Search result for Deep learning coursera Online Courses & Certifications
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Deep Learning in Computer Vision
by Anton Konushin , Alexey Artemov- 3.8
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Approx. 13 hours to complete
Deep learning added a huge boost to the already rapidly developing field of computer vision. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. Deep learning in optical flow estimation Deep learning models for image segmentation...
IBM AI Engineering Professional Certificate
- 0.0
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Learn how to provide business insights from big data using machine learning and deep learning techniques. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses....
Bayesian Methods for Machine Learning
by Daniil Polykovskiy , Alexander Novikov- 4.5
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Approx. 33 hours to complete
They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. We will also see applications of Bayesian methods to deep learning and how to generate new images with it. Learning with priors GP for machine learning...
Practical Reinforcement Learning
by Pavel Shvechikov , Alexander Panin- 4.3
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Approx. 26 hours to complete
Welcome to the Reinforcement Learning course. - using deep neural networks for RL tasks Reinforcement learning vs all Model-free reinforcement learning Supervised & Reinforcement Learning Combining supervised & reinforcement learning...
Deep Learning with PyTorch
by Packt Publishing- 3.9
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4.5 hours on-demand video
Build useful and effective deep learning models with the PyTorch Deep Learning framework This video course will get you up-and-running with one of the most cutting-edge deep learning libraries: PyTorch. Along the way he nurtured his interests in Deep Learning by attending Coursera and Udacity MOOCs....
$11.99
Natural Language Processing
by Anna Potapenko , Alexey Zobnin , Anna Kozlova , Sergey Yudin , Зимовнов Андрей Вадимович- 4.4
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Approx. 32 hours to complete
Throughout the lectures, we will aim at finding a balance between traditional and deep learning techniques in NLP and cover them in parallel. To succeed in that, we expect your familiarity with the basics of linear algebra and probability theory, machine learning setup, and deep neural networks....
Introduction to Deep Learning
by Evgeny Sokolov , Зимовнов Андрей Вадимович , Alexander Panin , Ekaterina Lobacheva , Nikita Kazeev- 4.5
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Approx. 34 hours to complete
The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. What Deep Learning is and is not Deep learning as a language Deep Learning for images Training tips and tricks for deep CNNs Learning new tasks with pre-trained CNNs Deep learning for sequences...
IBM Machine Learning Professional Certificate
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Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning....
PyTorch: Deep Learning with PyTorch - Masterclass!: 2-in-1
by Packt Publishing- 3.9
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7.5 hours on-demand video
Start your journey with PyTorch to build useful & effective models with the PyTorch Deep Learning framework from scratch The first course, Deep Learning with PyTorch, covers building useful and effective deep learning models with the PyTorch Deep Learning framework. Along the way, he nurtured his interests in Deep Learning by attending Coursera and Udacity MOOCs....
$11.99
Getting started with TensorFlow 2
by Dr Kevin Webster- 4.9
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Approx. 26 hours to complete
In this course you will learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading models....
