Overview
Shared with your team. Learn about space permissions and restrictions.
Greetings!
Think of your personal space as a folder of storing all the documents that don't currently belong anywhere else. It's all yours. Customize this page by clicking the edit button on top.
Cricket is a bat-and-ball game played between two teams of eleven players on a field at the centre of which is a 22-yard (20-metre) pitch with a wicket at each end, each comprising two bails balanced on three stumps. Two players from the batting team (the striker and nonstriker) stand in front of either wicket, with one player from the fielding team (the bowler) bowling the ball towards the striker's wicket from the opposite end of the pitch. The striker's goal is to hit the bowled ball and then switch places with the nonstriker, with the batting team scoring one run for each exchange. Runs are also scored when the ball reaches or crosses the boundary of the field or when the ball is bowled illegally.
What's inside this space?
What is a Large Language Model (LLM)
Last Updated : 10 Jan, 2024
Generative Summary
Now you can generate the summary of any article of your choice.
Got it
Large Language Models (LLMs) represent a breakthrough in artificial intelligence, employing neural network techniques with extensive parameters for advanced language processing.
This article explores the evolution, architecture, applications, and challenges of LLMs, focusing on their impact in the field of Natural Language Processing (NLP).
What are Large Language Models(LLMs)?
A large language model is a type of artificial intelligence algorithm that applies neural network techniques with lots of parameters to process and understand human languages or text using self-supervised learning techniques. Tasks like text generation, machine translation, summary writing, image generation from texts, machine coding, chat-bots, or Conversational AI are applications of the Large Languag.e Model. Examples of such LLM models are Chat GPT by open AI, BERT (Bidirectional Encoder Representations from Transformers) by Google, etc.
There are many techniques that were tried to perform natural language-related tasks but the LLM is purely based on the deep learning methodologies. LLM (Large language model) models are highly efficient in capturing the complex entity relationships in the text at hand and can generate the text using the semantic and syntactic of that particular language in which we wish to do so.
Author-generated image with the use of AI
LLM Models
If we talk about the size of the advancements in the GPT (Generative Pre-trained Transformer) model only then:
GPT-1 which was released in 2018 contains 117 million parameters having 985 million words.
GPT-2 which was released in 2019 contains 1.5 billion parameters.
GPT-3 which was released in 2020 contains 175 billion parameters. Chat GPT is also based on this model as well.
GPT-4 model is expected to be released in the year 2023 and it is likely to contain trillions of parameters.
How do Large Language Models work?
Large Language Models (LLMs) operate on the principles of deep learning, leveraging neural network architectures to process and understand human languages.
These models, are trained on vast datasets using self-supervised learning techniques. The core of their functionality lies in the intricate patterns and relationships they learn from diverse language data during training. LLMs consist of multiple layers, including feedforward layers, embedding layers, and attention layers. They employ attention mechanisms, like self-attention, to weigh the importance of different tokens in a sequence, allowing the model to capture dependencies and relationships.
Architecture of LLM
A Large Language Model’s (LLM) architecture is determined by a number of factors, like the objective of the specific model design, the available computational resources, and the kind of language processing tasks that are to be carried out by the LLM. The general architecture of LLM consists of many layers such as the feed forward layers, embedding layers, attention layers. A text which is embedded inside is collaborated together to generate predictions.
Important components to influence Large Language Model architecture –
Model Size and Parameter Count
input representations
Self-Attention Mechanisms
Training Objectives
Computational Efficiency
Decoding and Output Generation
Transformer-Based LLM Model Architectures
Transformer-based models, which have revolutionized natural language processing tasks, typically follow a general architecture that includes the following components:
Input Embeddings: The input text is tokenized into smaller units, such as words or sub-words, and each token is embedded into a continuous vector representation. This embedding step captures the semantic and syntactic information of the input.
Positional Encoding: Positional encoding is added to the input embeddings to provide information about the positions of the tokens because transformers do not naturally encode the order of the tokens. This enables the model to process the tokens while taking their sequential order into account.
