Convolutional Neural Networks (CNN) Courses and Certifications

Brandon Rohrer

How Convolutional Neural Networks Work, in Depth

Comprehensive exploration of convolutional neural networks, covering their structure, operation, and training process, including key concepts like filtering, pooling, and backpropagation.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

Krish Naik

Alexnet Architecture In-Depth Discussion Along With Code - Deep Learning Advanced CNN

In-depth exploration of AlexNet architecture, including mathematical operations, layers, and implementation code for advanced deep learning and convolutional neural networks.

• YouTube
• 23 minutes
• Self-Paced
• Free Video

Abhishek Thakur

Captcha Recognition Using PyTorch - Convolutional-RNN + CTC Loss

Learn to implement a Convolutional-RNN model for captcha recognition using PyTorch. Covers data preparation, model architecture, CTC loss, and training/inference processes.

• YouTube
• 1 hour 17 minutes
• Self-Paced
• Free Video

Pysource

Train Mask R-CNN for Image Segmentation

Learn to train and implement Mask R-CNN for image segmentation using free online GPU resources. Covers dataset preparation, model training, and practical application for object detection and segmentation.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

DigitalSreeni

Pretrained CNN Features for Semantic Segmentation Using Random Forest

Explore semantic segmentation using VGG16 features and Random Forest, offering improved results for limited training data compared to U-net.

• YouTube
• 22 minutes
• Self-Paced
• Free Video

DigitalSreeni

Malarial Cell Classification Using CNN

Learn to classify malarial cells using CNN in Python. Covers dataset acquisition, TensorFlow setup, network design, and result analysis for detecting parasitized and healthy cells.

• YouTube
• 49 minutes
• Self-Paced
• Free Video

Aladdin Persson

EfficientNet Paper Walkthrough

Comprehensive walkthrough of the EfficientNet paper, exploring model scaling, proposed methods, and results. Offers insights into innovative approaches for improving neural network efficiency.

• YouTube
• 26 minutes
• Self-Paced
• Free Video

Code With Aarohi

InceptionV3 - Inception Network

Detailed exploration of InceptionV3 architecture, covering its improvements over previous versions, including factorized convolutions, RMSProp optimizer, and label smoothing for enhanced CNN performance.

• YouTube
• 23 minutes
• Self-Paced
• Free Video

Alfredo Canziani

Convolutional Neural Networks

Explore convolutional neural networks, from basic concepts to modern implementations. Learn about kernels, biological inspirations, CNN evolution, and their advantages in image processing and feature extraction.

• YouTube
• 1 hour 38 minutes
• Self-Paced
• Free Video

Alexander Amini;  MIT

Convolutional Neural Networks

Explore convolutional neural networks for computer vision, covering feature extraction, convolution operations, and applications in autonomous navigation and medical imaging.

• YouTube
• 49 minutes
• Self-Paced
• Free Video

Alexander Amini;  MIT

Convolutional Neural Networks

Explore deep learning for computer vision, covering CNNs, object detection, and image segmentation. Learn about architectures, applications, and impacts in various fields.

• YouTube
• 35 minutes
• Self-Paced
• Free Video

Brandon Rohrer

How Convolution Works

Guided tour of 2D convolution for CNNs and image processing, covering element-wise operations, feature detection, kernels, and practical applications.

• YouTube
• 20 minutes
• Self-Paced
• Free Video

Credential

Deep Learning

147089 ratings at Coursera

Master neural networks, deep learning techniques, and their real-world applications. Build and optimize various architectures using Python and TensorFlow to tackle cutting-edge AI challenges in multiple domains.

• Coursera
• 12 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 5 courses

James Briggs

Convolutional Neural Nets Explained and Implemented in Python - PyTorch

Comprehensive guide to Convolutional Neural Networks: theory, implementation, and practical applications using Python and PyTorch for computer vision tasks like image classification.

• YouTube
• 35 minutes
• Self-Paced
• Free Video

Code With Aarohi

Explained Identity Block and Convolution Block in ResNet - Residual Networks

Practical explanation of identity and convolutional blocks in ResNet, including line-by-line code breakdown and discussion of residual concepts in deep learning architectures.

• YouTube
• 24 minutes
• Self-Paced
• Free Video