of them is Deep learning, having its roots in traditional machine learning algorithms like neural networks that could be operated on a huge amount of data.
Deep neural networks are in high insistence as they have consummated the state-of-the-art methods recently in multiple fields of machine learning.
Machine learning can be defined as the process of inducing intelligence into a system or machine without explicit programming. —Andrew Ng
Let simply understand the formal definition and then engage in the easy way to discuss the topic.
Deep Learning is a subpart of Machine Learning in Artificial Intelligence that operates ML algorithms energized from the biological neuron structure and working of the human brain to assist machines with intelligence.
While deep neural networks are on full rage, the complications of huge work frames act as a barricade to use for new developers to machine learning. There are numerous programs for revised high-level APIs for creating neural network models that appear identical at certain points but show various differences.
Keras is one of the utmost high-level neural networks APIs, where it is written in Python and foothold many backend neural network computation tools.
In this blog, we will learn about the most promising neural networks library, Keras, for deep learning, it gives insight to the easy concept of Keras, its layers, the difference with TensorFlow and various aspects in footing with Deep Learning Neural Networks.
Keras is a compact and accessible-to-understand esteemed Python library for deep learning that can be executed over TensorFlow (or CNTK or Theano). It was developed by a Google engineer named François Chollet. It lets developers fixate on the core concepts of deep learning like constructing layers for neural networks while being concerned with the nitty-gritty of particulars of tensors, their shapes, and mathematical specifics (operations).
The main reason behind using the Keras is about being user friendly, ease of deep adopting and model building, hold a broad range of production implementation selections, accumulation with many backends supports such as TensorFlow, CNTK, Theano, MXNet, and PlaidML and many more, secure foundation to various GPUs and distributed training. It is also endorsed by Google, Microsoft, Nvidia, Amazon, Apple, Uber, and others.
TensorFlow serves as a backend for Keras, one can use Keras for deep learning applications without collaborating with the comparably complex TensorFlow (or CNTK or Theano).
Sequential API: It is based on the concept of sequence of layers, this is the most notorious and elementary part of Keras. It supports designing models layer-by-layer for complex problems with the limitation that Sequential API doesn’t build models that share layers or exhibit multiple inputs and outputs.
Functional API: It can be accounted for as a linear stockpile of layers. It favors designing the same models while providing more flexibility in terms of readability and accessibility. It considers multiple input and output layers along with shared layers that let to construct complex network structures. While using a functional API, one needs to run the previous layer to the present layer that needs the implementation of the input layer.
User-friendliness: Keras is an API, constructed for human beings, not machines. It lays the user experience center and front. It emphasizes the best exercise in reducing cognitive tasks such as it proffers easy and engaged APIs, reduces the action required for general use cases and gives clear and actionable results over user errors.
Easy Extensibility: New components can be added easily like new groups or functions, and even current components provide broad examples like newly added components permit complete articulation that makes Keras a good choice for a breakthrough.
Keras-Flow Chart- Principles and Layers
Modularity: A working model can be described as a sequence or a graph of abandoned, entirely configurable components that fuel jointly with some restrictions. In general, neural networks, cost functions, initialization schemes, activation functions, optimizers, regularization schemes are solo functions that work together to produce new models.
Operate with Python: Possessing no different configuration models data in a predefined format, models are addressed in Python code only which is compact, light to debug, tweak, and gives ease to flexibility.
Keras has a wide collection of predefined layer types and it also supports writing one own layer.
Core Layers: It consists dense (dot product + bias), Activation function (transfer function having neuron shape), Dropout (randomly, fix a part of inputs to zero at a rate of each training update to dodge overfitting), Lambda (bind an arbitrary interpretation as a layer object).
Convolution Layers: The use of filters to design a feature map that executes from 1D to 3D and incorporates most variants like cropping and transposed convolution layers for every dimensionality. 2D convolution that is motivated by the visual cortex is used for image recognition.
Pooling Layers: They are conducted from 1D to 3D that involve variants as max and average pooling. Nearby connected layers serve like convolution layers.
Recurrent Layers: They include simple that are fully connected recurrence, gated, LSTM and other layers that are beneficial for language processing in which Noise Layers aid to evade overfitting.
Keras models are highly implemented over a vast range of platforms (deep learning framework) includes;
In iOS through CoreML
In Android, through TensorFlow Android runtime
In a browser through Keras.js and WebDNN
On Google Cloud through TensorFlow-Serving
In addition, Keras equips ten famous models as Keras Applications that are
They can be accounted for by predicting the classification of images, features extracting, and fine-tuning of models for multiple sets of groups.
For example, fine-tuning can be deployed to speed up training, like, one can add one layer, lock the base layer to train another layer, then unlock a few base layers to fine-tune the training.
Keras provides easy access while writing scripts, there is no need to understand each detail of the backend, TensorFlows offers advanced operations to create models. Find below the major differences between them;
High-level API wrapper
Deploy separate API debug tool like TFDBG
Deploy TensorBoard visualization tool
Expeditious implementation to build models with standard layers.
Enables to produce a random computational graph or model layers.
Straightforward to operate in Python Language.
The requirement to understand and learn the syntax of deploying some TensorFlow Function.
Huge active communities.
Giant functioning communities and extensively shared assets as resources.
Difference between Keras and TensorFlow
Let’s conclude the tutorial by following points;
Keras is a high-level API that is deployed to create deep neural networks accessible with the help of backend tools.
It is easy to implement and attain with Python support.
Its installation is simple and one can adopt any virtual environment or external base for it like AWS.
It has various network models that combinedly make it easier for us to use the convenient model for pre-trained and tweak our own network model.
I hope this tutorial gave deep insights into Keras, its various aspects and differences with TensorFlow. Never miss a single analytical update from Analytics Steps, share this blog on Facebook, Twitter, and LinkedIn.
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