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Learn Image Classification, earn certificates with free online courses from Stanford, Alexander Amini, Johns Hopkins, NPTEL and other top universities around the world. Read reviews to decide if a class is right for you.
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you'll learn about how to use convolutional neural networks to improve your image classification models.
Learn about fundamental deep learning architectures such as CNNs, RNNs, LSTMs, and GRUs for modeling image and sequential data.
Explore advanced ML techniques for supply chain optimization, including neural networks for demand prediction and image classification for quality control.
Train a custom image recognition model to identify cloud types using AutoML Vision, no ML expertise required. Hands-on lab in Google Cloud console.
Aprende a usar Kubernetes y Cloud Vision API para clasificar imágenes de Reddit y mostrar los resultados en una aplicación web.
Hands-on specialization in deep learning for computer vision, covering image classification, object detection, and advanced techniques. Gain practical skills using MATLAB for real-world applications.
Master PyTorch for neural networks: classification, CNNs, object detection, style transfer, and RNNs. Hands-on exercises in data preparation, model building, and advanced techniques for image and audio processing.
Master recurrent neural networks for time series prediction and NLP tasks using TensorFlow. Implement advanced architectures like GRUs and LSTMs for stock forecasting, image classification, and text analysis.
Master deep learning with Keras: from wine quality prediction to advanced image classification using CNNs, transfer learning, and GPU acceleration. Optimize models, implement data augmentation, and deploy in real-world scenarios.
Unlock the potential of deep learning by mastering Convolutional Neural Networks and Transfer Learning with hands-on TensorFlow and Keras experience for image and text classification.
Learn the basics of practical image processing techniques in MATLAB.
We will cover the basics of Tensorflow and Machine Learning in the initial sessions and advanced topics in the latter part. After this course, the students will be able to build ML models using Tensorflow.
Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.
Learn PyTorch for deep learning: tensors, gradient descent, linear regression, image processing, neural networks, CNNs, ResNet, regularization, data augmentation, and GANs for image generation.
Explore deep learning concepts and implement neural networks, including CNNs, using MATLAB for practical applications in image classification and beyond.
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