Rajilesh P

I'm a freelance software developer and tech enthusiast.

[Rajilesh P @my_updates]

Transfer Learning with ResNet 18 The Domain Shift Challenge | Season 1 | Part 32
#TransferLearning #DomainShift #ResNet18 #FineTuning #PyTorch #ComputerVision #ResNet #PretrainedModels #ImageClassification #DomainAdaptation #ArtificialIntelligence #AI #SoanceLearningHub

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Mastering Fine Tuning Adapting ResNet 18 for ASL Recognition | Season 1 | Part 31
#FineTuning #TransferLearning #ResNet18 #ASLRecognition #SignLanguageRecognition #PyTorch #ComputerVision #ImageClassification #ResNet #ArtificialIntelligence #AI #Python #SoanceLearningHub

[Rajilesh P @my_updates]

Mastering TensorBoard in PyTorch | Season 1 | Part 30
#TensorBoard #PyTorch #DeepLearning #MachineLearning #NeuralNetworks #ModelTraining #ModelMonitoring #ModelDebugging #Overfitting #ComputerVision #ArtificialIntelligence #AI #Python #DataScience #SoanceLearningHub

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Curing Overfitting with Regularization | Season 1 | Part 29
#Overfitting #Regularization #PyTorch #DeepLearning #MachineLearning #Dropout #WeightDecay #L2Regularization #DataAugmentation #EarlyStopping #NeuralNetworks #ComputerVision #ArtificialIntelligence #AI #SoanceLearningHub

[Rajilesh P @my_updates]

Unboxing the Black Box – Grad CAM in PyTorch | Season 1 | Part 28
#GradCAM #PyTorch #ExplainableAI #XAI #ComputerVision #DeepLearning #CNN #NeuralNetworks #MachineLearning #ImageClassification #ArtificialIntelligence #AI #Python #ModelInterpretability #SoanceLearningHub

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The Regularization Stack Fixing Neural Network Overfitting | Season 1 | Part 27
#Regularization #Overfitting #NeuralNetworks #DeepLearning #MachineLearning #Dropout #WeightDecay #L2Regularization #DataAugmentation #EarlyStopping #AI #ModelGeneralization #SoanceLearningHub

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Diagnosing Fixing ML Model Errors Bias Variance Tradeoff | Season 1 | Part 26
#BiasVarianceTradeoff #MachineLearning #ModelEvaluation #Overfitting #Underfitting #BiasVariance #DeepLearning #ModelGeneralization #ArtificialIntelligence #AI #Python #DataScience #SoanceLearningHub

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Building a Real World PyTorch DataLoader Taming the Raw Image | Season 1 | Part 25 #PyTorch #DataLoader #PyTorchDataLoader #ComputerVision #DeepLearning #ImageDataset #DataPreprocessing #Torchvision #CNN #MachineLearning #ArtificialIntelligence #AI #Python #SoanceLearningHub

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Building a Bulletproof Deep Learning Pipeline | Season 1 | Part 24
🚀 Welcome to **Building a Bulletproof Deep Learning Pipeline | Season 1 | Part 24**! In this episode, you'll learn how to build a complete, production-ready Deep Learning pipeline using PyTorch. From collecting and preprocessing data to training, validating, testing, and deploying models, you'll understand every stage required to create reliable AI systems that perform well in real-world environments. This tutorial brings together everything you've learned so far and demonstrates how each component fits into a robust end-to-end workflow. 📚 In this video you'll learn: • What is a Deep Learning pipeline? • Data collection and dataset organization • Data cleaning and preprocessing • Data augmentation techniques • Training, validation, and test dataset splits • Building a PyTorch training pipeline • Choosing loss functions and optimizers • Learning rate scheduling • Model evaluation and performance metrics • Saving and loading trained models • Inference and deployment workflow • Best practices for building production-ready AI systems 💡 **Acknowledgement:** Some of the concepts and best practices discussed in this video are inspired by the educational work of **@AndrejKarpathy**. If you want to deepen your understanding of AI, Neural Networks, and Deep Learning, be sure to check out the **@AndrejKarpathy** YouTube channel for outstanding educational content. This is **Season 1, Part 24** of the **Deep Learning Foundations** series, where you'll build the knowledge needed to understand and develop modern AI and Computer Vision systems. Perfect for: ✅ Python Beginners ✅ PyTorch Beginners ✅ Data Science Learners ✅ Machine Learning Engineers ✅ Deep Learning Developers ✅ AI Enthusiasts ✅ Computer Vision Developers ✅ Students and Researchers Subscribe to **Soance Learning Hub** for practical tutorials on Python, PyTorch, Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, OpenCV, NumPy, TensorFlow, Data Science, Web Development, Mobile App Development, and modern software engineering. 👍 If you found this video helpful, don't forget to Like, Share, and Subscribe for more AI and programming tutorials! #DeepLearning #PyTorch #MachineLearning #ArtificialIntelligence #AI #ComputerVision #DeepLearningPipeline #DataPreprocessing #ModelTraining #ModelEvaluation #NeuralNetworks #Python #LearnAI #OpenCV #SoanceLearningHub

