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How AI and Deep Learning Helps In Weather Forecasting

Posted on February 7, 2020May 21, 2020 by Dexlab

The world’s fight against extreme weather conditions and climate change is at the forefront of all discussions and debates on the environment. In fact, climate change is the biggest concern we are faced with today, and studying the climate has increasingly become the primary preoccupation of scientists and researchers. They have received a shot in the arm with the increase in the scope of artificial intelligence and deep learning in predicting weather patterns.

Take for instance the super cyclone Amphan that has ravaged West Bengal and Orissa. Had it not been for weather forecasting techniques, meteorologists would never had predicted the severity of the cyclone and the precautionary evacuation of thousands of people from coastal areas would not have been taken, leading to massive loss of lives. This is where the importance of weather forecasting lies.

Digitizing the prediction model

Traditionally, weather forecasting depends on a combination of observations of the current state of the weather and data sets from previous observations. Meteorologists prepare weather forecasts collecting a wealth of data and running it through prediction models. These sets of data come from hundreds of observations like temperature, wind speed, and precipitation produced by weather stations and satellites across the globe. Due to the digitization of these weather models, accuracy has improved much more than it was a few decades ago. And with the recent introduction of machine learning, forecasting has become an even more accurate and exact science.

Machine Learning

Machine learning can be utilized to make comparisons between historical weather forecasts and observations in real time. Also, machine learning can be used to make models account for inaccuracies in predictions, like overestimated rainfall.

At weather forecast institutions, prediction models use gradient boosting that is a machine learning technique for building predictive models. This is used to correct any errors that come into play with traditional weather forecasting.

Deep Learning

Machine Learning and Deep Learning are increasingly being used for nowcasting, a model of forecasting in the real time, traditionally within a two-hour time span. It provides precipitation forecasts by the minute. With deep learning, a meteorologist can anywhere in the vicinity of a weather satellite (which runs on deep learning technology) use nowcasting rather than just those who live near radar stations (which are used in traditional forecasting).

Extreme Weather Events

Deep learning is being used not only for predicting usual weather patterns, it is being used to predict extreme weather conditions as well. Rice University engineers have designed a deep learning computer system that has trained itself to predict, in accurate terms, extreme weather conditions like heat waves or cold waves. The computer system can do so up to five days in advance. And the most fascinating part is it uses the least information about current weather conditions to make predictions.

This system could effectively guide NWP (numerical weather prediction) that currently does not have the ability to predict extreme weather conditions like heat waves. And it could be a super cheap way to do so as well.

According to sciencedaily.com, with further development, the system could serve as an early warning system for weather forecasters, and as a tool for learning more about the atmospheric conditions that lead to extreme weather, said Rice’s Pedram Hassanzadeh, co-author of a study about the system published online in the American Geophysical Union’s Journal of Advances in Modeling Earth Systems.

Thus, it is no surprise then that machine learning and deep learning is being widely adopted the world over. In India, is it being taken up as a form of study and training in metropolitans like Delhi and Gurgaon. For the best Machine Learning course in Delhi and deep learning course in delhi, check out the DexLab Analytics website today.

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Python Statistics Fundamentals: How to Describe Your Data? (Part II)

Posted on January 14, 2020January 25, 2020 by Dexlab

Python Statistics Fundamentals: How to Describe Your Data? (Part II)

In the first part of this article, we have seen how to describe and summarize datasets and how to calculate types of measures in descriptive statistics in Python. It’s possible to get descriptive statistics with pure Python code, but that’s rarely necessary.

Python is an advanced programming language extensively used in all of the latest technologies of Data Science, Deep Learning and Machine learning. Furthermore, it is particularly responsible for the growth of the Machine Learning course in India. Moreover, numerous courses like Deep Learning for Computer vision with Python, Text Mining with Python course and Retail Analytics using Python are pacing up with the call of the age. You must also be in line with the cutting-edge technologies by enrolling with the best Python training institute in Delhi now, not to regret it later.

In this part, we will see the Python statistics libraries which are comprehensive, popular, and widely used especially for this purpose. These libraries give users the necessary functionality when crunching data. Below are the major Python libraries that are used for working with data.

NumPy and SciPy – Fundamental Scientific Computing

NumPy stands for Numerical Python. The most powerful feature of NumPy is the n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities. NumPy is much faster than the native Python code due to the vectorized implementation of its methods and the fact that many of its core routines are written in C (based on the CPython framework).

For example, let’s create a NumPy array and compute basic descriptive statistics like mean, median, standard deviation, quantiles, etc.

