🤖🧠 The Little Book of Deep Learning – A Complete Summary and Chapter-Wise Overview

🗓️ 08 Oct 2025
📚 AI News & Trends

In the ever-evolving world of Artificial Intelligence, deep learning continues to be the driving force behind breakthroughs in computer vision, speech recognition and natural language processing. For those seeking a clear, structured and accessible guide to understanding how deep learning really works, “The Little Book of Deep Learning” by François Fleuret is a gem. This ...

#DeepLearning #ArtificialIntelligence #MachineLearning #NeuralNetworks #AIGuides #FrancoisFleuret

🤖🧠 Build a Large Language Model From Scratch: A Step-by-Step Guide to Understanding and Creating LLMs

🗓️ 08 Oct 2025
📚 AI News & Trends

In recent years, Large Language Models (LLMs) have revolutionized the world of Artificial Intelligence (AI). From ChatGPT and Claude to Llama and Mistral, these models power the conversational systems, copilots, and generative tools that dominate today’s AI landscape. However, for most developers and learners, the inner workings of these systems remain a mystery until now. ...

#LargeLanguageModels #LLM #ArtificialIntelligence #DeepLearning #MachineLearning #AIGuides

🤖🧠 Mastering Large Language Models: Top #1 Complete Guide to Maxime Labonne’s LLM Course

🗓️ 22 Oct 2025
📚 AI News & Trends

In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have become the foundation of modern AI innovation powering tools like ChatGPT, Claude, Gemini and countless enterprise AI applications. However, building, fine-tuning and deploying these models require deep technical understanding and hands-on expertise. To bridge this knowledge gap, Maxime Labonne, a leading AI ...

#LLM #ArtificialIntelligence #MachineLearning #DeepLearning #AIEngineering #LargeLanguageModels

🤖🧠 The Ultimate #1 Collection of AI Books In Awesome-AI-Books Repository

🗓️ 22 Oct 2025
📚 AI News & Trends

Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the 21st century. From powering self-driving cars to enabling advanced conversational AI like ChatGPT, AI is redefining how humans interact with machines. However, mastering AI requires a strong foundation in theory, mathematics, programming and hands-on experimentation. For enthusiasts, students and professionals seeking ...

#ArtificialIntelligence #AIBooks #MachineLearning #DeepLearning #AIResources #TechBooks

🤖🧠 AI Projects : A Comprehensive Showcase of Machine Learning, Deep Learning and Generative AI

🗓️ 27 Oct 2025
📚 AI News & Trends

Artificial Intelligence (AI) is transforming industries across the globe, driving innovation through automation, data-driven insights and intelligent decision-making. Whether it’s predicting house prices, detecting diseases or building conversational chatbots, AI is at the core of modern digital solutions. The AI Project Gallery by Hema Kalyan Murapaka is an exceptional GitHub repository that curates a wide ...

#AI #MachineLearning #DeepLearning #GenerativeAI #ArtificialIntelligence #GitHub

In Python, image processing unlocks powerful capabilities for computer vision, data augmentation, and automation—master these techniques to excel in ML engineering interviews and real-world applications! 🖼 

# PIL/Pillow Basics - The essential image library
from PIL import Image

# Open and display image
img = Image.open("input.jpg")
img.show()

# Convert formats
img.save("output.png")
img.convert("L").save("grayscale.jpg")  # RGB to grayscale

# Basic transformations
img.rotate(90).save("rotated.jpg")
img.resize((300, 300)).save("resized.jpg")
img.transpose(Image.FLIP_LEFT_RIGHT).save("mirrored.jpg")

more explain: https://hackmd.io/@husseinsheikho/imageprocessing

#Python #ImageProcessing #ComputerVision #Pillow #OpenCV #MachineLearning #CodingInterview #DataScience #Programming #TechJobs #DeveloperTips #AI #DeepLearning #CloudComputing #Docker #BackendDevelopment #SoftwareEngineering #CareerGrowth #TechTips #Python3

💡 Building a Simple Convolutional Neural Network (CNN)

Constructing a basic Convolutional Neural Network (CNN) is a fundamental step in deep learning for image processing. Using TensorFlow's Keras API, we can define a network with convolutional, pooling, and dense layers to classify images. This example sets up a simple CNN to recognize handwritten digits from the MNIST dataset.

