Convolutional neural networks (CNNs) are a type of deep neural network commonly used for analyzing visual imagery. CNNs use various techniques like convolution, ReLU activation, and pooling to extract features from images and reduce dimensionality while retaining important information. CNNs are trained end-to-end using backpropagation to update filter weights and minimize output error. Overall CNN architecture involves an input layer, multiple convolutional and pooling layers to extract features, fully connected layers to classify features, and an output layer. CNNs can be implemented using sequential models in Keras by adding layers, compiling with an optimizer and loss function, fitting on training data over epochs with validation monitoring, and evaluating performance on test data.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
image classification is a common problem in Artificial Intelligence , we used CIFR10 data set and tried a lot of methods to reach a high test accuracy like neural networks and Transfer learning techniques .
you can view the source code and the papers we read on github : https://github.com/Asma-Hawari/Machine-Learning-Project-
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Simplilearn
This presentation on Convolutional neural network tutorial (CNN) will help you understand what is a convolutional neural network, hoe CNN recognizes images, what are layers in the convolutional neural network and at the end, you will see a use case implementation using CNN. CNN is a feed forward neural network that is generally used to analyze visual images by processing data with grid like topology. A CNN is also known as a "ConvNet". Convolutional networks can also perform optical character recognition to digitize text and make natural-language processing possible on analog and hand-written documents. CNNs can also be applied to sound when it is represented visually as a spectrogram. Now, lets deep dive into this presentation to understand what is CNN and how do they actually work.
Below topics are explained in this CNN presentation(Convolutional Neural Network presentation)
1. Introduction to CNN
2. What is a convolutional neural network?
3. How CNN recognizes images?
4. Layers in convolutional neural network
5. Use case implementation using CNN
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
image classification is a common problem in Artificial Intelligence , we used CIFR10 data set and tried a lot of methods to reach a high test accuracy like neural networks and Transfer learning techniques .
you can view the source code and the papers we read on github : https://github.com/Asma-Hawari/Machine-Learning-Project-
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...Simplilearn
This presentation on Convolutional neural network tutorial (CNN) will help you understand what is a convolutional neural network, hoe CNN recognizes images, what are layers in the convolutional neural network and at the end, you will see a use case implementation using CNN. CNN is a feed forward neural network that is generally used to analyze visual images by processing data with grid like topology. A CNN is also known as a "ConvNet". Convolutional networks can also perform optical character recognition to digitize text and make natural-language processing possible on analog and hand-written documents. CNNs can also be applied to sound when it is represented visually as a spectrogram. Now, lets deep dive into this presentation to understand what is CNN and how do they actually work.
Below topics are explained in this CNN presentation(Convolutional Neural Network presentation)
1. Introduction to CNN
2. What is a convolutional neural network?
3. How CNN recognizes images?
4. Layers in convolutional neural network
5. Use case implementation using CNN
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
The next phase of Smart Network Convergence could be putting Deep Learning systems on the Internet. Deep Learning and Blockchain Technology might be combined in the smart networks of the future for automated identification (deep learning) and automated transaction (blockchain). Large scale future-class problems might be addressed with Blockchain Deep Learning nets as an advanced computational infrastructure, challenges such as million-member genome banks, energy storage markets, global financial risk assessment, real-time voting, and asteroid mining.
Blockchain Deep Learning nets and Smart Networks more generally are computing networks with intelligence built in such that identification and transfer is performed by the network itself through sophisticated protocols that automatically identify (deep learning), and validate, confirm, and route transactions (blockchain) within the network.
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
A convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks that has Successfully been applied to analyzing visual imagery
YU CS Summer 2021 Project | TensorFlow Street Image Classification and Object...JacobSilbiger1
YU CS Summer 2021 Project | TensorFlow Street Image Classification and Object Detection Model
By: Nissim Cantor, Avi Radinsky, Jacob Silbiger
Github: https://github.com/ndcantor/tensorflow-street-classifier
Demo: https://www.youtube.com/watch?v=ItXdPJ3okMo
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
The next phase of Smart Network Convergence could be putting Deep Learning systems on the Internet. Deep Learning and Blockchain Technology might be combined in the smart networks of the future for automated identification (deep learning) and automated transaction (blockchain). Large scale future-class problems might be addressed with Blockchain Deep Learning nets as an advanced computational infrastructure, challenges such as million-member genome banks, energy storage markets, global financial risk assessment, real-time voting, and asteroid mining.
