Deep Learning – Introduction to Convolutional Neural Networks

Convolutional Neural Networks – Convolutional neural networks (CNNs) are motivated by the architecture of the cerebral cortex. Which is a higher-level brain structure responsible for complex cognitive functions.

Moreover, it is important to highlight that these systems exhibit a significant distance from the complexity and capabilities that characterize the human brain. however, for the current discourse, we shall forego any discussion of neural science pertaining to the biological aspects, given my lack of expertise and academic knowledge in the biological science domain. This blog post alludes to a movement towards synthetic entities but in plain english and at very high level for ease of understanding.

• Deep Learning – Introduction to Recurrent Neural Networks
• Deep Learning – Deep Convolutional Generative Adversarial Networks Basics
• Deep Learning – Backpropagation Algorithm Basics

AILabPage defines Deep learning is “undoubtedly a mind-blowing synchronization technique applied on the basis of three foundation pillars: large data, computing power, skills (enriched algorithms), and experience, which practically has no limits.

What is Deep Learning?

Deep learning is a subfield of the machine learning domain. Deep learning is entirely concerned with algorithms inspired by the structure and function of artificial neural networks, which are inspired by the human brain (inspired only, please).

Deep learning is used with too much ease to predict the unpredictable. In our opinion, “we are all so busy creating artificial intelligence by using a combination of non-biological neural networks and natural intelligence rather than exploring what we have in hand.

Deep learning is performed by a specialist with a complex skillset in order to achieve better results from the same data set than what could be achieved without it. It makes amazing attempts to mimic the natural intelligence (NI) mechanics of the biological neuron system.

Deep learning prioritizes the acquisition of data representations as its foundational approach to learning. It has a complex skill set because of the methods it uses for training, i.e., learning in deep learning is based on “learning data representations” rather than “task-specific algorithms,” which is the case for other methods.

“I think people need to understand that deep learning is making a lot of things, behind the scenes, much-better” – Sir Geoffrey Hinton

Human Brain: It is a special or critical point of discussion for everyone and a puzzling game of all times as well. How our brain is designed and how it functions we can’t cover in this post as I am nowhere close or even can dream to be close to a neuroscientist. Out of curiosity, I am tempted to compare artificial neural networks with the human brain (with the help of talk shows on such topics).

It’s fascinating to me to know how the human brain is able to decode technologies, numbers, puzzles, handle entertainment, understand science, set body modes into pleasure, aggression, art, etc. How does the brain train itself to name a certain object by just looking at 2-3 images when ANNs need millions of those?

Artificial Neural Networks

AILabPage defines artificial neural networks (ANNs) as “Biologically inspired computing code with a number of simple, highly interconnected processing elements for simulating (only an attempt) human brain working and processing information models.”

It’s way different than a computer program, though. There are several kinds of Neural Networks in deep learning. Neural networks consist of input and output layers and at least one hidden layer.

• Multi-Layer Perceptron
• Radial Basis Network
• Recurrent Neural Networks
• Generative Adversarial Networks
• Convolutional Neural Networks.

In this discussion, we delve into the realm of Convolutional Neural Networks (CNNs), exploring their nuances and applications. While our primary focus remains on CNNs, we also touch upon Region-based Convolutional Neural Networks (R-CNNs) for broader context.

This post and the mind map (above), provides an in-depth exploration of CNNs, elucidating their architectural intricacies, training methodologies, and pivotal components. Through this exploration, we aim to shed light on the practical implementations and real-life business use cases of CNN technology.

Neural Network Algorithms are like tech-savvy problem-solvers that use radial basis functions as their secret sauce. Think of them as super-smart tools that can be strategically applied to tackle different challenges.

Convolutional neural networks (CNN) – Outlook

CNNs are a subset of advanced machine learning models created to handle structured grid-based information, particularly images. CNNs are primarily used for image and video analysis. They are designed to automatically learn and extract hierarchical features from visual data using convolutional layers.

CNNs are highly successful in tasks like image classification, object detection, and image generation. When a filter is applied to the input, it will cause the activation to happen. This is a simple and easy thing to do. A feature map is made by using a filter many times on an input. This shows where the features are and how strong they are in the input.

A common application of CNNs is in computerized vision tasks such as the classification of images, the detection of objects within them, and the segmentation of images.

• Deep Learning – Introduction to Recurrent Neural Networks
• Deep Learning – Deep Convolutional Generative Adversarial Networks Basics
• Deep Learning – Backpropagation Algorithm Basics

CNNs represent a powerful class of neural networks renowned for their prowess in tasks like image recognition, processing, and classification.