import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import mnist
import numpy as np

# 1. Load and preprocess the MNIST dataset
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Reshape images for CNN: (batch_size, height, width, channels)
# MNIST images are 28x28 grayscale, so channels = 1
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255

# 2. Define the CNN architecture
model = models.Sequential()

# First Convolutional Block
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))

# Second Convolutional Block
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))

# Flatten the 3D output to 1D for the Dense layers
model.add(layers.Flatten())

# Dense (fully connected) layers
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax')) # Output layer for 10 classes (digits 0-9)

# 3. Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Print a summary of the model layers
model.summary()

# 4. Train the model (uncomment to run training)
# print("\nTraining the model...")
# model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_split=0.1)

# 5. Evaluate the model (uncomment to run evaluation)
# print("\nEvaluating the model...")
# test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
# print(f"Test accuracy: {test_acc:.4f}")

Code explanation: This script defines a simple CNN using Keras. It loads and normalizes MNIST images. The Sequential model adds Conv2D layers for feature extraction, MaxPooling2D for downsampling, a Flatten layer to transition to 1D, and Dense layers for classification. The model is then compiled with an optimizer, loss function, and metrics, and a summary of its architecture is printed. Training and evaluation steps are included as commented-out examples.

#Python #DeepLearning #CNN #Keras #TensorFlow

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By: @CodeProgrammer ✨

💡 Keras: Building Neural Networks Simply

Keras is a high-level deep learning API, now part of TensorFlow, designed for fast and easy experimentation. This guide covers the fundamental workflow: defining, compiling, training, and using a neural network model.

from tensorflow import keras
from tensorflow.keras import layers

# Define a Sequential model
model = keras.Sequential([
    # Input layer with 64 neurons, expecting flat input data
    layers.Dense(64, activation="relu", input_shape=(784,)),
    # A hidden layer with 32 neurons
    layers.Dense(32, activation="relu"),
    # Output layer with 10 neurons for 10-class classification
    layers.Dense(10, activation="softmax")
])

model.summary()

• Model Definition: keras.Sequential creates a simple, layer-by-layer model.
• layers.Dense is a standard fully-connected layer. The first layer must specify the input_shape.
• activation functions like "relu" introduce non-linearity, while "softmax" is used on the output layer for multi-class classification to produce probabilities.

# (Continuing from the previous step)
model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

print("Model compiled successfully.")

• Compilation: .compile() configures the model for training.
• optimizer is the algorithm used to update the model's weights (e.g., 'adam' is a popular choice).
• loss is the function the model tries to minimize during training. sparse_categorical_crossentropy is common for integer-based classification labels.
• metrics are used to monitor the training and testing steps. Here, we track accuracy.

import numpy as np

# Create dummy training data
x_train = np.random.random((1000, 784))
y_train = np.random.randint(10, size=(1000,))

# Train the model
history = model.fit(
    x_train,
    y_train,
    epochs=5,
    batch_size=32,
    verbose=0 # Hides the progress bar for a cleaner output
)

print(f"Training complete. Final accuracy: {history.history['accuracy'][-1]:.4f}")
# Output (will vary):
# Training complete. Final accuracy: 0.4570

• Training: The .fit() method trains the model on your data.
• x_train and y_train are your input features and target labels.
• epochs defines how many times the model will see the entire dataset.
• batch_size is the number of samples processed before the model is updated.

# Create a single dummy sample to test
x_test = np.random.random((1, 784))

# Get the model's prediction
predictions = model.predict(x_test)
predicted_class = np.argmax(predictions[0])

print(f"Predicted class: {predicted_class}")
print(f"Confidence scores: {predictions[0].round(2)}")
# Output (will vary):
# Predicted class: 3
# Confidence scores: [0.09 0.1  0.1  0.12 0.1  0.09 0.11 0.1  0.09 0.1 ]

• Prediction: .predict() is used to make predictions on new, unseen data.
• For a classification model with a softmax output, this returns an array of probabilities for each class.
• np.argmax() is used to find the index (the class) with the highest probability score.

#Keras #TensorFlow #DeepLearning #MachineLearning #Python

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By: @CodeProgrammer ✨