Keras is a high level framework that runs on top of AI library such as Tensorflow, Theano, or CNTK. The key feature of Keras is that it allow to switch out the underlying library without performing any code changes. Keras contains commonly used neural-network building blocks such as layers, optimizer, activation functions etc and keras has support for convolutional and recurrent neural networks. In addition keras contains datasets and some pre-trained deep learnig applications that make it easier to learn for beginners. Essentially Keras is democrasting deep learning by reducing barrier into deep learning.
TensorFlow and Keras are popular deep learning frameworks. TensorFlow is an open source library for numerical computation using data flow graphs. It was developed by Google and is widely used for machine learning and deep learning. Keras is a higher-level neural network API that can run on top of TensorFlow. It focuses on user-friendliness, modularization and extensibility. Both frameworks make building and training neural networks easier through modular layers and built-in optimization algorithms.
1) The document provides a tutorial on Keras with the objective of introducing deep learning and how to use the Keras library in Python.
2) The tutorial covers installing Keras, configuring backends, an overview of deep learning concepts, and how to build models using Keras modules, layers, model compilation, CNNs, and LSTMs.
3) Examples are provided on creating simple Keras models using different layers like Dense, Dropout, and Flatten to demonstrate how models can be built for classification tasks.
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow, CNTK or Theano.
We can easily build a model and train it using keras very easily with few lines of code.The steps to train the model is described in the presentation.
Use Keras if you need a deep learning library that:
-Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
-Supports both convolutional networks and recurrent networks, as well as combinations of the two.
-Runs seamlessly on CPU and GPU.
The anatomy of a neural network consists of layers, input data and targets, a loss function, and an optimizer. Layers are the building blocks and include dense, RNN, CNN, and more. Keras is a user-friendly deep learning framework that allows easy construction of neural networks by stacking layers. It supports TensorFlow as a backend and offers pre-trained models, GPU acceleration, and integration with data libraries. To set up a deep learning workstation, software like TensorFlow, Keras, and CUDA must be installed along with a GPU. The hypothesis space refers to all possible models considered by an algorithm. Loss functions measure prediction error while optimizers adjust parameters to minimize loss and improve accuracy. Common examples are described.
The document discusses Keras, a Python deep learning library that allows for easy and fast prototyping of convolutional and recurrent neural networks. It presents an outline for a talk that introduces deep learning concepts and architectures like CNNs and RNNs. It then demonstrates how to model applications in image recognition, simulated car control, and speech recognition using Keras' simple API and layers. Code walkthroughs and demos are provided for each application.
First steps with Keras 2: A tutorial with ExamplesFelipe
In this presentation, we give a brief introduction to Keras and Neural networks, and use examples to explain how to build and train neural network models using this framework.
Talk given as part of an event by Rio Machine Learning Meetup.
This document provides instructions on how to install Keras on a machine. It recommends installing Keras within a virtual environment for package management. It describes downloading prerequisites like NumPy, Pandas, SciKit-learn and Matplotlib using pip. It also recommends creating a conda environment as an alternative to a virtual environment for installing Keras and its dependencies. The steps include activating the environment before installing Keras using pip or conda.
TensorFlow meetup: Keras - Pytorch - TensorFlow.jsStijn Decubber
Slides from the TensorFlow meetup hosted on October 9th at the ML6 offices in Ghent. Join our Meetup group for updates and future sessions: https://www.meetup.com/TensorFlow-Belgium/
This document provides an introduction to time series modeling using deep learning with TensorFlow and Keras. It discusses machine learning and deep learning frameworks like TensorFlow and Keras. TensorFlow is an open source library for numerical computation using data flow graphs that can run on CPUs, GPUs, and distributed systems. Keras is a higher-level API that provides easy extensibility and works with Python. The document also covers neural network concepts like convolutional neural networks and recurrent neural networks as well as how to get started with time series modeling using these techniques in TensorFlow and Keras.
The document discusses transfer learning and building complex models using Keras and TensorFlow. It provides examples of using the functional API to build models with multiple inputs and outputs. It also discusses reusing pretrained layers from models like ResNet, Xception, and VGG to perform transfer learning for new tasks with limited labeled data. Freezing pretrained layers initially and then training the entire model is recommended for transfer learning.
Keras Core is a rewrite of Keras that supports multiple backends including TensorFlow, JAX, and PyTorch. It allows users to write models once and run them with any supported backend, avoiding lock-in. Key features include distributed training APIs, pretrained models, and seamless integration with backend-specific workflows while maintaining Keras' simplicity. Keras Core aims to maximize performance, ecosystem support, and addressable market for deep learning models.