Encoder: Based on a neural network technique, the encoder analyses the input text and creates a number of hidden states that protect the context and meaning of text data. Multiple encoder layers make up the core of the transformer architecture. Self-attention mechanism and feed-forward neural network are the two fundamental sub-components of each encoder layer.
Self-Attention Mechanism: Self-attention enables the model to weigh the importance of different tokens in the input sequence by computing attention scores. It allows the model to consider the dependencies and relationships between different tokens in a context-aware manner.
Feed-Forward Neural Network: After the self-attention step, a feed-forward neural network is applied to each token independently. This network includes fully connected layers with non-linear activation functions, allowing the model to capture complex interactions between tokens.
Decoder Layers: In some transformer-based models, a decoder component is included in addition to the encoder. The decoder layers enable autoregressive generation, where the model can generate sequential outputs by attending to the previously generated tokens.
Multi-Head Attention: Transformers often employ multi-head attention, where self-attention is performed simultaneously with different learned attention weights. This allows the model to capture different types of relationships and attend to various parts of the input sequence simultaneously.
Layer Normalization: Layer normalization is applied after each sub-component or layer in the transformer architecture. It helps stabilize the learning process and improves the model’s ability to generalize across different inputs.
Output Layers: The output layers of the transformer model can vary depending on the specific task. For example, in language modeling, a linear projection followed by SoftMax activation is commonly used to generate the probability distribution over the next token.
It’s important to keep in mind that the actual architecture of transformer-based models can change and be enhanced based on particular research and model creations. To fulfill different tasks and objectives, several models like GPT, BERT, and T5 may integrate more components or modifications.
Large Language Models Examples
Now let’s look at some of the famous LLMs which has been developed and are up for inference.
GPT – 3: The full form for GPT is a Generative pre-trained Transformer and this is the third version of such a model hence it is numbered as 3. This is developed by Open AI and you must have heard about Chat GPT which is launched by Open AI and is nothing but the GPT-3 model.
BERT – The full form for this is Bidirectional Encoder Representations from Transformers. This large language model has been developed by Google and is generally used for a variety of tasks related to natural language. Also, it can be used to generate embeddings for a particular text may be to train some other model.
RoBERTa – The full form for this is the Robustly Optimized BERT Pretraining Approach. In the series of attempts to improve the performance of the transformer architecture, RoBERTa is an enhanced version of the BERT model which is developed by Facebook AI Research.
BLOOM – It is the first multilingual LLM generated by the association of the different organizations and researchers who combined their expertise to develop this model which is similar to the GPT-3 architecture.
To explore further these models you can click on the particular model to get to know how you can use them by using the open source platforms like Hugging Face of Open AI. These articles cover the implementation part for each of these models in Python.
Large Language Models Use Cases
The main reason behind such a craze about the LLMs is their efficiency in the variety of tasks they can accomplish. From the above introductions and technical information about the LLMs you must have understood that the Chat GPT is also an LLM so, let’s use it to describe the use cases of Large Language Models.
Code Generation – One of the craziest use cases of this service is that it can generate quite an accurate code for a specific task that is described by the user to the model.
Debugging and Documentation of Code – If you are struggling with some piece of code regarding how to debug it then ChatGPT is your savior because it can tell you the line of code which are creating issues along with the remedy to correct the same. Also now you don’t have to spend hours writing the documentation of your project you can ask ChatGPT to do this for you.
Question Answering – As you must have seen that when AI-powered personal assistants were released people used to ask crazy questions to them well you can do that here as well along with the genuine questions.
Language Transfer – It can convert a piece of text from one language to another as it supports more than 50 native languages. It can also help you correct the grammatical mistakes in your content.
