Top 5 Courses on Coursera for Deep Learning

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To create the patterns of the human brain and train the model to think, act, decide, and work like humans it is necessary to involve the field of deep learning. The deep neural networks help in connecting the dots in the mainframe to link all subprograms. The deep learning models along with other technologies are a trending research topic.

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The combination of machine learning, neural networks, and artificial intelligence have formed the base of deep learning. This technology stack, though not initially empty, opens the world of connectivity, data training, networking, analysis, data recognition, and fully transforming the Machines!

This elm has its roots deep into the domain of machine learning, from where it evolved. It is all about making – Human Computer! So what does the term – “Human-Computer” mean. It refers to the ongoing work in the field of machine learning and artificial intelligence that tries to build the most sophisticated machines. The main thing about these machines is that they would be able to work like Humans!

To build the machines working as humans require great efforts, technical capabilities, the latest technology, and many other things. For this, there is a high study in various fields. The field of deep learning is emerged due to the same. 

What is Deep Learning?

Deep learning is a technique that involves the study of the human brain. It studies the patterns that the human brain makes while taking any decision. It does a careful study of the human brain to draw a replica of how humans think. It involves the study of various situations, different types of decisions, drawing various patterns.

For this, it requires an unsupervised learning technique to generate the dataset. It will be an unstructured dataset because the trends and patterns generated by the study of the human brain are very random and discontinuous. Due to this, it is required to draw a pattern from these and analyze them properly. It requires proper sorting and data pre-processing. 

It includes various things like speech recognition, object recognition, handwriting recognition, text parsing, text to speech conversion (and vice-versa), translating languages, and many other things. The list of deep learning applications is quite large. It involves one or the other part of each domain under machine learning and artificial intelligence. In this way, these things are interlinked with one another.

It takes the big data that is available all over the internet. It takes the data from the cloud as well. After taking the data, which might be available in a structured format, it sorts or arranges the data accordingly. The sorted data is grouped accordingly. Similarly, the data proceeds for further analysis, i.e., to draw patterns and respective conclusions from the data, we require further steps. 

It is widely known as deep neural learning as it uses neural networks that are the latest and fast-growing networks that provide communication and data-transfer linkage over the whole model effectively and efficiently.

Why Deep Learning?

This raises the question – Why not deep learning? If you have data in bulk which requires high processing and high-quality results are required then this is the best technique. It keeps the machine future-oriented, i.e., it has a high scope in the future as well. This is due to the reason that the machine can adjust well in the changing condition as it is easy to modify it at our convenience.

It helps in the faster calculation, accurate predictions, and various other operations related to calculus, algebra, probability, and statistics. It eliminates te\he need for featured engineering. This helps in lossless data transmission with the help of neural networks. 

Like this, there are several other benefits associated with deep learning. Due to all those, and their applications in the industry, we need to study deep learning. Then comes the question that from where to start?

Thousands of platforms teach deep learning and other relevant concepts. These differ according to various countries. Coursera is one of the biggest and most popular online platforms that teach various concepts. The concepts are taught by the domain experts. Not only this, but they also provide continuous assessments to track the progress of each individual. With this all, they provide sufficient time to complete a particular course.

The timing of the course along with the pace can be adjusted by an individual as per his daily timetable. This provides the learner, the flexibility to choose a particular time, pace, and allot the time as per his convenience. In this way, Coursera provides the individual with loads of benefits.

The assessments are of high-quality and there is a sufficient number of attempts provided to those. They come up with proper reasoning and procedure. They provide us with proper mentoring from a group of trained and qualified educates, professors, and experts in that particular domain.

Deep Learning Courses on Coursera

It is always recommended that study form the best as it will give you the proper pathway that you need to follow. Coursera provides the best guidance and teaching. When it comes to deep learning, Coursera is one of the finest platforms with great teachers and other technical things.

Hence, it is the widely recommended platform for deep learning with various benefits. The following are the top courses on deep learning provided on Coursera. These courses are highly on demand. These are the top choices of various learners.