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PyTorch Optimizer Showdown | Season 1 | Part 23
🚀 Welcome to **PyTorch Optimizer Showdown | Season 1 | Part 23**! In this episode, you'll compare the most popular optimization algorithms in PyTorch and learn when to use each one. From **SGD** and **Momentum** to **RMSProp** and **Adam**, you'll explore how each optimizer updates neural network weights, their strengths and weaknesses, and the trade-offs between training speed, convergence, stability, and generalization. This tutorial provides practical insights and PyTorch examples to help you choose the right optimizer for your Deep Learning projects. 📚 In this video you'll learn: • What is an optimizer in Deep Learning? • Stochastic Gradient Descent (SGD) • SGD with Momentum • RMSProp explained • Adam optimizer explained • Comparing convergence speed and stability • Optimizer trade-offs and use cases • Choosing learning rates and hyperparameters • Implementing optimizers in PyTorch • Best practices for optimizer selection • Real-world Deep Learning applications This is **Season 1, Part 23** of the **Deep Learning Foundations** series, where you'll build the knowledge needed to understand and develop modern AI and Computer Vision systems. 💡 Acknowledgement: This video is inspired by concepts and teaching approaches shared by  @AndrejKarpathy  If you want to deepen your understanding of AI, Machine Learning, and Neural Networks, I highly recommend checking out the  @AndrejKarpathy  YouTube channel for outstanding educational content. Perfect for: ✅ Python Beginners ✅ PyTorch Beginners ✅ Data Science Learners ✅ Machine Learning Engineers ✅ Deep Learning Developers ✅ AI Enthusiasts ✅ Computer Vision Developers ✅ Students and Researchers Subscribe to **Soance Learning Hub** for practical tutorials on Python, PyTorch, Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, OpenCV, NumPy, TensorFlow, Data Science, Web Development, Mobile App Development, and modern software engineering. 👍 If you found this video helpful, don't forget to Like, Share, and Subscribe for more AI and programming tutorials! #PyTorch #Optimizers #SGD #Momentum #RMSProp #Adam #DeepLearning #MachineLearning #ArtificialIntelligence #AI #Python #NeuralNetworks #ComputerVision #LearnAI #SoanceLearningHub

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Mastering Adam The Architecture of Adaptive Optimizers | Season 1 | Part 22
🚀 Welcome to **Mastering Adam: The Architecture of Adaptive Optimizers | Season 1 | Part 22**! In this episode, you'll explore the **Adam (Adaptive Moment Estimation)** optimizer, one of the most powerful and widely used optimization algorithms in Deep Learning. Learn how Adam combines the strengths of Momentum and Adaptive Learning Rates to train Neural Networks faster, more efficiently, and with greater stability. This tutorial explains the intuition, mathematics, and PyTorch implementation of Adam, helping you understand why it has become the default optimizer for many modern AI applications. 📚 In this video you'll learn: • What is the Adam optimizer? • Why optimization matters in Deep Learning • How Adam combines Momentum and RMSProp • Understanding first and second moments • Adaptive learning rates explained • Bias correction in Adam • Adam vs SGD, Momentum, and RMSProp • Choosing learning rates and hyperparameters • Implementing Adam in PyTorch • Best practices for training Deep Learning models • Real-world applications of Adam This is **Season 1, Part 22** of the **Deep Learning Foundations** series, where you'll build the knowledge needed to understand and develop modern AI and Computer Vision systems. Perfect for: ✅ Python Beginners ✅ PyTorch Beginners ✅ Data Science Learners ✅ Machine Learning Engineers ✅ Deep Learning Developers ✅ AI Enthusiasts ✅ Computer Vision Developers ✅ Students and Researchers Subscribe to **Soance Learning Hub** for practical tutorials on Python, PyTorch, Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, OpenCV, NumPy, TensorFlow, Data Science, Web Development, Mobile App Development, and modern software engineering. 👍 If you found this video helpful, don't forget to Like, Share, and Subscribe for more AI and programming tutorials! #AdamOptimizer #Adam #PyTorch #DeepLearning #MachineLearning #Optimization #Momentum #RMSProp #ArtificialIntelligence #AI #Python #NeuralNetworks #ComputerVision #LearnAI #SoanceLearningHub