Use cases of LLM are not limited to the above-mentioned one has to be just creative enough to write better prompts and you can make these models do a variety of tasks as they are trained to perform tasks on one-shot learning and zero-shot learning methodologies as well. Due to this only Prompt Engineering is a totally new and hot topic in academics for people who are looking forward to using ChatGPT-type models extensively.
Large Language Models Applications
LLMs, such as GPT-3, have a wide range of applications across various domains. Few of them are:
Natural Language Understanding (NLU)
Large language models power advanced chatbots capable of engaging in natural conversations.
They can be used to create intelligent virtual assistants for tasks like scheduling, reminders, and information retrieval.
Content Generation
Creating human-like text for various purposes, including content creation, creative writing, and storytelling.
Writing code snippets based on natural language descriptions or commands.
Language Translation
Large language models can aid in translating text between different languages with improved accuracy and fluency.
Text Summarization
Generating concise summaries of longer texts or articles.
Sentiment Analysis
Analyzing and understanding sentiments expressed in social media posts, reviews, and comments.
Difference Between NLP and LLM
NLP is Natural Language Processing, a field of artificial intelligence (AI). It consists of the development of the algorithms. NLP is a broader field than LLM, which consists of algorithms and techniques. NLP rules two approaches i.e. Machine learning and the analyze language data. Applications of NLP are-
Automotive routine task
Improve search
Search engine optimization
Analyzing and organizing large documents
Social Media Analytics.
while on the other hand, LLM is a Large Language Model, and is more specific to human- like text, providing content generation, and personalized recommendations.
What are the Advantages of Large Language Models?
Large Language Models (LLMs) come with several advantages that contribute to their widespread adoption and success in various applications:
LLMs can perform zero-shot learning, meaning they can generalize to tasks for which they were not explicitly trained. This capability allows for adaptability to new applications and scenarios without additional training.
LLMs efficiently handle vast amounts of data, making them suitable for tasks that require a deep understanding of extensive text corpora, such as language translation and document summarization.
LLMs can be fine-tuned on specific datasets or domains, allowing for continuous learning and adaptation to specific use cases or industries.
LLMs enable the automation of various language-related tasks, from code generation to content creation, freeing up human resources for more strategic and complex aspects of a project.
Challenges in Training of Large Language Models
There has been no doubt in the abilities of the LLMs in the future and this technology is part of most of the AI-powered applications which will be used by multiple users on a daily basis. But there are some drawbacks as well of LLMs.
For the successful training of a large language model, millions of dollars are required to set up that big computing power that can train the model utilizing parallel performance.
It requires months of training and then humans in the loop for the fine-tuning of models to achieve better performance.
Requiring a large amount of text corpus getting can be a challenging task because ChatGPT only is being accused of being trained on the data which has been scraped illegally and building an application for commercial purposes.
In the era of global warming and climate change, we cannot forget the carbon footprint of an LLM it is said that training a single AI model from scratch have carbon footprints equal to the carbon footprint of five cars in their whole lifetime which is a really serious concern.
Conclusion
Due to the challenges faced in training LLM transfer learning is promoted heavily to get rid of all of the challenges discussed above. LLM has the capability to bring revolution in the AI-powered application but the advancements in this field seem a bit difficult because just increasing the size of the model may increase its performance but after a particular time a saturation in the performance will come and the challenges to handle these models will be bigger than the performance boost achieved by further increasing the size of the models.
Frequently Asked Questions
1. What is a large language model?
A large language model is a powerful artificial intelligence system trained on vast amounts of text data.
2.What is a LLM in AI?
In AI, LLM refers to Large Language Models, such as GPT-3, designed for natural language understanding and generation.
3. What are the best Large Language Models?
Open AI,ChatGPT,GPT-3,GooseAI,Claude,Cohere,GPT-4.
4. How does LLM model work?
LLMs work by training on diverse language data, learning patterns, and relationships, enabling them to understand and generate human-like text.
5. What is an example of an LLM model?
GPT-3 (Generative Pre-trained Transformer 3) is an example of a state-of-the-art large language model in AI.
