This guide provides practical examples for adding new content to your website.
Let’s create a sample blog post about recent advancements in AI:
Create a new file named 2025-06-15-recent-ai-advancements.md in the _posts directory
Add this content to the file:
---
title: "Recent Advancements in Artificial Intelligence"
date: 2025-06-15
categories:
- artificial-intelligence
- technology
tags:
- AI
- machine-learning
- deep-learning
- transformers
excerpt: "An overview of the most significant AI advancements in the past year and their implications for the future."
header:
image: "/images/blog/ai-advancements-header.jpg"
teaser: "/images/blog/ai-advancements-teaser.jpg"
---
# Recent Advancements in Artificial Intelligence
The field of artificial intelligence has seen remarkable progress in the past year. This post highlights some of the most significant breakthroughs and their potential impact.
## Multimodal Models
Multimodal AI models that can process and generate different types of data (text, images, audio) have reached new levels of capability. These models can:
- Understand complex relationships between different modalities
- Generate high-quality content across different formats
- Perform reasoning tasks that combine multiple types of information
## Advancements in Reinforcement Learning
Reinforcement learning has made significant strides in:
- More sample-efficient learning algorithms
- Better generalization to new environments
- Applications in robotics and autonomous systems
## Ethical AI and Governance
With the increasing power of AI systems, there has been greater focus on:
- Ensuring AI fairness and reducing biases
- Developing robust governance frameworks
- Creating standards for responsible AI development
## Conclusion
As AI continues to advance rapidly, we must balance innovation with responsible development. The coming years will likely bring even more transformative capabilities, making it essential for researchers, industry leaders, and policymakers to collaborate on ensuring these technologies benefit humanity.
mkdir -p images/blog
Let’s create a tutorial on deep learning with PyTorch:
Create a new file named pytorch-deep-learning.md in the _tutorials directory
Add this content to the file:
---
title: "Getting Started with Deep Learning in PyTorch"
date: 2025-06-15
categories:
- deep-learning
- python
tags:
- pytorch
- neural-networks
- deep-learning
- tutorial
header:
image: "/images/tutorials/pytorch-header.jpg"
teaser: "/images/tutorials/pytorch-teaser.jpg"
excerpt: "A beginner-friendly guide to building and training neural networks using PyTorch."
---
# Getting Started with Deep Learning in PyTorch
PyTorch is one of the most popular deep learning frameworks, known for its flexibility and intuitive design. This tutorial will guide you through the basics of building and training neural networks with PyTorch.
## Prerequisites
Before we begin, make sure you have the following installed:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import matplotlib.pyplot as plt
import numpy as np
Let’s create a basic neural network for image classification:
class SimpleNN(nn.Module):
def __init__(self):
super(SimpleNN, self).__init__()
self.flatten = nn.Flatten()
self.linear_relu_stack = nn.Sequential(
nn.Linear(28*28, 512),
nn.ReLU(),
nn.Linear(512, 512),
nn.ReLU(),
nn.Linear(512, 10)
)
def forward(self, x):
x = self.flatten(x)
logits = self.linear_relu_stack(x)
return logits
# Initialize the model
model = SimpleNN()
print(model)
PyTorch provides convenient data loaders for common datasets:
# Load MNIST dataset
transform = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.1307,), (0.3081,))
])
train_dataset = torchvision.datasets.MNIST(
root='./data',
train=True,
download=True,
transform=transform
)
test_dataset = torchvision.datasets.MNIST(
root='./data',
train=False,
download=True,
transform=transform
)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1000)
Now, let’s train our neural network:
def train(model, train_loader, epochs=5):
# Loss function and optimizer
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
# Training loop
for epoch in range(epochs):
running_loss = 0.0
for batch_idx, (data, target) in enumerate(train_loader):
# Zero the parameter gradients
optimizer.zero_grad()
# Forward pass
outputs = model(data)
# Calculate loss
loss = loss_fn(outputs, target)
# Backward pass and optimize
loss.backward()
optimizer.step()
# Print statistics
running_loss += loss.item()
if batch_idx % 100 == 99:
print(f'Epoch {epoch+1}, Batch {batch_idx+1}: Loss {running_loss/100:.3f}')
running_loss = 0.0
print('Training complete!')