In this article, we embark on a journey to demystify CNNs, offering an intuitive explanation in plain language. At the heart of a CNN lie four pivotal stages: convolution, pooling, flattening, and full connection. Each of these stages plays a critical role, contributing to the network’s overall efficacy.

Firstly, let’s delve into convolution, a process where filters are applied to the input image using predefined parameters. This step, akin to applying a magnifying glass to certain parts of the image, helps identify important features. Strides dictate how these filters move across the image, while padding ensures consistency in processing. After convolution, activation functions like ReLU (Rectified Linear Unit) are applied, enhancing the network’s ability to capture nonlinear relationships within the data.

Convolutional Neural Networks are a special kind of multi-layer neural networks.

The process of convolution, pooling, and activation forms the backbone of CNNs, underpinning their remarkable success. It’s imperative to execute this process meticulously, as any misstep can compromise the network’s performance. By understanding these fundamental principles, we unlock the potential of CNNs to revolutionize image analysis and beyond

What are Convolutional Neural Networks

Convolutional neural networks, usually appear like magic to many, but in reality, they are simple combination of science and mathematics. These networks are a class of neural networks that have proven very effective in areas of image recognition this in most cases, they are applied to image processing.

CNNs have had huge adoption and success within computer vision applications, but mainly with supervised learning as compared with unsupervised learning, which has gotten very little attention.

This network is a great example of variation in multilayer perceptrons for processing and classification. It’s a deep learning algorithm in which it takes input as an image, applies weights and biases effectively to its objects, and is finally able to differentiate images from each other.

As per Wiki: “In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery.

They have existed for several decades but have been shown to be very powerful when large labelled datasets are used. This requires fast computers (e.g., GPUs)!

The artificial intelligence solutions behind CNN amazingly transform how businesses and developers create user experiences and solve real-world problems. CNN is also known as the application of neuroscience to machine learning. They employ mathematical operations known as “convolution,” which is a specialized kind of linear operation.

Convolutional neural network applications include high-calibre AI systems such as AI-based robots, virtual assistants, and self-driving cars. For image processing, the filters scan through the image to pass the feature map, which gets generated for each filter. Adding more and more filtering layers along with creating more feature maps generally allows abstracts for creating deeper CNN. Other common applications are used for

• Image Processing
  • Recognition
  • Classification
  • Video labelling
  • Text analysis,
• Speech Recognition
  • Natural language processing
  • Text classification processing

Convolutional Neural Network applications solve many unresolved problems that could have remained unsolved without CNN layers, including high-caliber AI systems such as AI-based robots, virtual assistants, and self-driving cars.

Other common applications where CNNs are used, as mentioned above, are emotion recognition, estimating age and gender, etc. The best-known models are convolutional neural networks and recurrent neural networks.

Some history around – Convolutional Neural Networks (CNN’s)

Convolutional Neural Networks (CNNs) stand as a cornerstone in the field of computer vision, reshaping how machines perceive and interpret visual information. Developed by Yann LeCun in the late 1980s, CNNs experienced significant refinement, with LeNet5 emerging as a breakthrough architecture. Named after LeCun’s numerous iterations, LeNet5 showcased the potential of CNNs in deep learning.

The pivotal moment for CNNs came in 2012 when Alex Krizhevsky’s CNN model dominated the ImageNet competition, marking a turning point in computer vision research. This victory spurred widespread adoption and fueled a race among tech giants to leverage CNNs for various applications.

CNNs have since become indispensable in diverse domains, from powering facial recognition systems to enabling object detection in autonomous vehicles. Their ability to automatically learn hierarchical features from raw data makes them incredibly versatile and applicable to a myriad of tasks.

In the 1990s, LeNet’s architecture primarily targeted character recognition tasks, such as reading zip codes and digits. However, the continuous evolution and refinement of CNNs have expanded their scope to encompass a wide range of visual recognition challenges, cementing their status as one of the most influential innovations in computer vision.

Training CNNs

Training a CNN involves feeding batches of data through the model, computing loss, and updating weights using optimization techniques. The trained CNN can then be evaluated on validation or test sets and used for making predictions on new, unseen data.

CNNs play a crucial role in advancing image recognition, object detection, and other visual understanding tasks, revolutionizing industries such as healthcare, autonomous driving, and security.