6. What are large language models for education?
Large Language Models are widely being in used for educational purposes:
Provides learning goals
Gives a critical summary of any topic to the students
Educate students on any topic they want to learn.
Ready to dive into the future? Mastering Generative AI and ChatGPT is your gateway to the cutting-edge world of AI. Perfect for tech enthusiasts, this course will teach you how to leverage Generative AI and ChatGPT with hands-on, practical lessons. Transform your skills and create innovative AI applications that stand out. Don't miss out on becoming an AI expert – Enroll now and start shaping the future!
Follow
10
Top 20 LLM (Large Language Models)
Similar Reads
Fine Tuning Large Language Model (LLM)
Top 20 LLM (Large Language Models)
Top 10 Open-Source LLM Models - Large Language Models
LLM vs GPT : Comparing Large Language Models and GPT
What is LLMOps (Large Language Model Operations)?
Multiturn Deviation in Large Language Model
What is PaLM 2: Google's Large Language Model Explained
Develop an LLM Application using Openai
Falcon LLM: Comprehensive Guide
Pi: World's Friendliest Chatbot Gets Even Smarter with Inflection-2.5 LLM
RAG Vs Fine-Tuning for Enhancing LLM Performance
NLP vs LLM: Understanding Key Differences
Securing LLM Systems Against Prompt Injection
Open Sorce LLM : Hugging Face Impacts on the AI Community
What is JanitorLLM : The Free Version LLM of Janitor AI 2024
Build RAG pipeline using Open Source Large Language Models
Future of Large Language Models
Exploring Multimodal Large Language Models
Large Language Models (LLMs) vs Transformers
Exploring the Technical Architecture Behind Large Language Models
Top 20 Applications of Large Language Models in Real-Life
From GPT-3 to GPT-4: Evolution and Innovations in Large Language Models
10 Free Resources to Learn Large Language Models (LLMs)
Explaining the language in Natural Language
Natural Language Processing(NLP) VS Programming Language
ML | JURASSIC-1 - Language Model
Universal Language Model Fine-tuning (ULMFit) in NLP
Building a Simple Language Translation Tool Using a Pre-Trained Translation Model
Convolutional Neural Network (CNN) in Machine Learning
Last Updated : 13 Mar, 2024
Generative Summary
Now you can generate the summary of any article of your choice.
Got it
Convolutional Neural Networks (CNNs) are a powerful tool for machine learning, especially in tasks related to computer vision. Convolutional Neural Networks, or CNNs, are a specialized class of neural networks designed to effectively process grid-like data, such as images.
In this article, we are going to discuss convolutional neural networks (CNN) in machine learning in detail.
What is Convolutional Neural Network(CNN)?
A Convolutional Neural Network (CNN) is a type of deep learning algorithm that is particularly well-suited for image recognition and processing tasks. It is made up of multiple layers, including convolutional layers, pooling layers, and fully connected layers. The architecture of CNNs is inspired by the visual processing in the human brain, and they are well-suited for capturing hierarchical patterns and spatial dependencies within images.
Key components of a Convolutional Neural Network include:
Convolutional Layers: These layers apply convolutional operations to input images, using filters (also known as kernels) to detect features such as edges, textures, and more complex patterns. Convolutional operations help preserve the spatial relationships between pixels.
Pooling Layers: Pooling layers downsample the spatial dimensions of the input, reducing the computational complexity and the number of parameters in the network. Max pooling is a common pooling operation, selecting the maximum value from a group of neighboring pixels.
Activation Functions: Non-linear activation functions, such as Rectified Linear Unit (ReLU), introduce non-linearity to the model, allowing it to learn more complex relationships in the data.
Fully Connected Layers: These layers are responsible for making predictions based on the high-level features learned by the previous layers. They connect every neuron in one layer to every neuron in the next layer.