SciPy stands for Scientific Python, which is built on NumPy. NumPy arrays are used as the basic data structure by SciPy.

Scipy is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transforms, Linear Algebra, Optimization and Sparse matrices. Specifically in statistical modelling, SciPy boasts of a large collection of fast, powerful, and flexible methods and classes. It can run popular statistical tests such as t-test, chi-square, Kolmogorov-Smirnov, Mann-Whitney rank test, Wilcoxon rank-sum, etc. It can also perform correlation computations, such as Pearson’s coefficient, ANOVA, Theil-Sen estimation, etc.

Pandas – Data Manipulation and Analysis

Pandas library is used for structured data operations and manipulations. It is extensively used for data preparation. The DataFrame() function in Pandas takes a list of values and outputs them in a table. Seeing data enumerated in a table gives a visual description of a data set and allows for the formulation of research questions on the data.

The describe() function outputs various descriptive statistics values, except for the variance. The variance is calculated using the var() function in Pandas.

The mean() function, returns the mean of the values for the requested axis.

Matplotlib – Plotting and Visualization

Matplotlib is a Python library for creating 2D plots. It is used for plotting a wide variety of graphs, starting from histograms to line plots to heat plots. One can use Pylab feature in IPython notebook (IPython notebook –pylab = inline) to use these plotting features inline. If the inline option is ignored, then pylab converts IPython environment to an environment, very similar to Matlab.

matplotlib.pylot is a collection of command style functions.

If a single list array is provided to the plot() command, matplotlib assumes it is a sequence of Y values and internally generates the X value for you.

Each function makes some change to a figure, like creating a figure, creating a plotting area in a figure, decorating the plot with labels, etc. Now, let us create a very simple plot for some given data, as shown below:

Scikit-learn – Machine Learning and Data Mining

Scikit-learn built on NumPy, SciPy and matplotlib. Scikit-learn is the most widely used Python library for classical machine learning. But, it is necessary to include it in the discussion of statistical modeling as many classical machine learning (i.e. non-deep learning) algorithms can be classified as statistical learning techniques. This library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensional reduction.

Conclusion

In this article, we covered a set of Python open-source libraries that form the foundation of statistical modelling, analysis, and visualization. On the data side, these libraries work seamlessly with the other data analytics and data engineering platforms, such as Pandas and Spark (through PySpark). For advanced machine learning tasks (e.g. deep learning), NumPy knowledge is directly transferable and applicable in popular packages such as TensorFlow and PyTorch. On the visual side, libraries like Matplotlib, integrate nicely with advanced dashboarding libraries like Bokeh and Plotly.

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html

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Deep Learning and its Progress as Discussed at Intel’s AI Summit

Posted on November 20, 2019May 23, 2020 by Dexlab

Deep Learning and its Progress as Discussed at Intel’s AI Summit

At the latest AI summit organized by Intel, Mr. Naveen Rao, Vice President and General Manager of Intel’s AI Products Group, focused on the most vibrant age of computing that is the present age we are living. According to Rao, the widespread and sudden growth of neural networks is putting the capability of the hardware into a real test. Therefore, we now have to reflect deeply on “how processing, network, and memory work together” to figure a pragmatic solution, he said.

The storage of data has seen countless improvements in the last 20 years. We can now boast of our prowess of handling considerably large sets of data, with greater computing capability in a single place. This led to the expansion of the neural network models with an eye on the overall progress in neural Network Machine Learning Python and computing in general.

With the onset of exceedingly large data sets to work with, Deep learning for Computer Vision Course and the other models of Deep Learning to recognize speech, images, and text are extensively feeding on them. The technological giants were undoubtedly the early birds to grab the technical: the hardware and the software configuration to have an edge on the others.

Surely, Deep Learning is on its peak now, where computers can identify the images with incredible vividness. On the other hand, chatbots can carry on with almost natural conversations with us. It is no wonder that the Deep learning Training Institutes all over the world are jumping in the race to bring all of these new technologies efficiently to the general mass.

The Big Problem

We are living in the dynamic age of AI and Machine Learning, with the biggies like Google, Facebook, and its peers, having the technical skills and configuration to take up the challenges. However, the neural networks have fattened up so much lately that it has already started to give the hardware a tough time, getting the better of them all the time.

The number of parameters of the Neural network models is increasing as never before. They are “actually increasing on the order of 10x year on year”, as per Rao. Thus, it is a wall looming in AI. Though Intel is trying its best to tackle this obvious wall, which might otherwise give the industry a severe setback, with extensive research to bring new chip architectures and memory technologies into play, it cannot solve the AI processing problem single-handedly. Rao concluded on a note of requesting the partners in the present competitive scenario.