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The PyTorch Trade off Deploying Pre Trained Vision Models | Season 1 | Part 21
🚀 Welcome to **The PyTorch Trade-off: Deploying Pre-Trained Vision Models | Season 1 | Part 21**! In this episode, you'll learn how to choose and deploy the right pre-trained Computer Vision model using PyTorch. Discover the trade-offs between accuracy, inference speed, model size, memory consumption, and deployment requirements, and understand how to select the best architecture for your real-world AI application. From lightweight edge devices to high-performance cloud servers, this tutorial will help you make informed decisions when working with pre-trained vision models. 📚 In this video you'll learn: • What are pre-trained vision models? • Transfer learning with PyTorch • Fine-tuning vs feature extraction • Comparing ResNet, DenseNet, EfficientNet, MobileNet, ConvNeXt, and Vision Transformer (ViT) • Accuracy vs speed trade-offs • Choosing models for mobile, edge, and cloud deployment • Memory and computational requirements • Deploying pre-trained models in production • Best practices for real-world Computer Vision applications • Selecting the right model for your project This is **Season 1, Part 21** of the **Deep Learning Foundations** series, where you'll build the knowledge needed to understand and develop modern AI and Computer Vision systems. Perfect for: ✅ Python Beginners ✅ PyTorch Beginners ✅ Data Science Learners ✅ Machine Learning Engineers ✅ Deep Learning Developers ✅ AI Enthusiasts ✅ Computer Vision Developers ✅ Students and Researchers Subscribe to **Soance Learning Hub** for practical tutorials on Python, PyTorch, Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, OpenCV, NumPy, TensorFlow, Data Science, Web Development, Mobile App Development, and modern software engineering. 👍 If you found this video helpful, don't forget to Like, Share, and Subscribe for more AI and programming tutorials! #PyTorch #ComputerVision #TransferLearning #PretrainedModels #DeepLearning #MachineLearning #ResNet #EfficientNet #VisionTransformer #MobileNet #ConvNeXt #ArtificialIntelligence #AI #Python #SoanceLearningHub

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Image Classification Architecture A Strategic Masterclass| Season 1 | Part 20
🚀 Welcome to **Image Classification Architecture: A Strategic Masterclass | Season 1 | Part 20**! In this episode, you'll explore the evolution of the world's most influential image classification architectures and understand the strategic innovations that transformed Computer Vision. Starting with **LeNet-5** and progressing through **AlexNet**, **VGG**, **GoogLeNet (Inception)**, **ResNet**, **DenseNet**, and **EfficientNet**, you'll discover how each architecture solved major challenges in Deep Learning and paved the way for today's state-of-the-art AI systems. Rather than simply learning the architectures, you'll understand the engineering decisions, trade-offs, and breakthroughs that made each one successful. 📚 In this video you'll learn: • The evolution of image classification architectures • How LeNet-5 introduced modern CNNs • Why AlexNet sparked the Deep Learning revolution • VGG and the benefits of deeper neural networks • GoogLeNet (Inception) and multi-scale feature extraction • ResNet and residual learning • DenseNet and efficient feature reuse • EfficientNet and compound model scaling • Comparing architecture accuracy, speed, and efficiency • Choosing the right CNN architecture for your project • Real-world Computer Vision applications This is **Season 1, Part 20** of the **Deep Learning Foundations** series, where you'll build the knowledge needed to understand and develop modern AI and Computer Vision systems. Perfect for: ✅ Python Beginners ✅ PyTorch Beginners ✅ Data Science Learners ✅ Machine Learning Engineers ✅ Deep Learning Developers ✅ AI Enthusiasts ✅ Computer Vision Developers ✅ Students and Researchers Subscribe to **Soance Learning Hub** for practical tutorials on Python, PyTorch, Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, OpenCV, NumPy, TensorFlow, Data Science, Web Development, Mobile App Development, and modern software engineering. 👍 If you found this video helpful, don't forget to Like, Share, and Subscribe for more AI and programming tutorials! #ImageClassification #CNN #LeNet #AlexNet #VGG #GoogLeNet #ResNet #DenseNet #EfficientNet #DeepLearning #ComputerVision #PyTorch #ArtificialIntelligence #AI #SoanceLearningHub