CNNs are trained using a large dataset of labeled images, where the network learns to recognize patterns and features that are associated with specific objects or classes. Proven to be highly effective in image-related tasks, achieving state-of-the-art performance in various computer vision applications. Their ability to automatically learn hierarchical representations of features makes them well-suited for tasks where the spatial relationships and patterns in the data are crucial for accurate predictions. CNNs are widely used in areas such as image classification, object detection, facial recognition, and medical image analysis.
The convolutional layers are the key component of a CNN, where filters are applied to the input image to extract features such as edges, textures, and shapes.
The output of the convolutional layers is then passed through pooling layers, which are used to down-sample the feature maps, reducing the spatial dimensions while retaining the most important information. The output of the pooling layers is then passed through one or more fully connected layers, which are used to make a prediction or classify the image.
Convolutional Neural Network Design
The construction of a convolutional neural network is a multi-layered feed-forward neural network, made by assembling many unseen layers on top of each other in a particular order.
It is the sequential design that give permission to CNN to learn hierarchical attributes.
In CNN, some of them followed by grouping layers and hidden layers are typically convolutional layers followed by activation layers.
The pre-processing needed in a ConvNet is kindred to that of the related pattern of neurons in the human brain and was motivated by the organization of the Visual Cortex.
Convolutional Neural Network Training
CNNs are trained using a supervised learning approach. This means that the CNN is given a set of labeled training images. The CNN then learns to map the input images to their correct labels.
The training process for a CNN involves the following steps:
Data Preparation: The training images are preprocessed to ensure that they are all in the same format and size.
Loss Function: A loss function is used to measure how well the CNN is performing on the training data. The loss function is typically calculated by taking the difference between the predicted labels and the actual labels of the training images.
Optimizer: An optimizer is used to update the weights of the CNN in order to minimize the loss function.
Backpropagation: Backpropagation is a technique used to calculate the gradients of the loss function with respect to the weights of the CNN. The gradients are then used to update the weights of the CNN using the optimizer.
CNN Evaluation
After training, CNN can be evaluated on a held-out test set. A collection of pictures that the CNN has not seen during training makes up the test set. How well the CNN performs on the test set is a good predictor of how well it will function on actual data.
The efficiency of a CNN on picture categorization tasks can be evaluated using a variety of criteria. Among the most popular metrics are:
Accuracy: Accuracy is the percentage of test images that the CNN correctly classifies.
Precision: Precision is the percentage of test images that the CNN predicts as a particular class and that are actually of that class.
Recall: Recall is the percentage of test images that are of a particular class and that the CNN predicts as that class.
F1 Score: The F1 Score is a harmonic mean of precision and recall. It is a good metric for evaluating the performance of a CNN on classes that are imbalanced.
Different Types of CNN Models
LeNet
AlexNet
ResNet
GoogleNet
MobileNet
VGG
1.LeNet
LeNet is a pioneering convolutional neural network (CNN) architecture developed by Yann LeCun and his colleagues in the late 1990s. It was specifically designed for handwritten digit recognition, and was one of the first successful CNNs for image recognition.
LeNet consists of several layers of convolutional and pooling layers, followed by fully connected layers. The architecture includes two sets of convolutional and pooling layers, each followed by a subsampling layer, and then three fully connected layers.
The first convolutional layer uses a kernel of size 5×5 and applies 6 filters to the input image. The output of this layer is then passed through a pooling layer that reduces the spatial dimensions of the feature maps. The second convolutional layer uses a kernel of size 5×5 and applies 16 filters to the output of the first pooling layer. This is followed by another pooling layer and a subsampling layer.
The output of the subsampling layer is then passed through three fully connected layers, with 120, 84, and 10 neurons respectively. The last fully connected layer is used for classification, and produces a probability distribution over the 10 digits (0-9).
LeNet was trained on the MNIST dataset, which consists of 70,000 images of handwritten digits, and was able to achieve high recognition accuracy. The LeNet architecture, although relatively simple compared to current architectures, served as a foundation for many later CNNs, and it’s considered as a classic and simple architecture for image recognition tasks.
