✨ Unlocking Image Clarity: A Comprehensive Guide to Super-Resolution Techniques ✨

📖 Table of Contents Unlocking Image Clarity: A Comprehensive Guide to Super-Resolution Techniques Introduction Configuring Your Development Environment Need Help Configuring Your Development Environment? What Is Super-Resolution? Usual Problems with Low-Resolution Imagery Traditional Computer Vision A...

🏷️ #ArtificialIntelligence #ComputerVision #DeepLearning #ImageProcessing #MachineLearning #TechnologyApplications #Tutorial

✨ Adversarial Learning with Keras and TensorFlow (Part 3): Exploring Adversarial Attacks Using Neural Structured Learning (NSL) ✨

📖 Table of Contents Adversarial Learning with Keras and TensorFlow (Part 3): Exploring Adversarial Attacks Using Neural Structured Learning (NSL) Introduction to Advanced Adversarial Techniques in Machine Learning Harnessing NSL for Robust Model Training: Insights from Part 2 Deep Dive into…...

🏷️ #AdversarialLearning #DeepLearning #ImageProcessing #Keras #MachineLearning #NeuralNetworks #NeuralStructuredLearning #TensorFlow #Tutorial

✨ Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch ✨

📖 Table of Contents Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch Introduction What Will We Do in This Blog? Why Are We Using Vector Embeddings? What’s Coming Next? Configuring Your Development Environment Installing Docker (Required for…...

🏷️ #Docker #MachineLearning #OpenSearch #SearchEngines #SemanticSearch #Tutorial #VectorSearch

✨ FastAPI Meets OpenAI CLIP: Build and Deploy with Docker ✨

📖 Table of Contents FastAPI Meets OpenAI CLIP: Build and Deploy with Docker Building on FastAPI Foundations What’s Next? What Is OpenAI CLIP? How OpenAI CLIP Works: Understanding Text-Image Matching and Contrastive Learning Contrastive Pre-Training: Aligning Text and Image Embeddings Shared…...

🏷️ #AIApplications #DockerDeployment #FastAPIDevelopment #MachineLearning #Tutorial

✨ Introduction to Gradio for Building Interactive Applications ✨

📖 Table of Contents Introduction to Gradio for Building Interactive Applications What Is Gradio? High-Impact Projects Powered by Gradio AUTOMATIC1111’s Stable Diffusion Web UI oobabooga’s Text Generation Web UI The Next Generation of Gradio: What’s New in Version 5 Performance Improvements…...

🏷️ #Gradio #MachineLearning #Python #SoftwareDevelopment #Tutorial

✨ Implementing Approximate Nearest Neighbor Search with KD-Trees ✨

📖 Table of Contents Implementing Approximate Nearest Neighbor Search with KD-Trees Introduction to Approximate Nearest Neighbor Search Mathematical Foundation KD-Trees for Approximate Nearest Neighbor Search Construction of KD-Trees Querying with KD-Trees Step 1: Forward Traversal Step 2: Computing th...

🏷️ #ApproximateNearestNeighbor #KDTree #MachineLearning #NearestNeighborAlgorithm #Tutorial

✨ Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset ✨

📖 Table of Contents Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset Introduction Dataset and Task Overview About the Dataset What Are We Detecting? Defining Pothole Severity Can the Pothole Severity Logic Be Improved? Configuring Your Development Environment Training…...

🏷️ #ComputerVision #DeepLearning #ObjectDetection #Tutorial #YOLO

✨ People Tracker with YOLOv12 and Centroid Tracker ✨

📖 Table of Contents People Tracker with YOLOv12 and Centroid Tracker Introduction Why People Tracker Monitoring Matters How YOLOv12 Enables Real-Time Applications Configuring Your Development Environment Downloading the Input Video Install gdown Download the Video Visualizing the Inference and Trackin...

🏷️ #ComputerVision #ObjectDetection #PeopleTracker #Tutorial #YOLOv12

✨ Object Detection and Visual Grounding with Qwen 2.5 ✨

📖 Table of Contents Object Detection and Visual Grounding with Qwen 2.5 Introduction and Types of Spatial Understanding Object Detection Visual Grounding and Counting Understanding Relationships How Spatial Understanding Works in Qwen 2.5 VL Models Prompt Structure Task-Specific Instruction Object or…...

