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A major element of the AI world, deep learning, is gaining popularity among students because of its close connection with neural networks, programming, and other multi-faced prospects. Studying deep learning opens a lot of doors of opportunity for their careers. Here are the top 10 online deep learning courses they can take up in 2022.
Price: INR 455
This course focuses on the application of state-of-the-art Deep Learning architectures to various medical imaging challenges. You will tackle several different tasks, including cancer segmentation, pneumonia classification, cardiac detection, Interpretability, and many more. This course is for Python developers and Machine Learning engineers who want to learn how to tackle real-world problems occurring on a daily basis in the field of medical imaging with the help of Deep Convolutional Neural Networks.
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Price: INR 455
In this course, you will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state-of-the-art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor-Critic. The model is so strong that for the first time in the courses, students will be able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field).
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Price: INR 455
There are lots of different tools in the space of Deep Learning and in this course, you will learn the most important and most progressive ones so that when you're done with Deep Learning A-Z™ your skills are on the cutting edge of today's technology. If you are just starting out into Deep Learning, then you will find this course extremely useful. Deep Learning A-Z™ is structured around special coding blueprint approaches meaning that you won't get bogged down in unnecessary programming or mathematical complexities and instead you will be applying Deep Learning techniques from very early on in the course. You will build your knowledge from the ground up and you will see how with every tutorial you are getting more and more confident.
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Price: INR 2560
This course will take a look at NLP (natural language processing) with deep learning. Previously, you have learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. This course will bring you more architecture knowledge.
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Price: Unknown
In this course, instructor Harshit Tyagi provides a complete guide to understanding NLP using recurrent neural networks (RNNs). Harshit begins by introducing you to word encodings and using TensorFlow for tokenization. He describes the important concept of word embeddings and shows you how to use TensorFlow to classify movie reviews and project vectors. Harshit discusses RNNs and long short-term memory (LSTM), then shows you how to improve the movie review classifier from earlier in the course. He concludes with a discussion of how you can train RNNs to predict the next word in a sentence, which in turn allows you to generate some original text.
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Price: Unknown
In this course, instructor Kumaran Ponnambalam provides a simplified path to understand various optimization and tuning options available for deep learning models and shows you how to use these options to improve models. He begins by reviewing Deep Learning, including artificial neural networks and architectures. Next, Kumaran discusses the process of hyperparameter tuning. He examines the building blocks of neural networks and the levers available to tune them. Kumaran offers recommendations and best practices. Then he concludes with an end-to-end tuning example.
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Price: Unknown
PyTorch is quickly becoming one of the most popular deep learning frameworks around, as well as a must-have skill in your artificial intelligence tool kit. It's gained admiration from industry leaders due to its deep integration with Python; its integration with top cloud platforms, including Amazon SageMaker and Google Cloud Platform; and its computational graphs that can be defined on the fly. In this course, join Jonathan Fernandes as he dives into the basics of deep learning using PyTorch. Starting with a working image recognition model, he shows how the different components fit and work in tandem, from tensors, loss functions, and autograd all the way to troubleshooting a PyTorch network.
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Price: Unknown
In this course, learn how to develop a face recognition system that can detect faces in images, identify the faces, and even modify faces with "digital makeup" as you've experienced in popular mobile apps. Find out how to set up a development environment. Discover tools you can leverage for face recognition. See how a machine learning model can be trained to analyze images and identify facial landmarks. Learn the steps involved in coding facial feature detection, representing a face as a set of measurements, and encoding faces. Additionally, learn how to repurpose and adjust pre-existing systems.
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Price: Unknown
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network's architecture, and apply deep learning to your own applications.
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Price: Unknown
This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision, and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses, you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings.
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