# Train the model
train(model, train_loader)
Let’s visualize some predictions:
def visualize_predictions(model, test_loader):
# Get a batch of test data
dataiter = iter(test_loader)
images, labels = next(dataiter)
# Make predictions
with torch.no_grad():
outputs = model(images)
_, predicted = torch.max(outputs, 1)
# Plot results
plt.figure(figsize=(12, 6))
for i in range(10):
plt.subplot(2, 5, i+1)
plt.imshow(images[i][0], cmap='gray')
plt.title(f'Pred: {predicted[i]}, True: {labels[i]}')
plt.axis('off')
plt.tight_layout()
plt.show()
# Visualize predictions
visualize_predictions(model, test_loader)
Finally, let’s save our trained model:
# Save the model
torch.save(model.state_dict(), 'mnist_model.pth')
# Load the model
loaded_model = SimpleNN()
loaded_model.load_state_dict(torch.load('mnist_model.pth'))
loaded_model.eval() # Set to evaluation mode
In this tutorial, we’ve covered the basics of building, training, and using neural networks with PyTorch. This is just the beginning – PyTorch offers many more advanced features for building sophisticated deep learning models.
In future tutorials, we’ll explore more complex architectures like CNNs and RNNs, as well as techniques for transfer learning and fine-tuning.
3. Create the directory for your tutorial images:
mkdir -p images/tutorials
4. Add your header and teaser images to the directory
## Example 3: Adding a New Project
1. Create a new file named `sentiment-analysis-lstm.md` in the `_projects` directory
2. Add this content to the file:
```markdown
---
title: "Sentiment Analysis with LSTM Networks"
date: 2025-06-15
categories:
- natural-language-processing
- deep-learning
tags:
- sentiment-analysis
- lstm
- nlp
- tensorflow
header:
image: "/images/projects/sentiment-analysis-header.jpg"
teaser: "/images/projects/sentiment-analysis-teaser.jpg"
excerpt: "A project implementing LSTM networks for sentiment analysis on movie reviews."
---
# Sentiment Analysis with LSTM Networks
This project demonstrates how to implement a Long Short-Term Memory (LSTM) neural network for sentiment analysis of movie reviews.
## Project Overview
Sentiment analysis is the task of determining whether a piece of text expresses positive, negative, or neutral sentiment. In this project, I used the IMDB movie reviews dataset to train an LSTM model that can classify reviews as positive or negative.
## Technologies Used
- Python 3.8
- TensorFlow 2.6
- Keras
- NumPy
- Pandas
- Matplotlib
## Implementation Details
The model architecture consists of:
- An embedding layer to convert words to vectors
- An LSTM layer with 128 units
- A dropout layer for regularization
- A dense output layer with sigmoid activation
The implementation achieved 87.5% accuracy on the test set, demonstrating the effectiveness of LSTM networks for text classification tasks.
## Code Repository
The complete code is available on [GitHub](https://github.com/yourusername/sentiment-analysis-lstm).
## Results and Visualization
[Include visualizations of the training process, confusion matrix, or sample predictions]
## Future Improvements
Potential enhancements to this project include:
- Using bidirectional LSTM for better context understanding
- Implementing attention mechanisms
- Incorporating pre-trained word embeddings like GloVe or Word2Vec
- Exploring transfer learning with models like BERT or RoBERTa
mkdir -p images/projects
/images/bio-pic-2.jpg with your new image_config.yml:author:
name : "Rakib Al Fahad"
avatar : "/images/your-new-profile-pic.jpg"
/images/waterfront.jpg or update index.html:header:
image: "/images/your-new-banner.jpg"