Sourced from: www.datanami.com/2019/11/13/deep-learning-has-hit-a-wall-intels-rao-says

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The Future of AI and Machine Learning: What the Experts Say?

Posted on November 4, 2019May 23, 2020 by Dexlab

The Future of AI and Machine Learning: What the Experts Say?

It’s hard to ignore the growing prowess of AI and machine learning.

Previously, Gartner predicted that AI will become one of the key priorities for more than 30% C-Suite professionals by 2020. Indeed, it’s true; software vendors across the globe are following this new gold rush. For them, data is like new oil. In this blog, we explore the future of this budding technology and gain some new insights and ideas. Let’s see what the heavyweights from the digital industry have to say:

Hyper-targeting and Personalization

Ben Wald, Co-Founder & VP of Solutions Implementation at Very

Though machine learning is a subset of data analysis, it’s rapidly influencing the IoT industry and its respective devices. In the last couple of years, nearly 90% of data was generated through an array of smartphones, watches and cars. These mountains of data help in forming better customer relationships.

How? Using Machine Learning Using Python of course! With this power tool, the corporate houses are trying to understand their target audience and extract crucial information regarding how well they receive their products and related after-sales services. Fine-tuning personalization on a wider scale is the key. Hopefully, soon, we will be able to achieve this goal. We are still in the nascent stage.

Improved Search Engine Experiences

Dorit Zilbershot, Chief Product Officer at Attivio

Did you know that AI algorithms have a massive impact on search engine results?

In the next few years, search engines are expected to enhance user and admin experience: courtesy breakthroughs in neural networks and deep learning technologies. These revolutionary technologies, especially deep learning for computer vision with Python will make sure users enjoy a fabulous searching experience and will deliver highly relevant answers. Currently, we are working on delivering results that are based on user’s query and profile. The process requires a lot of manual configurations and a fundamental understanding of how search engines work. Later, the results will be customized based on individuals’ past preferences, interactions and words used. It will be fun to see how machine learning algorithms transform the dynamo of content publishing and search engines.

Quantum Computing

Matt Reaney, Founder & CEO of Big Cloud

Real and revolutionary, the concept of quantum computing is wreaking havoc in the domain of science and technology. It is the future of machine learning triggering an array of innovations. Integrating quantum computing with machine learning is expected to transform the field triggering accelerated learning, quicker processing and better capabilities. This means the intricate challenges that we can’t solve now could be done in a fraction of time then.

The potential of quantum computing is huge in the future and is likely affect millions of lives, notably in medicine and healthcare industry.

Currently, there are no commercially-built quantum algorithms or hardware available in the market. However, several research facilities and government agencies have been investing in this new field of science of late.

End Notes

At DexLab Analytics, we love to craft and curate insights from industry pundits, especially when it comes to something as significant as technological innovations that transform lives altogether. Follow us and stay updated!

How progressive is an Artificial Neural Network? Tracking ANNs

Posted on October 16, 2019May 23, 2020 by Dexlab

How progressive is an Artificial Neural Network? Tracking ANNs

The major improvements that Artificial Neural Network is bringing about in favour of deep learning for computer vision with Python are ground-breaking. Machine vision, in general, is hugely benefitted with the inclusion of the computer vision course Python, spurred by the all-new technology of Neural Networks. This is by and large a huge advancement in the field of computer science and gives much of an insight into what the future holds for us.

However, along with an array of experiments that are performed day in day out with Neural Network Machine Learning Python, numerous other fields are also likely to be revamped in much the same way. Predicting the weather, studying animals and other critical studies of cosmology are also believed to be easing soon holding the hands of the Artificial Neural Network technology.

Some Well-known Feats of the Artificial Neural Network

Artificial Neural networks (ANNs) are used in studying the patterns, relationships from the collected data just like humans. Going by the name, ANNs are modelled on the neural networks found in our brains, which are used to infuse the machines with the ability to learn by them. Besides, ANNs have been hugely successful in bringing about the concept of self-driving cars, boosting medical technology and numerous other fields. But, here we lay down some other fields which are soaking in the Artificial Neural Network extensively.

Meteorology

The accurate prediction of hailstorms and providing relevant alerts to the specific areas are expected to boost shortly. With the inclusion of Convolutional neural networks, (CNNs) the study of meteorology is deemed to achieve new heights. Besides, this improved technology would also be capable of identifying the size of the hails during this storm.

Tracking Bird Migration

We are all aware of the phenomenon of migration for the birds. But with the changing age, the routes of the birds are also different from what they used to be. However, if you need to track the migration of the birds, you can opt for the exclusive Neural Networks in Python course.