🏷️ #ObjectDetection #Qwen25 #Qwen25 #Tutorial #VisualGrounding

✨ AI for Healthcare: Fine-Tuning Google’s PaliGemma 2 for Brain Tumor Detection ✨

📖 Table of Contents AI for Healthcare: Fine-Tuning Google’s PaliGemma 2 for Brain Tumor Detection Configuring Your Development Environment Setup and Imports Load the Brain Tumor Dataset Format Dataset to PaliGemma Format Display Train Image and Label COCO Format BBox to…...

🏷️ #FineTuning #ObjectDetection #PaliGemma2 #PEFT #QLoRA #Transformers #Tutorial #VisionLanguageModels

✨ Object Tracking with YOLOv8 and Python ✨

📖 Table of Contents Object Tracking with YOLOv8 and Python YOLOv8: Reliable Object Detection and Tracking Understanding YOLOv8 Architecture Mosaic Data Augmentation Anchor-Free Detection C2f (Coarse-to-Fine) Module Decoupled Head Loss Object Detection and Tracking with YOLOv8 Object Detection Object T...

🏷️ #AdvancedComputerVision #DataScience #DeepLearning #MachineLearning #ObjectDetection #ObjectTracking #ProgrammingTutorials #Tutorial #VideoObjectTracking #YOLO

✨ Image Processing with Gemini Pro ✨

📖 Table of Contents Image Processing with Gemini Pro Getting Started with Gemini Pro: An Overview Gemini Pro Setup Integrating Google AI Python SDK with Gemini Pro Image Processing with Gemini Pro: Python Code Generation Comprehensive List of GenAI Models Compatible…...

🏷️ #ArtificialIntelligence #ChatGPT #DeepLearning #Gemini #GeminiPro #GenAI #GenerativeAI #GoogleCloud #ImageProcessing #Python #Transformers #Tutorial #VertexAI

✨ Meet BLIP: The Vision-Language Model Powering Image Captioning ✨

📖 Table of Contents Meet BLIP: The Vision-Language Model Powering Image Captioning What Is Image Captioning and Why Is It Challenging? Why It’s Challenging Why Traditional Vision Tasks Aren’t Enough Configuring Your Development Environment A Brief History of Image Captioning Models…...

🏷️ #ComputerVision #DeepLearning #ImageCaptioning #MultimodalAI #Tutorial

✨ Setting Up LLaVA/BakLLaVA with vLLM: Backend and API Integration ✨

📖 Table of Contents Setting Up LLaVA/BakLLaVA with vLLM: Backend and API Integration Why vLLM for Multimodal Inference The Challenges of Serving Image + Text Prompts at Scale Why Vanilla Approaches Fall Short How vLLM Solves Real-World Production Workloads Configuring Your…...

🏷️ #DeepLearning #ModelDeployment #Tutorial #vLLM

#CNN #DeepLearning #Python #Tutorial

Lesson: Building a Convolutional Neural Network (CNN) for Image Classification

This lesson will guide you through building a CNN from scratch using TensorFlow and Keras to classify images from the CIFAR-10 dataset.

---

Part 1: Setup and Data Loading

First, we import the necessary libraries and load the CIFAR-10 dataset. This dataset contains 60,000 32x32 color images in 10 classes.

import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
import numpy as np

# Load the CIFAR-10 dataset
(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()

# Check the shape of the data
print("Training data shape:", x_train.shape)
print("Test data shape:", x_test.shape)

#TensorFlow #Keras #DataLoading

---

Part 2: Data Exploration and Preprocessing

We need to prepare the data before feeding it to the network. This involves:
• Normalization: Scaling pixel values from the 0-255 range to the 0-1 range.
• One-Hot Encoding: Converting class vectors (integers) to a binary matrix.

Let's also visualize some images to understand our data.