1. Data Preparation:
   • Load the image of the deer in the green jungle with dimensions 32×32 pixels and three color channels (R, G, B).
   • Preprocess the image by resizing it to fit the input size of the CNN, normalize pixel values, and potentially augment the data with techniques like rotation or flipping.
2. Model Initialization:
   • Initialize the CNN model architecture suitable for image classification tasks, comprising convolutional layers, pooling layers, fully connected layers, and an output layer.
   • Randomly initialize the model parameters (weights and biases) or initialize them with pre-trained weights if available.
3. Forward Propagation:
   • Forward the preprocessed deer image through the CNN model.
   • Perform convolutions and apply activation functions like ReLU to extract features from the image.
   • Apply pooling operations to downsample feature maps and reduce spatial dimensions.
   • Flatten the feature maps to a one-dimensional vector for input to the fully connected layers.
4. Loss Computation:
   • Compute the loss or error between the predicted class probabilities and the true label (e.g., “deer”) using a suitable loss function like categorical cross-entropy.
   • Measure the discrepancy between the predicted and actual class probabilities to quantify the model’s performance.
5. Backward Propagation:
   • Utilize backpropagation to calculate the gradients of the loss function with respect to the model parameters.
   • Propagate the gradients backward through the network, updating the parameters to minimize the loss.
   • Adjust the weights and biases of the network to improve its ability to classify the image correctly.
6. Parameter Update:
   • Update the model parameters using an optimization algorithm such as stochastic gradient descent (SGD) or its variants.
   • Adjust the parameters in the direction that reduces the loss, guided by the computed gradients and the learning rate.
7. Iteration:
   • Repeat the forward and backward propagation steps for multiple iterations or epochs, processing batches of images to train the model.
   • Each iteration involves updating the model parameters based on gradients computed from a batch of training data.
8. Evaluation:
   • Periodically evaluate the model’s performance on a validation dataset to monitor its progress and prevent overfitting.
   • Calculate evaluation metrics such as accuracy or loss on the validation set to assess the model’s generalization ability.
9. Termination:
   • Define stopping criteria for training, such as reaching a maximum number of epochs or observing diminishing returns in validation performance.
   • Stop training when the model’s performance on the validation set no longer improves significantly.
10. Testing:

• After training concludes, evaluate the final trained model on a separate test dataset containing unseen images of deer and other objects.
• Assess the model’s ability to generalize to new data by computing metrics like accuracy or loss on the test set.

As depicted in above diagram the AILabPage_User trains the CNN model by providing data batches, which are received and processed by the model. During training, the CNN computes loss and updates weights using optimization techniques iteratively. Once trained, the CNN can be evaluated on validation or test sets to assess its performance, or used to make predictions on new data. Deep learning frameworks like TensorFlow facilitate the implementation of CNNs by providing essential tools and resources.

The combination of convolutional layers, pooling layers, and fully connected layers allows CNNs to capture intricate patterns and relationships in images, making them invaluable for various computer vision tasks. With the availability of deep learning frameworks like TensorFlow, implementing CNNs has become more accessible, enabling researchers and practitioners to leverage the power of CNNs in their applications. As technology continues to advance.

Data Processing – Convolutional Neural Network

CNN has a grid topology for processing data. Data points in this topology are called grid-like, as the processing of data happens in a spatial correlation between the neighbouring data points.

• 1D Grid – Time series data – Takes samples at regular time intervals
• 2D Grid – Image data – Grid of pixels

These neural networks use the convolution method as opposed to general matrix multiplication in at least one of the layers. Convolution leverages on

• Equivariant Representations –  This simply means that if the input changes, the output changes in the same way
• Sparse Interactions – This allows the network to efficiently describe complicated interactions between many variables using simple building blocks.
• Parameter sharing – Using the same parameter for more than one function in a model

The above structure was created to improve a machine-learning system. CNN also allows for working with inputs of variable size and efficiently describing complicated interactions between many variables from simple building blocks.

There are significant limitations to these neural networks at the API level. Input, e.g., an image, and output, e.g., classes of probabilities, are both fixed-size vectors. Even the computation through its data models is performed by mapping using a fixed number of layers.

Image Processing – Human vs Computers

For humans, the recognition of objects is the first skill we learn right from birth. A newborn baby starts recognizing faces as Papa, Mumma, etc. By the time you turn into an adult, recognition becomes effortless and a kind of automated process.

Human behavior for processing images is very different from that of machines. Humans give a label to each image automatically by just looking around and immediately characterizing the scene and giving each object a label without even consciously noticing.

