Here is a list of best coursera courses for deep learning.
This deep learning specialization provided by deeplearning.ai and taught by Professor Andrew Ng, which is the best deep learning online course for everyone who want to learn deep learning. The DL specialization include 5 sub related courses:
This is the first course of the Deep learning specialization, which will tell you what is neural networks and deep learning. It includes 4 part: Introduction to deep learning; Neural Networks Basics; Shallow neural networks; Deep Neural Networks. For people who know or don’t know deep learning, this is the best beginning course for AI.
This is the second course of the Deep Learning Specialization, which focus on the “trick” to make the deep learning to work well:
- Understand industry best-practices for building deep learning applications;
- Be able to effectively use the common neural network “tricks”, including initialization, L2 and dropout regularization, Batch normalization, gradient checking;
- Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence;
- Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance;
- Be able to implement a neural network in TensorFlow.
This is the third course in the Deep Learning Specialization. Professor Andrew Ng will teach you how to build a successful machine learning project. Most of the course content are drawn from Professor Andrew Ng’s experience from Stanford, Google , Baidu and the industry:
- Understand how to diagnose errors in a machine learning system;
- Be able to prioritize the most promising directions for reducing error;
- Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance;
- Know how to apply end-to-end learning, transfer learning, and multi-task learning
This is the fourth course of the Deep Learning Specialization, which will teach you how to build convolutional neural networks and apply it to image processing:
- Understand how to build a convolutional neural network, including recent variations such as residual networks.
- Know how to apply convolutional networks to visual detection and recognition tasks.
- Know to use neural style transfer to generate art.
- Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.
This is the fifth course of the Deep Learning Specialization, which will tell you how to build models for natural language, audio, and other sequence data:
- Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
- Be able to apply sequence models to natural language problems, including text synthesis.
- Be able to apply sequence models to audio applications, including speech recognition and music synthesis.
This deep learning course provided by University of Toronto and taught by Geoffrey Hinton, which is a classical deep learning course. It provides both the basic algorithms and the practical tricks related with deep learning and neural networks, and put them to be used for machine learning.
This entry-level deep learning course belongs to the “Advanced Machine Learning Specialization“, which provided by Higher School of Economics and Yandex. The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image.
This deep learning application course belongs to the “Advanced Machine Learning Specialization“, which provided by Higher School of Economics and Yandex. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. The course will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and oftenly demonstrated in movies and TV-shows example of computer vision and AI.
This deep learning course provided by Intel and taught by Intel Engineers, which provides an introduction to Deep Learning. Students will explore important concepts in Deep Learning, train deep networks using Intel Nervana Neon, apply Deep Learning to various applications and explore new and emerging Deep Learning topics.
This deep learning business course is provided by Yonsei University and it has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open source DL software, which are the future enablers. The second part focuses on the core technologies of DL and ML systems, which include NN (Neural Network), CNN (Convolutional NN), and RNN (Recurrent NN) systems. The third part focuses on four TensorFlow Playground projects, where experience on designing DL NNs can be gained using an easy and fun yet very powerful application called the TensorFlow Playground. This course was designed to help you build business strategies and enable you to conduct technical planning on new DL and ML services and products.
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