2.AlexNet
AlexNet is a convolutional neural network (CNN) architecture that was developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. It was the first CNN to win the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a major image recognition competition, and it helped to establish CNNs as a powerful tool for image recognition.
AlexNet consists of several layers of convolutional and pooling layers, followed by fully connected layers. The architecture includes five convolutional layers, three pooling layers, and three fully connected layers.
The first two convolutional layers use a kernel of size 11×11 and apply 96 filters to the input image. The third and fourth convolutional layers use a kernel of size 5×5 and apply 256 filters. The fifth convolutional layer uses a kernel of size 3×3 and applies 384 filters. The output of these convolutional layers is then passed through max-pooling layers that reduce the spatial dimensions of the feature maps.
The output of the pooling layers is then passed through three fully connected layers, with 4096, 4096, and 1000 neurons respectively. The last fully connected layer is used for classification, and produces a probability distribution over the 1000 ImageNet classes.
AlexNet was trained on the ImageNet dataset, which consists of 1.2 million images with 1000 classes, and was able to achieve high recognition accuracy. The AlexNet architecture was the first to show that CNNs could significantly outperform traditional machine learning methods in image recognition tasks, and was an important step in the development of deeper architectures like VGGNet, GoogleNet, and ResNet.
3. Resnet
ResNets (Residual Networks) are a type of deep learning algorithm that are particularly well-suited for image recognition and processing tasks. ResNets are known for their ability to train very deep networks without overfitting
ResNets are often used for keypoint detection tasks. Keypoint detection is the task of locating specific points on an object in an image. For example, keypoint detection can be used to locate the eyes, nose, and mouth on a human face.
ResNets are well-suited for keypoint detection tasks because they can learn to extract features from images at different scales.
ResNets have achieved state-of-the-art results on many keypoint detection benchmarks, such as the COCO Keypoint Detection Challenge and the MPII Human Pose Estimation Dataset.
4.GoogleNet
GoogleNet, also known as InceptionNet, is a type of deep learning algorithm that is particularly well-suited for image recognition and processing tasks. GoogleNet is known for its ability to achieve high accuracy on image classification tasks while using fewer parameters and computational resources than other state-of-the-art CNNs.
Inception modules are the key component of GoogleNet. They allow the network to learn features at different scales simultaneously, which improves the performance of the network on image classification tasks.
GoogleNet uses global average pooling to reduce the size of the feature maps before they are passed to the fully connected layers. This also helps to improve the performance of the network on image classification tasks.
GoogleNet uses factorized convolutions to reduce the number of parameters and computational resources required to train the network.
GoogleNet is a powerful tool for image classification, and it is being used in a wide variety of applications, such as GoogleNet can be used to classify images into different categories, such as cats and dogs, cars and trucks, and flowers and animals.
5. MobileNet
MobileNets are a type of CNN that are particularly well-suited for image recognition and processing tasks on mobile and embedded devices.
MobileNets are known for their ability to achieve high accuracy on image classification tasks while using fewer parameters and computational resources than other state-of-the-art CNNs.
MobileNets are also being used for keypoint detection tasks.
MobileNets have achieved state-of-the-art results on many keypoint detection benchmarks.
6. VGG
VGG is a type of convolutional neural network (CNN) that is known for its simplicity and effectiveness. VGGs are typically made up of a series of convolutional and pooling layers, followed by a few fully connected layers.
VGGs can be used by self-driving cars to detect and classify objects on the road, such as other vehicles, pedestrians, and traffic signs. This information can be used to help the car navigate safely.
VGGs are a powerful and versatile tool for image recognition tasks.
Applications of CNN
Image classification: CNNs are the state-of-the-art models for image classification. They can be used to classify images into different categories, such as cats and dogs, cars and trucks, and flowers and animals.