For computers, recognizing objects is slightly complex as they see everything as input and output, which come as a class or set of classes. This is known as image processing, which we will discuss in detail in the next section. In computers, CNNs do image recognition and image classification.

It is very useful in object detection, face recognition, and various text classification tasks with word embedding, etc. So in simple words, computer vision is the ability to automatically understand any image or video based on visual elements and patterns.

CNN Architecture – Deciphering Image Analysis

In this process, Our focus will be on image processing only, we’ll outline the step-by-step operation of a Convolutional Neural Network (CNN) architecture using an image of a deer in a jungle as input. Each stage, from initial feature extraction to final prediction generation, will be meticulously described, illuminating the intricate workings of CNNs in image analysis tasks.

CNN requires models to train and test. Each input image passes through a series of convolution layers with filters (Kernels), pooling, and fully connected layers (FC) and applies the softmax function (a generalization of the logistic function that “squashes” a K-dimensional vector of arbitrary real values into a real Kd vector) to classify an object with probabilistic values between 0 and 1. This is the reason every image in CNN gets represented as a matrix of pixel values.

Input Image

The raw input data is the image of a deer in a green jungle, with a width of 32 pixels, a height of 32 pixels, and three color channels (red, green, and blue). Each pixel’s intensity values range from 0 to 255.

import numpy as np
input_image = np.random.rand(32, 32, 3)

Convolutional Layer 1

The convolution and pooling layers act as feature extractors from the input image, while a fully connected layer acts as a classifier. In the above image figure, on receiving a dear image as input, the network correctly assigns the highest probability for it (0.94) among all four categories.

The sum of all probabilities in the output layer should be one, though. There are four main operations in the ConvNet shown in the image above:

• Convolution Operation: The input image is convolved with a set of learnable filters, also known as kernels or feature detectors. Each filter slides across the input image, computing dot products with local regions to extract various features.
• Feature Map Generation: The convolution operation produces multiple feature maps, each highlighting different patterns or features present in the input image.
• Apply the convolution operation to the input image using the defined layer.
• Add a bias term to the convolved output.
• Apply an activation function, such as ReLU, to introduce non-linearity.
• Lets assume the output probabilities for image above are [0.2, 0.1, 0.3, 0.4]
• The size of the feature map is controlled by three parameters.
  • Depth –  Number of filters used for the convolution operation.
  • Stride – number of pixels by which filter matrix over the input matrix.
  • padding – It’s good to input matrix with zeros around the border, matrix.
  • Calculating total error at the output layer with summation over all 4 classes.
    •  Total Error = ∑  ½ (target probability – output probability) ²
    • Computation of output of neurons that are connected to local regions in the input. This may result in volume such as [32x32x16] for 16 filters.

from tensorflow.keras.layers import Conv2D
conv_layer1 = Conv2D(filters=32, kernel_size=(3, 3), activation=’relu’)(input_image)

Each filter scans across the image, computing dot products between its weights and local patches of the image. This process generates feature maps that capture different image features.

ReLU Activation

The Rectified Linear Unit (ReLU) Layer serves as a crucial element in Convolutional Neural Networks (CNNs), adding non-linearity to the network and enabling it to learn complex patterns from the input data.

• Non-linear Transformation: Similar to the first convolutional layer, ReLU activation is applied to introduce non-linearity and enhance the network’s representational capacity.

Activation Function: Apply a non-linear activation function, such as ReLU (Rectified Linear Unit), to introduce non-linearity into the model. ReLU sets negative values to zero and keeps positive values unchanged, helping the network learn more complex representations.

For instance, our input feature map size is [32x32x16], applying ReLU to it retains the same size of [32x32x16]. ReLU introduces non-linearity to the network, allowing it to model complex relationships and make nonlinear transformations of the input data. This non-linear behavior is essential for the network to learn and represent intricate patterns and features present in the input data, which may not be captured by linear operations alone.

We now apply an activation function again, such as ReLU or sigmoid, to the outputs of fully connected layers to introduce non-linearity and ensure the model can learn complex mappings.

Overall, the ReLU Layer plays a critical role in CNNs by enabling the network to learn sophisticated representations of the input data and thus improving its ability to perform tasks such as image classification, object detection, and segmentation.