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Building Classification Metrics from Scratch | Season 1 | Part 19
🚀 Welcome to **Building Classification Metrics from Scratch | Season 1 | Part 19**! In this episode, you'll build the most important Machine Learning classification metrics from scratch using Python. Instead of relying on built-in libraries, you'll implement the Confusion Matrix, Accuracy, Precision, Recall, F1-Score, and other evaluation metrics step by step to gain a deeper understanding of how AI models are evaluated. This hands-on tutorial will help you understand the mathematics behind model evaluation and prepare you to build reliable Machine Learning systems from the ground up. 📚 In this video you'll learn: • Why classification metrics matter • Understanding the Confusion Matrix • True Positives (TP), True Negatives (TN) • False Positives (FP) and False Negatives (FN) • Building Accuracy from scratch • Calculating Precision and Recall • Implementing the F1-Score • Understanding Specificity and Sensitivity • Evaluating imbalanced datasets • Writing classification metrics in Python • Comparing manual calculations with machine learning libraries This is *Season 1, Part 19* of the *Deep Learning Foundations* series, where you'll build the knowledge needed to understand and develop modern AI and Machine Learning systems. Perfect for: ✅ Python Beginners ✅ PyTorch Beginners ✅ Data Science Learners ✅ Machine Learning Engineers ✅ Deep Learning Developers ✅ AI Enthusiasts ✅ Computer Vision Developers ✅ Students and Researchers Subscribe to *Soance Learning Hub* for practical tutorials on Python, PyTorch, Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, OpenCV, NumPy, TensorFlow, Data Science, Web Development, Mobile App Development, and modern software engineering. 👍 If you found this video helpful, don't forget to Like, Share, and Subscribe for more AI and programming tutorials! #MachineLearning #ClassificationMetrics #ModelEvaluation #ConfusionMatrix #Precision #Recall #F1Score #Accuracy #DeepLearning #PyTorch #ArtificialIntelligence #AI #Python #DataScience #SoanceLearningHub

[Rajilesh P @my_updates]

The Diagnostic Toolkit Evaluation Metrics for Machine Learning| Season 1 | Part 18
🚀 Welcome to **The Diagnostic Toolkit: Evaluation Metrics for Machine Learning | Season 1 | Part 18**! In this episode, you'll learn how to properly evaluate Machine Learning models using the metrics that AI engineers rely on in real-world applications. You'll discover why accuracy alone is often misleading and how metrics like Precision, Recall, F1-Score, ROC-AUC, and the Confusion Matrix provide a much deeper understanding of model performance. Whether you're building classification models or preparing for AI interviews, this tutorial will help you choose the right evaluation metrics for every Machine Learning problem. 📚 In this video you'll learn: • Why model evaluation is important • The limitations of accuracy • Understanding the Confusion Matrix • True Positives, True Negatives, False Positives, and False Negatives • Precision and when to use it • Recall and when it matters • F1-Score explained • ROC Curve and AUC • Evaluating models with imbalanced datasets • Choosing the right evaluation metric • Best practices for Machine Learning model evaluation This is **Season 1, Part 18** of the **Deep Learning Foundations** series, where you'll build the knowledge needed to understand and develop modern AI and Machine Learning systems. Perfect for: ✅ Python Beginners ✅ PyTorch Beginners ✅ Data Science Learners ✅ Machine Learning Engineers ✅ Deep Learning Developers ✅ AI Enthusiasts ✅ Computer Vision Developers ✅ Students and Researchers Subscribe to **Soance Learning Hub** for practical tutorials on Python, PyTorch, Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, OpenCV, NumPy, TensorFlow, Data Science, Web Development, Mobile App Development, and modern software engineering. 👍 If you found this video helpful, don't forget to Like, Share, and Subscribe for more AI and programming tutorials! #MachineLearning #EvaluationMetrics #ModelEvaluation #ConfusionMatrix #Precision #Recall #F1Score #ROC #AUC #DeepLearning #PyTorch #ArtificialIntelligence #AI #Python #SoanceLearningHub