Deep Learning Specialization

This course is best if you want to dive deep into artificial intelligence and deep learning from scratch. It provides in-depth knowledge on convolutional neural networks, RNNs, LSTMs, and much more. In this, you will learn Python and Tensorflow.

You will learn various topics and do a good amount of quality projects. It contains detailed information about each topic under deep learning. In this way, it is a proper toolkit for starting your journey.

You will gain various skills like – Tensorflow, Convolutional Neural Networks, Artificial Neural Networks, Deep Learning, Back Propagation, Python Programming, Hyperparameter, Hyperparameter Optimization, Machine Learning, Inductive Transfer, Multi-Task Learning, Facial Recognition System.

It contains a series of courses that will lead you towards specialization in this area. In the end, you will earn a certificate. For this, you need to complete all the courses in this module and do the modules with proper grades.

You can also share the e-certificate on other platforms to show your achievement.

To enroll in this course – Click Here

Neural Networks and Deep Learning

This course will help you attain a hold over neural networks and deep learning. This will train you completely in the domain of deep learning with proper concepts and high skills. It is divided into five courses. You need to complete all five courses and all the related assessments. These courses will show you the deep-insight in the future world of deep learning.

You will come to know about the applications of deep learning and neural networks in the future. This will enhance your skills, even if you are a beginner, you won’t face any problem in pursuing this course. You will get proper guidance and mentoring from the experts.

You will earn the skills – Artificial Neural Networks, Backpropagation, Python Programming, Deep Learning.

After completing all the five courses, and assessments with proper grades, you will earn an e-certificate that will mark your achievement.

To enroll in this course – Click Here

Tensorflow 2 for Deep Learning

This is a specialization course that aims in teaching a deep learning framework – Tensorflow. It will start from training the model, using regression, callbacks, knowledge about Tensorflow, using Tensorflow APIs, deep learning, making probabilistic models, using Tensorflow libraries to deploying the full model using Tensorflow.

In this module, you will learn how to build various projects on deep learning using Tensorflow. You will make models on image classification, language translation, and much more. This module contains various courses, assessments, and projects. You need to complete all these with proper grades to earn an e-certificate that will be a mark of your achievement.

The skills you will gain are – Tensorflow, Keras, Tensorflow Probability, Probabilistic Neural Networks, Deep Learning, Generative Model, Probabilistic Programming Language (PRPL).

To enroll in this course – Click Here

Generative Deep Learning with Tensorflow

This course is a great advantage if you want to dive deep into the concepts of deep learning and do all sorts of experiments with image processing. If an image is your dataset and you want to analyze it, extract the contents, and perform various operations, then this is the best course for you.

It aims in building AutoEncoders, build various models on complex deep learning concepts, and projects on convolutional neural networks. Overall, it contains all the domains of deep learning.

This module contains four courses, assessments, and projects. You need to complete all these things and attain proper grades to earn an e-certificate. This will mark your progress and achievement.

The skills that you will earn are – Variational AutoEncoders, Auto Encoders, Generative Adversarial Networks, Neural Style Transfer.

To enroll in this course – Click Here 

Tensorflow: Advanced Techniques Specialization

This course deals with all the concepts related to Tensorflow, its function, libraries, and much more. It contains knowledge about various topics on machine learning and deep learning. It provides an environment to use Tensorflow APIs, introduces advanced computer vision scenario, and various other concepts.

There are various projects on convolutions, image segmentation, object detection, style transfer to auto encoding, GANs, and much more.

It contains four courses. The first is about basic functionalities of API, the second is about multi-processor environment and chip types, the third is about object detection, image segmentation, and other projects and the fourth is about exploring generative deep learning, Style transfer through Auto Encoding, VAEs to GANs.

You need to complete all four courses, assessments, and projects with good grades to earn an e-certificate that will mark your achievement.

The skills that you will earn are – Model Interpretability, Custom Training Loops, Custom, and Exotic Models, Generative Machine Learning, Object Detection, Functional API, Custom Layers, Custom and Exotic Models.

To enroll in this course – Click Here

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1 Response

  1. Raveeta Koul says:

    Nice…informative

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