Pooling Layer 1

• Max-Pooling Operation: The feature maps obtained from the convolutional layer are downsampled using a max-pooling operation. Max-pooling extracts the most prominent features by selecting the maximum value within each pooling region.
• Dimensionality Reduction: Max-pooling reduces the spatial dimensions of the feature maps, making subsequent computations more efficient while preserving essential features.

from tensorflow.keras.layers import MaxPooling2D
pool_layer1 = MaxPooling2D(pool_size=(2, 2))(conv_layer1)

Convolutional Layer 2

• Convolution Operation: The downsampled feature maps from the first pooling layer undergo another round of convolution with a new set of learnable filters.
• Feature Hierarchy Learning: The second convolutional layer learns higher-level features by combining lower-level features learned from the previous layer.

conv_layer2 = Conv2D(filters=64, kernel_size=(3, 3), activation=’relu’)(pool_layer1)

ReLU Activation

• Non-linear Transformation: Similar to the first convolutional layer, ReLU activation is applied to introduce non-linearity and enhance the network’s representational capacity.

Pooling Layer – 2

• Max Pooling operation on a Rectified Feature map.

So remember pooling layer does downsample the feature maps generated by convolutional layers.

• Define a pooling layer with specified parameters, such as pool size and stride.
• Apply the pooling operation to the output of the previous convolutional layer.

Also called subsampling or downsampling. The pooling layer does a downsampling operation along the spatial dimensions (width and height), resulting in a volume such as [16x16x16], i.e., it reduces the dimensionality of each feature map but retains the most important information.

• Max-Pooling Operation: The feature maps generated by the second convolutional layer are further downsampled using max-pooling to capture the most salient features.
• Spatial Information Reduction: Pooling reduces the spatial resolution of the feature maps while retaining essential information, aiding the network in focusing on the most discriminative features.

from tensorflow.keras.layers import MaxPooling2D
pool_layer1 = MaxPooling2D(pool_size=(2, 2))(conv_layer1)

Common pooling operations include max pooling, where the maximum value in each region is retained, or average pooling, where the average value is taken. Pooling reduces spatial dimensions while preserving important features.

Flattening

• Data Restructuring: The pooled feature maps are flattened into a one-dimensional vector, preserving the spatial hierarchy of features while converting them into a format suitable for processing by fully connected layers.
• Vector Representation: Flattening transforms the multi-dimensional feature maps into a linear array, preparing the data for input to the fully connected layers.

from tensorflow.keras.layers import Flatten
flattened_output = Flatten()(pool_layer2)

Fully Connected Layer – 1

In the fully connected layer, also known as the dense layer, each neuron is connected to every neuron in the adjacent layer. This connectivity allows for complex interactions and feature combinations to be learned. In the context of classification tasks, the fully connected layer computes class scores using a traditional multilayer perceptron architecture. This involves applying a softmax activation function to the output layer, resulting in a probability distribution over the different classes.

• Neuron Interconnection: The flattened feature vector is fed into a densely connected layer, where each neuron is connected to every neuron in the next layer.
• Feature Abstraction: Fully connected layers learn abstract representations of the input data, capturing complex relationships between features extracted from the convolutional layers.

For example, in the CIFAR-10 dataset, which consists of 10 classes, the output layer produces a volume of size [1x1x10]. Each of the 10 numbers in this volume represents the score or probability assigned to a specific class.

(The image left I decided to replace my deer’s picture with my dear son)

Before reaching the fully connected layers, the input data undergoes several convolutional and pooling layers. These layers serve to extract and abstract features from the input images. Once the feature maps are generated, they are flattened into a one-dimensional vector and passed to the fully connected layers.

These layers play a crucial role in learning high-level representations of the input data by connecting every neuron from the flattened feature maps to neurons in the fully connected layer. This dense connectivity enables the network to learn complex patterns and relationships present in the data, ultimately facilitating accurate classification or prediction tasks

• Flatten the output from the last pooling layer to a 1D vector.
• Define fully connected layers with specified number of neurons and activation functions.
• Connect the flattened output to the fully connected layers.

from tensorflow.keras.layers import Dense
fully_connected1 = Dense(units=128, activation=’relu’)(flattened_output)
fully_connected2 = Dense(units=64, activation=’relu’)(fully_connected1)

The main job of this layer is to basically take an input volume as it comes as output from Conv, ReLU, or pool layer proceedings. Arrange the output in an N-dimensional vector, where N is the number of classes that the program has to choose from.

Fully Connected Layer – 2

• Further Abstraction: An additional fully connected layer may be added to the network to extract higher-level features and relationships from the data.
• Representation Refinement: The second fully connected layer refines the learned representations, enabling the network to make more accurate predictions.