[Rajilesh P @my_updates]

Mastering Batch Normalization with LeNet 5 | Season 1 | Part 17
🚀 Welcome to **Mastering Batch Normalization with LeNet-5 | Season 1 | Part 17**! In this episode, you'll learn how **Batch Normalization** improves the performance, stability, and training speed of Convolutional Neural Networks using the classic **LeNet-5** architecture in PyTorch. Discover how Batch Normalization normalizes intermediate activations, enables faster convergence, reduces internal covariate shift, and helps build deeper, more accurate neural networks. This tutorial explains the theory and implementation of Batch Normalization with practical examples, helping you understand why it has become a standard component of modern Deep Learning architectures. 📚 In this video you'll learn: • What is Batch Normalization? • Why Batch Normalization is important • Understanding internal covariate shift • How Batch Normalization normalizes activations • Where to place Batch Normalization in LeNet-5 • BatchNorm after convolutional layers • BatchNorm with ReLU activation • Training LeNet-5 with Batch Normalization • Comparing performance with and without BatchNorm • Faster convergence and improved generalization • Implementing BatchNorm in PyTorch • Real-world applications in Computer Vision This is **Season 1, Part 17** of the **Deep Learning Foundations** series, where you'll build the knowledge needed to understand and develop modern AI and Computer Vision systems. Perfect for: ✅ Python Beginners ✅ PyTorch Beginners ✅ Data Science Learners ✅ Machine Learning Engineers ✅ Deep Learning Developers ✅ AI Enthusiasts ✅ Computer Vision Developers ✅ Students and Researchers Subscribe to **Soance Learning Hub** for practical tutorials on Python, PyTorch, Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, OpenCV, NumPy, TensorFlow, Data Science, Web Development, Mobile App Development, and modern software engineering. 👍 If you found this video helpful, don't forget to Like, Share, and Subscribe for more AI and programming tutorials! #BatchNormalization #BatchNorm #LeNet5 #PyTorch #CNN #ConvolutionalNeuralNetwork #DeepLearning #MachineLearning #ComputerVision #ArtificialIntelligence #AI #Python #ImageClassification #LearnAI #SoanceLearningHub

[Rajilesh P @my_updates]

LeNet 5 on MNIST Building Spatial Hierarchies in PyTorch | Season 1 | Part 16
🚀 Welcome to **LeNet-5 on MNIST: Building Spatial Hierarchies in PyTorch | Season 1 | Part 16**! In this episode, you'll build the classic LeNet-5 Convolutional Neural Network using PyTorch and train it on the MNIST handwritten digit dataset. You'll learn how convolutional layers, pooling layers, activation functions, and fully connected layers work together to identify patterns and classify handwritten numbers. This tutorial also explains how CNNs build spatial hierarchies, starting with simple visual features such as edges and gradually learning more complex shapes and digit-level patterns. 📚 In this video you'll learn: • What is the LeNet-5 architecture? • Why LeNet-5 is important in Deep Learning • Understanding the MNIST handwritten digit dataset • Building LeNet-5 with PyTorch • Convolutional layers and feature extraction • Pooling and spatial dimension reduction ��� Activation functions inside LeNet-5 • Flattening feature maps • Fully connected classification layers • How CNNs build spatial hierarchies • Training and evaluating the model • Making predictions on handwritten digits This is **Season 1, Part 16** of the **Deep Learning Foundations** series, where you'll build the knowledge needed to understand and develop modern AI and Computer Vision systems. Perfect for: ✅ Python Beginners ✅ PyTorch Beginners ✅ Data Science Learners ✅ Machine Learning Engineers ✅ Deep Learning Developers ✅ AI Enthusiasts ✅ Computer Vision Developers ✅ Students and Researchers Subscribe to **Soance Learning Hub** for practical tutorials on Python, PyTorch, Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, OpenCV, NumPy, TensorFlow, Data Science, Web Development, Mobile App Development, and modern software engineering. 👍 Like, Share, and Subscribe for more practical AI and programming tutorials! #LeNet5 #MNIST #PyTorch #CNN #ConvolutionalNeuralNetwork #ComputerVision #DeepLearning #MachineLearning #ImageClassification #ArtificialIntelligence #AI #Python #NeuralNetworks #LearnAI #SoanceLearningHub

