Deep learning is a subset of machine learning that involves training neural networks with multiple layers to recognize patterns and make decisions based on data. It drives advances in areas such as computer vision, natural language processing and autonomous systems, enabling breakthroughs in image and speech recognition, medical diagnostics and personalized recommendations. This article lists the best deep learning courses that provide comprehensive knowledge and practical skills needed to excel in this transformative field.
Specialization in deep learning
The Deep Learning specialization gives you the skills to build and optimize neural networks using Python and TensorFlow, covering architectures such as CNNs, RNNs, LSTMs and Transformers. It allows learners to apply these skills to real AI cases, gaining theoretical and practical knowledge to advance their careers in AI technology.
Professional TensorFlow Developer Certificate
This course teaches how to create and train neural networks using TensorFlow through a hands-on program. It helps you gain skills to build AI-powered applications, prepare for the Google TensorFlow certificate exam, and apply your knowledge to real-world projects including image recognition and natural language processing .
Introduction to Deep Learning and Neural Networks with Keras
This course introduces deep learning and compares it to artificial neural networks. It covers various models, teaching unsupervised models like autoencoders and restricted Boltzmann machines and supervised models like CNNs and recurrent networks. It also helps learners create their first deep learning model using the Keras library.
TensorFlow 2 for Deep Learning specialization
This specialization allows machine learning researchers and practitioners to develop practical skills in TensorFlow. It covers building, training, and evaluating models, customizing workflows with TensorFlow's lower-level APIs, and developing probabilistic models using the TensorFlow probability library.
Deep learning at NYU
This course covers the history of deep learning, neural networks, gradient descent, and backpropagation. It includes practical implementations using PyTorch, covering ConvNets, RNNs, autoencoders, GANs, transformers and graph neural networks.
Introduction to Deep Learning with PyTorch
This course teaches the basics of deep learning and how to build neural networks using PyTorch. Learners have the opportunity to work on hands-on projects such as image classification, style transfer and text generation. The program includes neural networks, CNNs, RNNs and deployment models.
Practical deep learning for coders
This course explains how to configure a GPU server and create deep learning models for computer vision, NLP, and recommendation systems. The course covers CNNs, RNNs and their practical applications.
Probabilistic Deep Learning with TensorFlow 2
This course delves into the probabilistic aspect of deep learning using TensorFlow. It focuses on managing uncertainty in real-world datasets, which is essential for applications such as autonomous vehicles and medical diagnostics. He also teaches how to develop probabilistic models with TensorFlow Probability, covering Bayesian neural networks and variational autoencoders.
Machine Learning with Python: from linear models to Deep Learning
This course teaches machine learning principles and algorithms for making predictions from training data. It covers topics such as representation, overfitting, regularization, clustering, classification, reinforcement learning, SVMs, and neural networks.
Deep learning applications for computer vision
This course teaches computer vision, starting with classical approaches and then applying Deep Learning methods to the same problems. It explores modern machine learning tools covering topics such as image classification, object detection, segmentation, facial recognition, and pose estimation.
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