The document discusses setting up and using Keras and TensorFlow libraries for machine learning. It provides instructions on installing the libraries, preparing data, defining a model with sequential layers, compiling the model to configure the learning process, training the model on data, and evaluating the trained model on test data. A sample program is included that uses a fashion MNIST dataset to classify images into 10 categories using a simple sequential model.
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017StampedeCon
This technical session provides a hands-on introduction to TensorFlow using Keras in the Python programming language. TensorFlow is Google’s scalable, distributed, GPU-powered compute graph engine that machine learning practitioners used for deep learning. Keras provides a Python-based API that makes it easy to create well-known types of neural networks in TensorFlow. Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to train neural networks of much greater complexity. Deep learning allows a model to learn hierarchies of information in a way that is similar to the function of the human brain.
Deep learning is a type of machine learning that uses artificial neural networks to learn from data. Neural
networks are inspired by the human brain and are able to learn complex patterns from data. This makes them
well-suited for natural language processing (NLP), which is the field of computer science that deals with the
interaction between computers and human (natural) languages.
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
To interactively experience a deep learning network carrying out a machine vision task in real time, navigate
to quickdraw.withgoogle.com to play the Quick, Draw! game. Click Let’s Draw! to begin playing the game.
You will be prompted to draw an object, and a deep learning algorithm will guess what you sketch.
A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful.
Keras with Tensorflow backend can be used for neural networks and deep learning in both R and Python. The document discusses using Keras to build neural networks from scratch on MNIST data, using pre-trained models like VGG16 for computer vision tasks, and fine-tuning pre-trained models on limited data. Examples are provided for image classification, feature extraction, and calculating image similarities.
Keras is a Python deep learning library that provides a simple and easy to use API for developing and evaluating neural network models. It can be used for tasks like binary classification, multi-class classification, regression, and more. Keras allows you to quickly build models with common neural network layers and train them on data.
apidays New York 2025 - Building Scalable AI Systems by Sai Prasad Veluru (Ap...apidays
Building Scalable AI Systems: Cloud Architecture for Performance
Sai Prasad Veluru, Software Engineer at Apple Inc
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
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Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
The document discusses Keras, a Python deep learning library that allows for easy and fast prototyping of convolutional and recurrent neural networks. It presents an outline for a talk that introduces deep learning concepts and architectures like CNNs and RNNs. It then demonstrates how to model applications in image recognition, simulated car control, and speech recognition using Keras' simple API and layers. Code walkthroughs and demos are provided for each application.
First steps with Keras 2: A tutorial with ExamplesFelipe
In this presentation, we give a brief introduction to Keras and Neural networks, and use examples to explain how to build and train neural network models using this framework.
Talk given as part of an event by Rio Machine Learning Meetup.
This document provides instructions on how to install Keras on a machine. It recommends installing Keras within a virtual environment for package management. It describes downloading prerequisites like NumPy, Pandas, SciKit-learn and Matplotlib using pip. It also recommends creating a conda environment as an alternative to a virtual environment for installing Keras and its dependencies. The steps include activating the environment before installing Keras using pip or conda.
TensorFlow meetup: Keras - Pytorch - TensorFlow.jsStijn Decubber
Slides from the TensorFlow meetup hosted on October 9th at the ML6 offices in Ghent. Join our Meetup group for updates and future sessions: https://www.meetup.com/TensorFlow-Belgium/
This document provides an introduction to time series modeling using deep learning with TensorFlow and Keras. It discusses machine learning and deep learning frameworks like TensorFlow and Keras. TensorFlow is an open source library for numerical computation using data flow graphs that can run on CPUs, GPUs, and distributed systems. Keras is a higher-level API that provides easy extensibility and works with Python. The document also covers neural network concepts like convolutional neural networks and recurrent neural networks as well as how to get started with time series modeling using these techniques in TensorFlow and Keras.
The document discusses transfer learning and building complex models using Keras and TensorFlow. It provides examples of using the functional API to build models with multiple inputs and outputs. It also discusses reusing pretrained layers from models like ResNet, Xception, and VGG to perform transfer learning for new tasks with limited labeled data. Freezing pretrained layers initially and then training the entire model is recommended for transfer learning.