[Rajilesh P @my_updates]

Convolutional Layers Kernels | Season 1 | Part 15
🚀 Welcome to **Convolutional Layers & Kernels | Season 1 | Part 15**! In this episode, you'll dive into **Convolutional Layers** and **Kernels (Filters)**, the core components that make Convolutional Neural Networks (CNNs) so powerful. Learn how kernels slide across an image, detect edges, textures, patterns, and shapes, and generate feature maps that enable AI models to understand visual information. This tutorial explains the inner workings of convolutional layers with practical examples, helping you build a strong foundation in Deep Learning and Computer Vision using PyTorch. 📚 In this video you'll learn: • What are Convolutional Layers? • What are Kernels (Filters)? • How kernels scan an image • Understanding receptive fields • Creating feature maps • Stride and padding explained • Feature extraction through multiple convolutional layers • Why convolution is efficient for image processing • Implementing convolutional layers in PyTorch • Real-world Computer Vision applications This is **Season 1, Part 14** of the **Deep Learning Foundations** series, where you'll build the knowledge needed to understand and develop modern AI and Computer Vision systems. Perfect for: ✅ Python Beginners ✅ PyTorch Beginners ✅ Data Science Learners ✅ Machine Learning Engineers ✅ Deep Learning Developers ✅ AI Enthusiasts ✅ Computer Vision Developers ✅ Students and Researchers Subscribe to **Soance Learning Hub** for practical tutorials on Python, PyTorch, Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, OpenCV, NumPy, TensorFlow, Data Science, Web Development, Mobile App Development, and modern software engineering. 👍 If you found this video helpful, don't forget to Like, Share, and Subscribe for more AI and programming tutorials! #CNN #ConvolutionalLayers #Kernels #Filters #ComputerVision #DeepLearning #MachineLearning #PyTorch #ArtificialIntelligence #AI #Python #OpenCV #ImageProcessing #LearnAI #SoanceLearningHub

[Rajilesh P @my_updates]

Constructing the Modern CNN Blueprint | Season 1 | Part 14

🚀 Welcome to Constructing the Modern CNN Blueprint | Season 1 | Part 14!
In this episode, you'll learn how to design a modern Convolutional Neural Network (CNN) architecture using PyTorch. Building on the fundamentals of convolution, you'll discover how convolutional layers, activation functions, pooling layers, normalization, and fully connected layers are combined to create high-performing image classification models.
This tutorial walks you through the blueprint of a modern CNN, helping you understand the design principles used in today's Computer Vision systems.

📚 In this video you'll learn:
• Designing a modern CNN architecture
• Choosing convolutional layers and filters
• Using ReLU activation functions
• Max Pooling and feature reduction
• Batch Normalization explained
• Dropout for reducing overfitting
• Flattening feature maps
• Fully connected output layers
• Building a CNN in PyTorch
• Best practices for CNN architecture design
• Real-world Computer Vision applications

This is Season 1, Part 14 of the Deep Learning Foundations series, where you'll build the knowledge needed to understand and develop modern AI and Computer Vision systems.

Perfect for:
✅ Python Beginners
✅ PyTorch Beginners
✅ Data Science Learners
✅ Machine Learning Engineers
✅ Deep Learning Developers
✅ AI Enthusiasts
✅ Computer Vision Developers
✅ Students and Researchers
Subscribe to Soance Learning Hub for practical tutorials on Python, PyTorch, Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, OpenCV, NumPy, TensorFlow, Data Science, Web Development, Mobile App Development, and modern software engineering.
👍 If you found this video helpful, don't forget to Like, Share, and Subscribe for more AI and programming tutorials!
#CNN #ConvolutionalNeuralNetwork #PyTorch #ComputerVision #DeepLearning #MachineLearning #ArtificialIntelligence #AI #Python #ImageClassification #BatchNormalization #OpenCV #LearnAI #Programming #SoanceLearningHub