# Define class names for CIFAR-10
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

# Visualize a few images
plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(x_train[i])
    plt.xlabel(class_names[y_train[i][0]])
plt.show()

# Normalize pixel values to be between 0 and 1
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

# One-hot encode the labels
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)
y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)

#DataPreprocessing #Normalization #Visualization

---

Part 3: Building the CNN Model

Now, we'll construct our CNN model. A common architecture consists of a stack of Conv2D and MaxPooling2D layers, followed by Dense layers for classification.

• Conv2D: Extracts features (like edges, corners) from the input image.
• MaxPooling2D: Reduces the spatial dimensions (downsampling), which helps in making the feature detection more robust.
• Flatten: Converts the 2D feature maps into a 1D vector.
• Dense: A standard fully-connected neural network layer.

model = models.Sequential()

# Convolutional Base
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))

# Flatten and Dense Layers
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax')) # 10 output classes

# Print the model summary
model.summary()

#ModelBuilding #CNN #KerasLayers

---

Part 4: Compiling the Model

Before training, we need to configure the learning process. This is done via the compile() method, which requires:
• Optimizer: An algorithm to update the model's weights (e.g., 'adam').
• Loss Function: A function to measure how inaccurate the model is during training (e.g., 'categorical_crossentropy' for multi-class classification).
• Metrics: Used to monitor the training and testing steps (e.g., 'accuracy').

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

#ModelCompilation #Optimizer #LossFunction

---

#Pandas #DataAnalysis #Python #DataScience #Tutorial

Top 30 Pandas Functions & Methods

This lesson covers 30 essential Pandas functions for data manipulation and analysis, each with a standalone example and its output.

---

1. pd.DataFrame()
Creates a new DataFrame (a 2D labeled data structure) from various inputs like dictionaries or lists.

import pandas as pd
data = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data)
print(df)

col1  col2
0     1     3
1     2     4

---

2. pd.Series()
Creates a new Series (a 1D labeled array).

import pandas as pd
s = pd.Series([10, 20, 30, 40], name='MyNumbers')
print(s)

0    10
1    20
2    30
3    40
Name: MyNumbers, dtype: int64

---

3. pd.read_csv()
Reads data from a CSV file into a DataFrame. (Assuming a file data.csv exists).

# Create a dummy csv file first
with open('data.csv', 'w') as f:
    f.write('Name,Age\nAlice,25\nBob,30')

df = pd.read_csv('data.csv')
print(df)

Name  Age
0  Alice   25
1    Bob   30

---

4. df.to_csv()
Writes a DataFrame to a CSV file.

import pandas as pd
df = pd.DataFrame({'Name': ['Charlie'], 'Age': [35]})
# index=False prevents writing the DataFrame index to the file
df.to_csv('output.csv', index=False)
# You can check that 'output.csv' has been created.
print("File 'output.csv' created.")

File 'output.csv' created.

#PandasIO #DataFrame #Series

---

5. df.head()
Returns the first n rows of the DataFrame (default is 5).

import pandas as pd
data = {'Name': ['A', 'B', 'C', 'D', 'E', 'F'], 'Value': [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)
print(df.head(3))

Name  Value
0    A      1
1    B      2
2    C      3

---

6. df.tail()
Returns the last n rows of the DataFrame (default is 5).

import pandas as pd
data = {'Name': ['A', 'B', 'C', 'D', 'E', 'F'], 'Value': [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)
print(df.tail(2))

Name  Value
4    E      5
5    F      6

---

7. df.info()
Provides a concise summary of the DataFrame, including data types and non-null values.

import pandas as pd
import numpy as np
data = {'col1': [1, 2, 3], 'col2': [4.0, 5.0, np.nan], 'col3': ['A', 'B', 'C']}
df = pd.DataFrame(data)
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   col1    3 non-null      int64  
 1   col2    2 non-null      float64
 2   col3    3 non-null      object 
dtypes: float64(1), int64(1), object(1)
memory usage: 200.0+ bytes

---

8. df.shape
Returns a tuple representing the dimensionality (rows, columns) of the DataFrame.

import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
print(df.shape)

(2, 3)

#DataInspection #PandasBasics

---

9. df.describe()
Generates descriptive statistics for numerical columns (count, mean, std, min, max, etc.).

import pandas as pd
df = pd.DataFrame({'Age': [22, 38, 26, 35, 29]})
print(df.describe())