Keras Core is a rewrite of Keras that supports multiple backends including TensorFlow, JAX, and PyTorch. It allows users to write models once and run them with any supported backend, avoiding lock-in. Key features include distributed training APIs, pretrained models, and seamless integration with backend-specific workflows while maintaining Keras' simplicity. Keras Core aims to maximize performance, ecosystem support, and addressable market for deep learning models.
The document discusses setting up and using Keras and TensorFlow libraries for machine learning. It provides instructions on installing the libraries, preparing data, defining a model with sequential layers, compiling the model to configure the learning process, training the model on data, and evaluating the trained model on test data. A sample program is included that uses a fashion MNIST dataset to classify images into 10 categories using a simple sequential model.
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017StampedeCon
This technical session provides a hands-on introduction to TensorFlow using Keras in the Python programming language. TensorFlow is Google’s scalable, distributed, GPU-powered compute graph engine that machine learning practitioners used for deep learning. Keras provides a Python-based API that makes it easy to create well-known types of neural networks in TensorFlow. Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to train neural networks of much greater complexity. Deep learning allows a model to learn hierarchies of information in a way that is similar to the function of the human brain.
Deep learning is a type of machine learning that uses artificial neural networks to learn from data. Neural
networks are inspired by the human brain and are able to learn complex patterns from data. This makes them
well-suited for natural language processing (NLP), which is the field of computer science that deals with the
interaction between computers and human (natural) languages.
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
To interactively experience a deep learning network carrying out a machine vision task in real time, navigate
to quickdraw.withgoogle.com to play the Quick, Draw! game. Click Let’s Draw! to begin playing the game.
You will be prompted to draw an object, and a deep learning algorithm will guess what you sketch.
A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful.
Keras with Tensorflow backend can be used for neural networks and deep learning in both R and Python. The document discusses using Keras to build neural networks from scratch on MNIST data, using pre-trained models like VGG16 for computer vision tasks, and fine-tuning pre-trained models on limited data. Examples are provided for image classification, feature extraction, and calculating image similarities.
Keras is a Python deep learning library that provides a simple and easy to use API for developing and evaluating neural network models. It can be used for tasks like binary classification, multi-class classification, regression, and more. Keras allows you to quickly build models with common neural network layers and train them on data.
apidays New York 2025 - Building Scalable AI Systems by Sai Prasad Veluru (Ap...apidays
Building Scalable AI Systems: Cloud Architecture for Performance
Sai Prasad Veluru, Software Engineer at Apple Inc
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
What is FinOps as a Service and why is it Trending?Amnic
The way we build and scale companies today has changed forever because of cloud adoption. However, this flexibility introduces unpredictability, which often results in overspending, inefficiencies, and a lack of cost accountability.
FinOps as a Service is a modern approach to cloud cost management that combines powerful tooling with expert advisory to bring financial visibility, governance, and optimization into the cloud operating model, without slowing down the engineering team. FinOps empowers the engineering team, finance, and leadership/management as they make data-informed decisions about cost, together.
In this presentation, we will break down what FinOps is, why it matters more than ever, and a little about how a managed FinOps service can help organizations:
- Optimize cloud spend - without slowing down dev
- Create visibility into the cost per team, service, or feature
- Set financial guardrails while allowing autonomy in engineering
- Drive cultural alignment between finance, engineering, and product
This will guide and help whether you are a cloud-native startup or a scaling enterprise, and convert cloud cost into a strategic advantage.
apidays New York 2025 - Breaking Barriers: Lessons Learned from API Integrati...apidays
Breaking Barriers: Lessons Learned from API Integration with Large Hotel Chains and the Role of Standardization
Constantine Nikolaou, Manager Business Solutions Architect at Booking.com
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
Report based on the findings of a quantitative research conducted by the research agency New Image Marketing Group, commissioned by the NGO Detector Media, compiled by PhD in Sociology Marta Naumova.
apidays New York 2025 - CIAM in the wild by Michael Gruen (Layr)apidays
CIAM in the wild: What we learned while scaling from 1.5 to 3 million users
Michael Gruen, VP of Engineering at Layr
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
Convene 360 Madison, New York
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
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Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
apidays New York 2025 - Open Source and disrupting the travel distribution ec...apidays
Open Source and disrupting the travel distribution ecosystem
Stu Waldron, Advisor & Acting Director at OpenTravel
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
Convene 360 Madison, New York
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
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https://apilandscape.apiscene.io/
apidays New York 2025 - Fast, Repeatable, Secure: Pick 3 with FINOS CCC by Le...apidays
Fast, Repeatable, Secure: Pick 3 with FINOS CCC
Leigh Capili, Kubernetes Contributor at Control Plane
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
May 14 & 15, 2025
------
Check out our conferences at https://www.apidays.global/
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AG-FIRMA FINCOME ARTICLE AI AGENT RAG.pdfAnass Nabil
AI CHAT BOT Design of a multilingual AI assistant to optimize agricultural practices in Morocco
Delivery service status checking
Mobile architecture + orchestrator LLM + expert agents (RAG, weather,sensors).