Interpreting the Dark Matter

Dark matter has been a topic which remains largely unexplored till date. Nothing beyond the name and the fact that it binds the universe together is brought to light. However, with the marked progress of the premium institutes like the Neural Networks Training in Delhi, the dark matter will no longer be a mystery.

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Application of Mode using R and Python

Posted on August 26, 2019August 26, 2019 by Dexlab

Application of Mode using R and Python

Mode, for a given set of observations, is that value of the variable, where the variable occurs with the maximum or the highest frequency.

This blog is in continuation with STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY. However, here we will elucidate the Mode and its application using Python and R.

Mode is the most typical or prevalent value, and at times, represents the true characteristics of the distribution as a measure of central tendency.

Application:

The numbers of the telephone calls received in 245 successive one minute intervals at an exchange are shown in the following frequency distribution table:

No of Calls	Frequency
0	14
1	21
2	25
3	43
4	51
5	40
6	51
7	51
8	39
9	12
Total	245

[Note: Here we assume total=245 when we calculate Mean from the same data]

Evaluate the Mode from the data.

Calculate Mode in R:

Calculate mode in R from the data, i.e. the most frequent number in the data is 51.

The number 51 repeats itself in 5, 7 and 8 phone calls respectively.

Calculate Median in Python:

First, make a data frame for the data.

Now, calculate the mode from the data frame.

Calculate mode in Python from the data, i.e. the most frequent number in the data is 51.

The number 51 repeats itself in 5, 7 and 8 phone calls respectively.

Mode is used in business, because it is most likely to occur. Meteorological forecasts are, in fact, based on mode calculations.

The modal wage of a group of the workers is the wages which the largest numbers of workers receive, and as such, this wage may be considered as the representative wage of the group.

In this particular data set we use the mode function to know the occurrence of the highest number of phone calls.

It will thus, help the Telephone Exchange to analyze their data flawlessly.

Note – As you have already gone through this post, now, if you are interested to know about the Harmonic Mean, you can check our post on the APPLICATION OF HARMONIC MEAN USING R AND PYTHON.

Dexlab Analytics is a formidable institute for Deep learning for computer vision with Python. Here, you would also find more information about courses in Python, Deep Learning, Machine Learning, and Neural Networks which will come with proper certification at the end.

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A Deeper Understanding of Deep Learning

Posted on August 17, 2019August 17, 2019 by Dexlab

A Deeper Understanding of Deep Learning

To define Deep Learning, it can be summed up as a machine learning technique used to teach computers all those things which comes to humans quite instinctively. This is a sub-classification of the umbrella term Machine Learning and is based on artificial neural networks.

The technology of driver-less cars, of computers with the knowledge of lampposts and trees as non-living entities and with their discretion of differentiating between a pedestrian and a lamppost, all are being developed from Deep Learning. Besides, the voice assistant you find nowadays, that comes with the smartphones, tablets, TVs and hands-free electronic gadgets, everything is matured by Deep Learning.

Deep Learning is an immensely effective technique with huge prospective. Thus, Deep Learning is a highly regarded technology and more and more people are looking forward to finding their career in it.

Deep Learning: The Path of Success

Among the ever-changing technologies, Deep Learning has its path paved to stand strong in the long run. Now, this is possible primarily because of the high accuracy levels that it has reached.

Pin-pointed Accuracy

With the convincing accuracy levels reached, Deep Learning is believed to be steadfast in situations which involves high risks and which calls for the least margin of errors. For example – driver-less cars.

Extensive Library

If you aim Deep Learning for computer vision with Python, you should be ready with enormous information that it can go through and process quite effortlessly, hence, putting forth an all-inclusive library to be used in real-time. For instance, millions of images, days of video and data should be fed to the system going forward to develop the technology of the driverless car.

Powerful Computing

If we talk about the power that Deep Learning needs, it is astonishingly unreal, the amount of power that this technology expects to perform in its optimum. None other than immensely powerful GPUs are used to get the best results.

As Deep Learning is quite a new thing, unknown in most of its dimensions, here are a few of the fields which have already absorbed or are trying to infuse Deep Learning in constructively.

Automobiles – As we have already mentioned that the automobile industry has already taken Deep Learning quite seriously and is effective moving in the direction, where, soon we would witness cars without any human drivers.
Defence and Aerospace – Deep learning is constantly taken into account when determining the objects that the satellites bring us. Via Deep Learning we can be sure of the areas/objects in the space. Furthermore, whether a particular zone is fit for the soldiers or not, can also be easily determined by Deep Learning.
Pharmacy – Deep Learning is highly significant even in modern medical science. For example, this technology is used to detect cancerous cells.

