Convolutional Neural Networks | complete deep learning course in Telugu | part 7 скачать в хорошем качестве

Convolutional Neural Networks | complete deep learning course in Telugu | part 7

In this video, I explain Convolutional Neural Networks (CNNs) — one of the most powerful architectures in Deep Learning, especially for image-related tasks. The explanation is in Telugu, with step-by-step clarity. You’ll learn: What is a CNN and why it is used The difference between CNNs and traditional neural networks Convolution operation with filters/kernels Stride, padding, and feature maps explained Pooling layers (Max Pooling & Average Pooling) Fully Connected Layers in CNN Real-world applications: Image classification, object detection, face recognition, etc. Step-by-step example with a small image and filter By the end, you’ll have a strong foundation in CNN architecture and how it processes images. ఈ వీడియోలో మనం Convolutional Neural Networks (CNNs) గురించి తెలుగులో పూర్తిగా తెలుసుకుందాం. ఇవి Image Processing & Computer Vision లో అత్యంత శక్తివంతమైన Deep Learning architectures. మీరు ఈ వీడియోలో తెలుసుకోగల విషయాలు: CNN అంటే ఏమిటి? దాని అవసరం ఏమిటి? CNN మరియు సాంప్రదాయ Neural Networks మధ్య తేడా Convolution operation (filters/kernels) ఎలా పనిచేస్తుంది Stride, Padding, Feature Maps వివరణ Pooling Layers (Max Pooling, Average Pooling) Fully Connected Layers పాత్ర CNNs ఎక్కడ వాడతాం? (Image Classification, Object Detection, Face Recognition వంటివి) ఒక చిన్న example తో step-by-step explanation ఈ వీడియో తర్వాత, మీరు CNN architecture గురించి మూలాధారమైన అవగాహన పొందుతారు. #CNN #ConvolutionalNeuralNetworks #DeepLearning #AIinTelugu #MLinTelugu #ImageClassification #ComputerVision #NeuralNetworks #TeluguTech #DeepLearningInTelugu #CodingInTelugu #AIForBeginners #DataScienceTelugu #ObjectDetection #FaceRecognition