apidays New York 2025 - Boost API Development Velocity with Practical AI Tool...apidays
Boost API Development Velocity with Practical AI Tooling
Sumit Amar, VP of Engineering at WEX
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
May 14 & 15, 2025
------
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apidays New York 2025 - Two tales of API Change Management by Eric Koleda (Coda)apidays
Two tales of API Change Management from my time at Google
Eric Koleda, Developer Advocate at Coda
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
Convene 360 Madison, New York
May 14 & 15, 2025
------
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At Opsio, we specialize in delivering advanced cloud services that enable businesses to scale, transform, and modernize with confidence. Our core offerings focus on cloud management, digital transformation, and cloud modernization — all designed to help organizations unlock the full potential of their technology infrastructure.We take a client-first approach, blending industry-leading hosted technologies with strategic expertise to create tailored, future-ready solutions. Leveraging AI, automation, and emerging technologies, our services simplify IT operations, enhance agility, and accelerate business outcomes. Whether you're migrating to the cloud or optimizing existing cloud environments, Opsio is your partner in achieving sustainable, measurable success.
apidays New York 2025 - Lessons From Two Technical Transformations by Leah Hu...apidays
You Can't Outrun Complexity - But You Can Orchestrate It: Lessons From Two Technical Transformations
Leah Hurwich Adler, Senior Staff Product Manager at Apollo GraphQL
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
May 14 & 15, 2025
------
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apidays New York 2025 - The FINOS Common Domain Model for Capital Markets by ...apidays
The FINOS Common Domain Model for Capital Markets
Tom Healey, Founder & Director at FINXIS LLC
Daniel Schwartz, Managing Partner at FT Advisory LLC
apidays New York 2025
API Management for Surfing the Next Innovation Waves: GenAI and Open Banking
May 14 & 15, 2025
------
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apidays Singapore 2025 - 4 Identity Essentials for Scaling SaaS in Large Orgs...apidays
4 identity factors you didn't know you needed to support large organizations in your SaaS
Daizen Ikehara, Principal Developer Advocate at Auth0
apidays Singapore 2025
Where APIs Meet AI: Building Tomorrow's Intelligent Ecosystems
April 15 & 16, 2025
------
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3. What is Deep Learning Framework?
Deep learning frameworks are software libraries or tools that provide building blocks,
abstraction, and functionalities for designing training and deploying model, this frameworks
simplify the processes of implementing complex mathematics operations required for neural
networks providing it structured environment for developing algorithm.
They simplify AI development by offering pre-built components and APIs, enabling developers to
focus on designing and training models.
5. What is Keras?
Keras is a popular high-level deep learning API written in
Python that runs on top of lower-level deep learning
frameworks like TensorFlow. It provides a user-friendly,
modular interface for building and training neural networks.
6. High-level API(frontend) & Low-level API(backend)
A high level API provides simplified user
friendly way to perform tasks without
requiring knowledge of the complex,
underlying details. It abstracts away
much of the complexity, allowing you to
focus on your main goals, such as
building and training machine learning
models.
A low-level API provides exposes the
core components and functions that
interact directly with the system or
perform the underlying computations.
This type of API requires more detailed
knowledge of the inner workings but
offers much more flexibility and control.
7. Features of keras
● Simple – but not simplistic. Keras reduces developer cognitive load to free you to
focus on the parts of the problem that really matter.
● Flexible – Keras adopts the principle of progressive disclosure of complexity:
simple workflows should be quick and easy, while arbitrarily advanced workflows
should be possible via a clear path that builds upon what you've already learned.
● Powerful – Keras provides industry-strength performance and scalability: it is
used by organizations including NASA, YouTube, or Waymo.
8. Features of keras
● Keras prioritises developer experience.
● Keras is broadly adopted in the industry and among the research community.
● Keras models are easy to turn models into products.
● Support multiple backend engines and does not lock you into one ecosystem.
● Keras has strong multi GPU support and distributed training support.