Output Layer

The final layer of the CNN is the output layer, which produces the network’s predictions. The number of neurons in this layer depends on the specific task, such as binary classification, multi-class classification, or regression. The activation function used in the output layer depends on the task as well.

• Prediction Generation: The final fully connected layer produces the network’s predictions by mapping the learned features to output classes or values.
• Activation Function: Depending on the task, an appropriate activation function (e.g., softmax for classification or linear for regression) is applied to the output layer to generate probability distributions or continuous predictions.

output_layer = Dense(units=num_classes, activation=’softmax’)(fully_connected2)

Through this detailed examination, we’ve unraveled the complex processing involved in a CNN architecture. From the extraction of low-level features to the generation of high-level abstractions, each step contributes to the network’s ability to understand and interpret visual data, ultimately enabling accurate predictions and insights.

CNN – Challenges

CNNs revolutionized image classification and analysis, but they face hurdles like vanishing/exploding gradients, overfitting, and data quality issues. Understanding and mitigating these challenges are crucial for enhancing CNN performance and advancing computer vision applications.

1. Vanishing/Exploding Gradients: CNNs may suffer from gradients becoming extremely small or large during training, hindering effective weight updates and model convergence.
2. Data Quality Issues: Poor quality or insufficient training data can lead to biased models, limiting their ability to generalize to new, unseen data.
3. Overfitting: CNNs may memorize training data instead of learning underlying patterns, resulting in poor performance on unseen data and reduced model generalization.

It encounter obstacles such as vanishing/exploding gradients, overfitting, and data quality concerns. Addressing these challenges is essential for improving CNN performance and ensuring accurate image analysis in various fields like healthcare, autonomous vehicles, and security systems.

Convolutional Neural Networks – Real-life Business Use Cases

Many modern companies are using CNNs as the backbone of their business, e.g., Pinterest uses it for home feed personalization, and Instagram uses it for search infrastructure. Three of the biggest users are listed below.

• Automatic Tagging Algorithms: Tagging, or social bookmarking, refers to the action of associating a relevant keyword or phrase with an entity (e.g., a document, image, or video). Our experiment (above) showed us that effective time-frequency representation for automatic tagging and more complex models benefit from more training data.
• Photo Search: To find images that are similar to the user’s  input or text input, use the Google search results. It works well on the Chrome app. Google’s algorithms rely on more than 200 unique signals, or “clues,” that make it possible to guess a search. Attributes here are websites, the age of content, IP address-based regions, and PageRanks. Sadly, this is highly biased based on your color of skin. You can give it a try, though.

• Product Recommendations: Large-scale recommender systems are in use in almost every e-commerce, retail, video-on-demand, or music-streaming business. Algorithms in recommender systems are typically classified into two categories: content-based and collaborative filtering methods, although modern recommenders combine both approaches.

These companies leverage CNNs to personalize content and improve search accuracy, demonstrating the widespread adoption and impact of convolutional neural networks in modern technology-driven businesses.

The process of a CNN entails four crucial stages: convolution, pooling, flattening, and full connection. These steps were comprehensively explored. Decide on the parameters, apply filters using strides, and include padding if necessary. Apply convolution to the image, followed by matrix activation using ReLU. The fundamental process at CNN, which is crucial to its success, is immensely important. If it is not correctly executed, the entire process is doomed to fail.

Conclusion- An endeavor was made in this article to elucidate the fundamental ideas behind convolutional neural networks in plain language. Convolutional Neural Networks (CNNs) are powerful deep learning models specifically designed for image recognition and processing tasks. By leveraging the hierarchical structure of convolutional layers, pooling layers, and fully connected layers, CNNs excel at extracting meaningful features from images and making accurate predictions. The step-by-step process outlined above demonstrates the key components and operations involved in building a CNN, from data preparation and convolutional layers to pooling, fully connected layers, and optimization.

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Books Referred & Other material referred

• Open Internet & AILabPage member’s hands-on lab work.
• LeNet5  documentation.

Points to Note:

All credits, if any, remain with the original contributor only. We have covered the convolutional neural network, a kind of machine learning, in this post, where we find hidden gems from unlabeled historical data. The last post was on supervised machine learning. In the next post, I will talk about reinforcement machine learning.

Feedback & Further Question

Do you have any questions about deep learning or machine learning? Leave a comment or ask your question via email. I will try my best to answer it.

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