✅Convolutional Neural Network - Explained in simple terms with implementation details (code, techniques and best tips).
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1/ Imagine you have a picture, like a puzzle made of tiny colored pieces. A Convolutional Neural Network (CNN) is like a special brain that can look at the puzzle pieces in different ways to figure out what's in the picture. It's really good at finding patterns.
2/The CNN works by sliding a small window over the puzzle pieces. This window is like a magnifying glass that looks at a small group of pieces at a time. It takes these pieces and finds out what features they have, like colors and shapes.
3/ As the window moves across the puzzle, CNN keeps learning more and more about picture's details. Then, the CNN puts all this information together and decides what picture is showing. This way, it can tell us what's in picture without even needing to see whole thing at once.
4/ A Convolutional Neural Network (CNN) is a class of deep neural networks primarily designed to process and analyze grid-like data, such as images and videos. CNNs are particularly well-suited for tasks involving pattern recognition and feature extraction from visual data.
5/ Layers: A CNN consists of multiple layers that perform different tasks. The key layers are:
Convolutional Layer: This is where the filters slide over the image to detect patterns.
6/ Pooling Layer: This layer reduces dimensions of the image, making network faster and more efficient. It helps retain important features.
Fully Connected Layer: This layer connects the features learned by the previous layers to make final decisions about what's in the image.
7/ Convolution and Feature Extraction:
Convolutional layers in CNNs are responsible for detecting local patterns and features in images. They use filters to slide over the input image and perform a mathematical operation called convolution.
8/ Filters in convolutional layers learn to recognize various features like edges, textures, and more complex patterns. During training, CNNs adjust the filter weights so that they become sensitive to specific patterns in the input images.
9/ Transfer Learning:
Transfer learning is a technique in machine learning and deep learning where a model that has been trained on one task is reused as the starting point for a model on a second task.
10/In the context of CNNs, transfer learning involves using a pre-trained neural network model as a foundation for solving a related but different problem.
11/ Pre-trained Models as Feature Extractors:
Pre-trained CNNs are models that have already been trained on massive datasets, often for image classification tasks. They have learned to extract valuable features from images.
12/You can use these pre-trained models as feature extractors by removing the top classification layers and using the remaining layers to extract features from your own images.
13/Data Augmentation:
Data augmentation is a technique used to artificially increase the diversity and size of a dataset by applying various transformations to the original data. This can help improve the generalization of machine learning models and prevent overfitting.
14/ One of the main benefits of data augmentation is addressing overfitting. By introducing variability in the training data, the model becomes more robust and less likely to memorize the training samples.
15/ Batch Normalization:
Batch normalization is a technique that normalizes the inputs of a layer during training, aiming to stabilize and accelerate the training process. It helps to mitigate issues like vanishing/exploding gradients and can lead to faster convergence.
16/ Dropout:
Dropout is a regularization technique used to prevent overfitting. During training, dropout randomly deactivates a portion of neurons, forcing the network to learn more robust and general features.
17/ Residual Networks:
Residual connections, are used to mitigate vanishing gradient problem and make it easier for deep networks to train. They enable gradients to flow directly through network, helping in the training of very deep architectures.
18/ Global Average Pooling:
Global Average Pooling (GAP) is a technique used to replace fully connected (FC) layers in the final stages of a CNN. It helps reduce overfitting and improve localization.
19/ Object Detection with Region Proposal Networks (RPNs) and Anchor Boxes:
Object detection involves identifying objects in an image and drawing bounding boxes around them. Region Proposal Networks (RPNs) are a part of modern object detection methods like Faster R-CNN.
20/Anchor boxes are predefined boxes of different sizes and aspect ratios that the network uses to suggest potential object locations.
21/ Semantic Segmentation with U-Net:
It involves classifying each pixel in an image into a specific class label. U-Net is used for semantic segmentation that consists of an encoder and decoder, allowing the network to capture both high-level and fine-grained features.
22/ Style Transfer:
It is a technique that uses CNNs to transform artistic style of one image onto content of another image. It involves extracting style and content features from 2 images & combining them to create a new image that has content of one image and style of another.
23/ Content Loss and Style Loss in Neural Style Transfer:
In neural style transfer, content loss and style loss are used to define the objectives that guide the optimization process. Content loss measures the difference between content of generated image and the content image.
✅Optimizers and Regularizers, Batch normalization, Dropout - Explained in simple terms with implementation details (code & techniques)
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1/ Optimizers: Imagine teaching a computer how to learn from examples. Optimizers are like smart guides that help the computer figure out how to adjust its "thinking knobs" to get better at solving problems. They help the computer learn in small steps.
2/ Regularizers: Think of regularizers as a safety net for the computer's learning process. Computer can get too good at remembering exact examples and doesn't understand bigger picture. Regularizers gently encourage computer to generalize better by adding some extra rules
✅Neural Network Classification- Explained in simple terms with implementation details (code, techniques and best tips).
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1/ Imagine you have a bunch of colorful building blocks, like LEGO pieces. Each block can be a different color, like red, blue, or green. Now, you want to teach the blocks to sort themselves based on their colors.
2/ A neural network is like game where you teach blocks to sort themselves. It's made up of little parts called "neurons" that work together to learn things. These neurons are like tiny smart blocks. Each neuron looks at color of a block & decides if it's red, blue, or green.
✅Object Localization and tracking in Computer Vision - Explained in simple terms with implementation details (code, techniques and best tips).
Important Computer Vision Series - A quick thread 👇🏻🧵
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1/ Imagine you have a computer, that can look at pictures just like you do. This computer wants to be able to point at things in pictures, like how you point at things you see. But there's a little problem: the computer doesn't know where things are in the pictures!
2/ So, the computer starts to learn. It looks at lots and lots of pictures of different things: animals, cars, toys, and more. It tries to figure out where each thing is in the pictures. To do this, it looks for clues like colors, shapes, and patterns.
✅Image Classification in ML/Deep Learning- Explained in simple terms with implementation details (code, techniques and best tips).
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1/ Imagine you have a big box of different toys, like cars, dolls, and animals. You want to sort these toys and put them into different groups based on what they look like.
2/ Just like when you learn to tell the difference between a cat and a dog, the computer learns by looking at the important parts of the pictures, like the shapes and colors. It practices a lot until it gets really good at putting the pictures into the right groups.
✅Model Evaluation in Machine Learning - Explained in simple terms with implementation details (code, techniques and best tips).
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1/ Imagine you're baking cookies. You follow a recipe, put all the ingredients together, and then you bake them in the oven. But how do you know if your cookies turned out delicious? You taste them, right?
2/ In ML, Models are like smart helpers that can do things like tell you what's in a picture or predict tomorrow's weather. We train these models using lots of data, just like you learn from lots of experiences.
✅Clustering in Machine Learning - Explained in simple terms with implementation details (code, techniques and best tips).
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1/ Imagine you have a bunch of colorful marbles, but they are all mixed up and you want to organize them. Clustering in machine learning is like finding groups of marbles that are similar to each other.
2/ You know how some marbles are big and some are small? And some are red, while others are blue or green? Clustering helps us put marbles that are similar in size or color together in the same groups. So, all the big red marbles might be in one group, and rest in another group