9. Types of keras models
Sequential Model
● The Sequential model is the simplest type of
model in Keras. It allows you to build a
neural network by stacking layers on top of
each other in a linear (sequential) fashion.
Functional API
● The Functional API in Keras provides a
more flexible way to build models.
● It allows for defining complex architectures,
including models with multiple inputs and
outputs, shared layers, and non-linear data
flows (such as residual connections or
multi-branch networks).
10. Example: keras sequential
import keras
from keras import layers
model = keras.Sequential()
model.add(layers.Dense(2, activation="relu"))
model.add(layers.Dense(3, activation="relu"))
model.add(layers.Dense(4
11. Example: Keras Functional API
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
visible = Input(shape=(2,))
hidden = Dense(2)(visible)
model = Model(inputs=visible, outputs=hidden)
12. Model subclassing, where you implement everything from scratch on your own. Use this if you have
complex, out-of-the-box research use cases.
13. Keras Layers
Layers are the basic building blocks of neural networks in Keras. A
layer consists of a tensor-in tensor-out computation function (the
layer's call method) and some state, held in TensorFlow variables (the
layer's weights).
14. 1. Core Layers:
○ Input Layer: Defines the input shape of the neural network.
○ Dense Layer: Fully connected layer, where each neuron is connected to every neuron in the previous layer
○ Activation Layers: Apply activation functions to introduce non-linearity.
○ Dropout Layer: Prevents overfitting by randomly setting a fraction of input units to zero.
○ Flatten Layer: Reshapes multi-dimensional input into a one-dimensional vector.
○ Reshape Layer: Reshapes the input tensor to a specified shape.
○ Permute Layer: Rearranges the dimensions of an input tensor.
○ RepeatVector Layer: Repeats the input along a new axis.
○ Lambda Layer: Applies an arbitrary function to the input.Masking Layer: Marks specific values in the input as
"masked" (ignored).
○ Embedding Layer: Converts categorical data into dense vectors.
15. 2. Convolution Layers
● Conv1D, Conv2D, Conv3D: Perform convolution operations on 1D, 2D, or 3D input data.
● Useful for tasks such as image classification, video analysis, and time series prediction.
● Conv2DTranspose, Conv3DTranspose: Perform transposed convolutions, often used in
generative models and image segmentation.
● SeparableConv2D, DepthwiseConv2D: Efficient convolutions by splitting the convolution
operation into multiple parts, reducing computation costs.
16. 3. Pooling Layers:
● MaxPooling2D: Downsamples input images by taking the maximum value in each pooling region.
● MaxPooling3D: Downsamples volumetric data by taking the maximum value in each pooling region.
● AveragePooling2D: Downsamples input images by taking the average value in each pooling region.
● AveragePooling3D: Downsamples volumetric data by taking the average value in each pooling region.
● GlobalMaxPooling2D: Downsamples input images to a single value by taking the maximum value across all spatial
dimensions
● GlobalMaxPooling3D: Downsamples volumetric data to a single value by taking the maximum value across all spatial
dimensions.
● GlobalAveragePooling2D: Downsamples input images to a single value by taking the average value across all spatial
dimensions.
● GlobalAveragePooling3D: Downsamples volumetric data to a single value by taking the average value across all
spatial dimensions.
17. 1. Modularity
Keras is modular. It considers a model in the form of a graph or a sequence. Keras allows you to save the
model you are working on. Keras provides a save() method to save the current model. You can even use
the model in the future.
2. Large Dataset
Keras contains a large pre-defined dataset. It provides you a variety of datasets. You can use this dataset
to be directly importing and loading it.
18. 3. Train from NumPy Data
Keras uses the NumPy array to train and evaluate the model. It makes use of the fit() method.
The fit() method fits the model to the training data. This training process may take some time.
fit() method had three arguments batch_size, validation_data and epochs.
4. Evaluation and Prediction
Keras has evaluate() and predict() methods. These methods can use the dataset of NumPy.
After testing the data, the evaluation of the result is done. These methods are used to evaluate
our models.
19. 5. Pre-trained Models in Keras
Keras contains a number of pre-trained models. These models can be imported from keras.applications.
These models are useful for feature extraction and fine-tuning. Keras.application is a module that contains
weights for image classification like VGG16, VGG19, Xception, etc.
6. Encoding in Keras
Karas allows you encoding feature. There is one_hot() function in Keras that enables encoding. It helps
you to encode integers in one step. It also enables you to tokenize the data. This function filters out the
white spaces, make the text to lower case, and filter out the punctuations.