Blockchain Deep Learning nets and Smart Networks more generally are computing networks with intelligence built in such that identification and transfer is performed by the network itself through sophisticated protocols that automatically identify (deep learning), and validate, confirm, and route transactions (blockchain) within the network.
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
A convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks that has Successfully been applied to analyzing visual imagery
YU CS Summer 2021 Project | TensorFlow Street Image Classification and Object...JacobSilbiger1
YU CS Summer 2021 Project | TensorFlow Street Image Classification and Object Detection Model
By: Nissim Cantor, Avi Radinsky, Jacob Silbiger
Github: https://github.com/ndcantor/tensorflow-street-classifier
Demo: https://www.youtube.com/watch?v=ItXdPJ3okMo
Now days, Detection of human head play an very important role in pedestrian counting. Machine learning is one platform, where human being can train a machine to act without being explicitly programmed and gives more accurate result, even when there is no enough data. Convolution neural network is one which works well for multimedia communication such as Text, Audio and Video. In this paper convnets play an important role in human head detection. In this paper it’s going to explain the less number of layers with more accuracy in the results with less time consuming.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
2. Introduction
In the past few decades, Deep Learning has proved to be a very powerful tool because of its
ability to handle large amounts of data. The interest to use hidden layers has surpassed
traditional techniques, especially in pattern recognition. One of the most popular deep neural
networks is Convolutional Neural Networks.
3. APPLICATIONS
• OCR and image recognition
• Decoding Facial Recognition
• Analyzing Documents
• Grey Areas
• Detecting objects in self-driving cars
• Social media face recognition
• Image analysis in medicine
• Advertising
4. What exactly is a CNN?
In deep learning, a convolutional neural network (CNN/ConvNet) is a class of deep neural networks, most
commonly applied to analyze visual imagery. Now when we think of a neural network we think about matrix
multiplications but that is not the case with ConvNet. It uses a special technique called Convolution. Now in
mathematics convolution is a mathematical operation on two functions that produces a third function that
expresses how the shape of one is modified by the other. But we don’t really need to go behind the mathematics part to
understand what a CNN is or how it works.
Bottom line is that the role of the ConvNet is to reduce the images into a form that is easier to process, without losing features
that are critical for getting a good prediction.
7. Introduction
Convolutional Neural Networks (CNNs) learns multi-level features and
classifier in a joint fashion and performs much better than traditional
approaches for various image classification and segmentation problems.
8. How does it work?
Before we go to the working of CNN’s let’s cover the basics such as what is an image
and how is it represented. An RGB image is nothing but a matrix of pixel values
having three planes whereas a grayscale image is the same but it has a single plane.
Take a look at this image to understand more.
9. For simplicity, let’s stick with grayscale images as we
try to understand how CNNs work.
10. Convolution
• The primary purpose of Convolution in case of a CNN is to extract features from the input image.
11. Convolution...
• The size of the output volume is controlled by three parameters that we need to decide
before the convolution step is performed:
Depth: Depth corresponds to the number of filters we use for the convolution operation.
Stride: Stride is the number of pixels by which we slide our filter matrix over the input
matrix.
Zero-padding: Sometimes, it is convenient to pad the input matrix with zeros around the
border, so that we can apply the filter to bordering elements of our input image matrix.
• With zero-padding wide convolution
• Without zero-padding narrow convolution
12. Non-Linearity (ReLU)
• Replaces all negative pixel values in the feature map by
zero.
• The purpose of ReLU is to introduce nonlinearity in CNN,
since most of the real-worlddata would be non-linear.
• Other non-linear functions such as tanh (-1,1) or sigmoid
(0,1) can also be used instead ofReLU (0,input).
13. Pooling
Reduces the dimensionality of each feature map but retains the most important information.
Pooling can be of different types: Max, Average, Sum etc.
14. • Together these layers extract the useful features from the images.