Object detection: CNNs can be used to detect objects in images, such as people, cars, and buildings. They can also be used to localize objects in images, which means that they can identify the location of an object in an image.
Image segmentation: CNNs can be used to segment images, which means that they can identify and label different objects in an image. This is useful for applications such as medical imaging and robotics.
Video analysis: CNNs can be used to analyze videos, such as tracking objects in a video or detecting events in a video. This is useful for applications such as video surveillance and traffic monitoring.
Advantages of CNN
CNNs can achieve state-of-the-art accuracy on a variety of image recognition tasks, such as image classification, object detection, and image segmentation.
CNNs can be very efficient, especially when implemented on specialized hardware such as GPUs.
CNNs are relatively robust to noise and variations in the input data.
CNNs can be adapted to a variety of different tasks by simply changing the architecture of the network.
Disadvantages of CNN
CNNs can be complex and difficult to train, especially for large datasets.
CNNs can require a lot of computational resources to train and deploy.
CNNs require a large amount of labeled data to train.
CNNs can be difficult to interpret, making it difficult to understand why they make the predictions they do.
Case Study of CNN for Diabetic retinopathy
Diabetic retinopathy also known as diabetic eye disease, is a medical state in which destruction occurs to the retina due to diabetes mellitus, It is a major cause of blindness in advance countries.
Diabetic retinopathy influence up to 80 percent of those who have had diabetes for 20 years or more.
The overlong a person has diabetes, the higher his or her chances of growing diabetic retinopathy.
It is also the main cause of blindness in people of age group 20-64.
Diabetic retinopathy is the outcome of destruction to the small blood vessels and neurons of the retina.
Conclusion
Convolutional neural networks (CNNs) are a powerful type of artificial neural network that are particularly well-suited for image recognition and processing tasks. They are inspired by the structure of the human visual cortex and have a hierarchical architecture that allows them to learn and extract features from images at different scales. CNNs have been shown to be very effective in a wide range of applications, including image classification, object detection, image segmentation, and image generation.
Frequently Asked Questions(FAQs)
1. What is a convolutional neural network (CNN)?
A Convolutional Neural Network (CNN) is a type of artificial neural network (ANN) that is specifically designed to handle image data. CNNs are inspired by the structure of the human visual cortex and have a hierarchical architecture that allows them to extract features from images at different scale
2. How does CNN work?
CNNs use a series of convolutional layers to extract features from images. Each convolutional layer applies a filter to the input image, and the output of the filter is a feature map. The feature maps are then passed through a series of pooling layers, which reduce their size and dimensionality. Finally, the output of the pooling layers is fed into a fully connected layer, which produces the final output of the network.
3. What are the different layers of CNN?
A CNN typically consists of three main types of layers:
Convolutional layer: The convolutional layer applies filters to the input image to extract local features.
Pooling layer: The pooling layer reduces the spatial size of the feature maps generated by the convolutional layer.
Fully connected layer: The fully connected layer introduces a more traditional neural network architecture, where each neuron is connected to every neuron in the previous layer.
4. What are some of the tools and frameworks for developing CNNs?
There are many popular tools and frameworks for developing CNNs, including:
TensorFlow: An open-source software library for deep learning developed by Google.
PyTorch: An open-source deep learning framework developed by Facebook.
MXNet: An open-source deep learning framework developed by Apache MXNet.
Keras: A high-level deep learning API for Python that can be used with TensorFlow, PyTorch, or MXNet.
5. What are some of the challenges of using CNNs?
CNNs can be challenging to train and require large amounts of data. Additionally, they can be computationally expensive, especially for large and complex models.
Are you passionate about data and looking to make one giant leap into your career? Our Data Science Course will help you change your game and, most importantly, allow students, professionals, and working adults to tide over into the data science immersion. Master state-of-the-art methodologies, powerful tools, and industry best practices, hands-on projects, and real-world applications. Become the executive head of industries related to Data Analysis, Machine Learning, and Data Visualization with these growing skills.