Deep Learning

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Course Details

Course Features

Instructor led Sessions
The most traditional way to learn with increased visibility, monitoring, and control over learners with ease to learn at any time from internet-connected devices.
Real-life Case Studies
Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.
Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.
Each certification associated with the program is affiliated with the top universities providing edge to gain epitome in the course.
Instructor led Sessions
With no limits to learning and in-depth vision from all-time available support to resolve all your queries related to the course.

Deep Learning

Oranium Tech introducing some fantastic content on Deep Learning. Machine learning is a rapidly growing field in Computer science, to the extent that it became a trendy buzzword. It seems it is everywhere today – from self-driving cars to automatic cancerous tumor detection. Deep learning is a sub-field in the world of Machine learning mainly based around neural networks – a conceptual model of the human brain that has been around for decades but is getting more and more attention in the last several years. Using this model we are capable of achieving wonderful results in solving complex problems that were once out of our reach. In this course we will start our journey in the world of deep learning – we will start by getting familiar with basic concepts and theory, all the way down to actual hands-on practice. We will cover important topics such as Convolutional neural networks (Convolution, Correlation, and Filtering), Generative Adversarial Networks, Deep reinforcement learning, common tools, and much more.

Course Syllabus

• Linear algebra
• Calculus
• Statistics
• Basic programming in Python
• Machine learning

• Intro, History, capabilities, the perceptron
• Neural network learning: Back-Propagation
• Practical network training
• Autoencoders, Batch-normalization
• Why does it work? Overfitting and generalization

• Intro to CNNs, Convolution, Correlation, FIltering.
• CNN architectures
• Detection and Segmentation
• Visualizing and Understanding
• Advanced CNNs for computer vision

• Recurrent Neural networks (RNNs)
• Advanced RNN: LSTM, GRU,
• Generative Adversarial Networks (GANs)
• Advanced GANs

• Deep reinforcement learning
• Deep Learning: Good -> Great
• Visual Question Answering, Visual Dialog
• Novel deep methods (Deep internal learning, Deep image prior)
• Recent works
• How to stay updated?

• Tensorflow
• Pytorch

• Computer Vision
• Natural Language Processing (NLP )
• Sequence modeling
• Natural / Biological signals

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