• The output from the convolutional and pooling layers represent high-level features of the input
image.
16. Putting it all together – Training using Backpropagation
• Step 1: We initialize all filters and parameters / weights with random values.
• Step 2: The network takes a training image as input, goes through the forward propagation
step (convolution, ReLU and pooling operations along with forward propagation in the Fully
Connected layer) and finds the output probabilities for each class.
• Let’s say the output probabilities for the boat image above are [0.2, 0.4, 0.1, 0.3].
• Since weights are randomly assigned for the first training example, output probabilities are
also random.
• Step 3: Calculate the total error at the output layer (summation over all 4 classes).
17. Step 4: Use Backpropagation to calculate the gradients of the error with respect to all weights
in the network and use gradient descent to update all filter values/ weights and parameter
values to minimize the output error.
• The weights are adjusted in proportion to their contribution to the total error.
• When the same image is input again, output probabilities might now be [0.1, 0.1, 0.7, 0.1],
which is closer to the target vector [0, 0, 1, 0].
• This means that the network has learnt to classify this particular image correctly by adjusting its
weights / filters such that the output error is reduced.
• Parameters like number of filters, filter sizes, architecture of the network etc. have all been fixed
before Step 1 and do not change during training process – only the values of the filter matrix and
connection weights get updated.
• Step 5: Repeat steps 2-4 with all images in the training set
18. • Parameters like number of filters, filter sizes, architecture of the network etc. have all
been fixed before Step 1 and do not change during training process – only the values of
the filter matrix and connection weights get updated.
• Step 5: Repeat steps 2-4 with all images in the training set
19. SEQUENTIAL
Model - Sequential
Sequential is the easiest way to build a model in Keras. It allows you to build a model layer by
layer. Each layer has weights that correspond to the layer the follows it.
• We use the ‘add()’ function to add layers to our model.
• ‘Dense’ is the layer type. Dense is a standard layer type that works for most cases. In a dense layer, all
nodes in the previous layer connect to the nodes in the current layer.
We define nodes in each of our input layers. This number can also be in the hundreds or
thousands. Increasing the number of nodes in each layer increases model capacity. I will go into
further detail about the effects of increasing model capacity shortly.
• ‘Activation’ is the activation function for the layer. An activation function allows models to take into
account nonlinear relationships.
The first layer needs an input shape. The input shape specifies the number of rows and columns
in the input.
The last layer is the output layer. It only has one node, two nodes as per requirment. which is for
our prediction.
20. Compiling Model.
Compiling the model takes two parameters: optimizer and loss.
• The optimizer controls the learning rate. We will be
using ‘adam’ as our optmizer.
• Adam is generally a good optimizer to use for many cases. The
adam optimizer adjusts the learning rate throughout training.
• The learning rate determines how fast the optimal weights for the
model are calculated. A smaller learning rate may lead to more
accurate weights (up to a certain point), but the time it takes to
compute the weights will be longer.
21. Train
• we will use the ‘fit()’ function on our model with the following five parameters:
• training data (train_X),
• target data (train_y),
• validation split,
• number of epochs
• The validation split will randomly split the data into use for training and testing.
• During training, we will be able to see the validation loss, If we will set the
validation split at 0.2, which means that 20% of the training data we provide in
the model will be set aside for testing model performance.
22. • The number of epochs is the number of times the model will cycle through the
data. The more epochs we run, the more the model will improve, up to a certain
point. After that point, the model will stop improving during each epoch. In
addition, the more epochs, the longer the model will take to run. To monitor this,
we will use ‘early stopping’.
• Early stopping will stop the model from training before the number of epochs is
reached if the model stops improving. We will set our early stopping monitor to
3. This means that after 3 epochs in a row in which the model doesn’t improve,
training will stop. Sometimes, the validation loss can stop improving then
improve in the next epoch, but after 3 epochs in which the validation loss doesn’t
improve, it usually won’t improve again.
• to make predictions on new data, we would use the ‘predict()’ function, passing
in our new data.
As you increase the number of nodes and layers in a model, the model capacity
increases. Increasing model capacity can lead to a more accurate model, up to
a certain point, at which the model will stop improving. Generally, the more
training data you provide, the larger the model should be.