7. Layers in Keras
There are numerous layers and parameters in Keras. All Keras layers have a number of methods in them.
These layers are useful to construct, train, configure the data. The dense layer is beneficial to implement
operations.
20. What is keras layers?
Keras layers are the building blocks used in designing and implementing
neural networks. These layers analyze the structure of input data, perform
transformations like convolution, pooling, and dense connections, and are
combined to create neural network architectures for various machine
learning tasks. Keras provides a wide variety of standard layers to handle
different types of input data and neural network architectures.
21. Types of Keras Layers
1. Convolutional Layers
This layer is mainly used in case of Image processing or Video processing
tasks for spatial convolution over images or sequences. The functionality of
the convolution layer is to apply the specified filters for input image to
generate feature maps.
22. Types of Keras layers
2. Pooling Layers
Pooling layer is used to reduce the dimensions of the feature map from the previous layer
before passing it to next layer in-order to make the computation faster and prevent overfitting.
Two main types of pooling layer are max pooling layer and average pooling layer.
● Max pooling layer takes the maximum of the input region. If we consider a 2×2 matrix
it is replaced by single value which is maximum among the four values.
● Similarly Average pooling layer takes the average of all the input values. If we
consider a 2×2 matrix it is replaced by a single value which is average of all the four
values.
23. Types of Keras layers
3. Dense Layer
● A fully connected neural network layer.
● Each neuron in the layer is connected to every neuron in the previous layer.
● Used for both input and output layers.
● Often used in the final layers of a network to make predictions.
keras.layers.Dense(units, activation=None, ...)
● units: Number of neurons in the layer.
● activation: Activation function (e.g., 'relu', 'sigmoid', 'softmax').
24. Types of Keras layers
4. Flatten Layer:
● Converts a multi-dimensional input (e.g., from a convolutional layer) into a 1D array.
● Useful when transitioning from convolutional layers to dense layers.
● The primary purpose of the Flatten layer is to transform the output of previous layers (like convolutional
layers) into a format that is compatible with the dense layers that follow.
● For example, convolutional layers output a 3D tensor (height, width, and number of channels), but a dense
layer requires a 1D vector as input. The Flatten layer reshapes the multi-dimensional data into a single long
vector.
● Although the Flatten layer does not perform any learning or modification of weights, it preserves the order of
the features while converting the multi-dimensional data into a 1D array. This allows the model to maintain
the spatial and hierarchical information extracted by previous layers.
keras.layers.Flatten()
25. Types of Keras layers
5. Dropout Layer:
● A regularization technique that randomly sets a fraction of input units to zero
at each update during training.
● Helps prevent overfitting by ensuring the network does not rely too heavily
on any one feature.
keras.layers.Dropout(rate)
rate: Fraction of the input units to drop (e.g., 0.5 means 50% of inputs will be set to zero).
26. Types of Keras layers
6. Activation Layer:
● Applies an activation function to the input.
● Can be a separate layer, but often specified directly within other layers (like Dense).
● keras.layers.Activation(activation)
● activation: The activation function to apply (e.g., 'relu', 'tanh', 'sigmoid').
28. Advantages of Keras
● User-friendly:
○ Keras has a simple, intuitive API that makes it easy to build and train deep learning models.
○ It abstracts away many of the complexities of TensorFlow, making it a good choice for
beginners.
● Fast Prototyping:
○ Keras allows for quick experimentation and iteration.
○ You can build complex models with just a few lines of code. Multiple
● Backend Support:
○ Keras can run on top of different backend engines like TensorFlow, Theano, and CNTK.
○ This provides flexibility and allows you to choose the backend that best suits your needs.
29. ● Wide Adoption:
○ Keras is widely adopted by the community and industry, which means there are
abundant resources, tutorials, and support available.
● Model Deployment:
○ Keras models can be easily deployed to various platforms, including web browsers,
mobile devices, and embedded systems.
30. ● Less Flexibility: While Keras' simplicity is a strength, it can also be a limitation. For
advanced users, the high-level abstractions might hide some of the lower-level details,
making it challenging to customize or implement certain complex operations.
● Performance Overhead: For very large and complex models, the abstraction layer in Keras
can introduce some performance overhead compared to using TensorFlow directly.
● Debugging: While Keras' error messages have improved, they can sometimes be less
informative compared to TensorFlow. This can make debugging more challenging in certain
cases.
● Limited Features: Compared to TensorFlow, Keras may have fewer advanced features and
functionalities, especially for research-oriented tasks.
Disadvantages of Keras