With these being said, Deep Learning is simply superb in how it has performed still and the promise that it is showing to be on par with the age. Therefore, if you are seeking for the Deep learning for computer vision course, you can simply avail of Deep Learning for computer vision Training Center in Delhi NCR.

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Deep Learning to Boost Ghost Hunting and Paleontology Efforts

Posted on March 11, 2019May 23, 2020 by Dexlab

Deep Learning to Boost Ghost Hunting and Paleontology Efforts

Deep leaning technology is taking the world by storm. It is leaving no territory untouched, not even the world of dead! Yes, this robust technology has now started hunting ghosts – for real. Of late, Nature Communication even published a paper highlighting that a ghost population has even contributed to today’s genomes.

With the help of a demographic model structured on deep learning in an Approximate Bayesian Computation framework, it is now possible to delve into the evolutionary history of the Eurasian population in sync with the present-day genetic evidence. Since it is believed that all modern humans have originated Out of Africa, the evolutionary history of the Eurasian population has been identified by introgressions from currently extinct hominins. What’s more, talking about the unknown population, the researchers believe they either trace their roots to Neanderthal-Denisova clade or simply forked early from the Denisova lineage.

If you want to take a look at the original paper, click here — www.nature.com/articles/s41467-018-08089-7

In addition, the study reflects how the fabulous technology of AI can be leveraged in paleontology. Whether it’s about discovering unpredictable ghosts or unraveling the fading footprints of the whole evolutionary journey, deep learning and AI are taking the bull (paleontology, in this respect) by its horns. According to the paper, researchers studied deep about the evolutionary process of Eurasian population, including past introgression events in OOA (Out of Africa) populations suiting the contemporary genetic evidence and they have produced several simulated evolutionary data, like the total size of ancestral human populations, the exact number of populations, the appropriate time when they branched out from one another, the rate at which they intermixed and so on. Besides, a wide number of simulated genomes for current-day humans have been launched.

The latest and very efficient deep learning method highlights the crucial importance of genomes – they can easily let you know which evolutionary models are most likely to reveal respective genetic patterns. Moreover, if you study closely, you will find that the culture of the entire industry has changed over the past few years. Advanced computers and technology modifications have achieved ‘things’ that were simply impossible with pen and paper a few years back. Perhaps, what’s more interesting is that our perspective of seeing data has changed completely. The potent advances in AI and machine learning have demystified the ways in which algorithms work leading to more concrete shreds of evidence and end-results, which were previously not possible with the age-old traditional methods.

The blog first appeared on— www.analyticsindiamag.com/deep-learning-uncovers-ghosts-in-modern-dna

Are you interested in artificial intelligence certification in Delhi NCR? DexLab Analytics is your go-to institute, which is specialized in imparting in-demand skill training courses. Be it artificial intelligence course, data science certification or Python Spark training, DexLab Analytics excels in all – for more information, contact us today.

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A Beginner’s Guide to Deep Learning: Exploring the Basics

Posted on February 11, 2019March 6, 2020 by Dexlab

A Beginner’s Guide to Deep Learning: Exploring the Basics

Over the last couple of years, no other part of data science has made progress like Deep Learning has. From self-driving cars to scientific research, deep learning has been the game-changer in almost every innovation made. Day by day, its influence on our way of life is getting stronger!

Deep learning is a vast and complex field having numerous sections. It takes several months of consistent effort to master the basics before delving deeper into the subject. And a thorough understanding of fundamental concepts of calculus and algebra is essential for getting started with deep learning education.

This article discusses the basics of deep learning for newbies.

Machine Leaning Basics

One must be thorough with the basics of machine learning, which includes reinforced, supervised and unsupervised learning, before starting off your deep learning education. Statistical techniques, like linear regression and logistic regression, are greatly needed in this field.

Deep Learning Introduction

First of all, you need to know the various deep learning frameworks. Deep Learning algorithms draw inspiration from artificial neural networks. While there are many free online courses, a professional course from a reputed deep learning training institute is the ideal starting point for beginners. Additionally, you can follow relevant eBooks, like the Neural Networks and Deep Learning PDF by Michael Nielson.

Understanding Neural Networks

Neural networks have a layered outlay and their functioning resembles the neurons of human brain. Neural networks are made up of an input layer, an output layer and a hidden layer, and produce output after receiving an input – just like human mind works. You need to be familiar with techniques of handling and pre-processing data, regularization methods, data augmentation, hyperparameter technique, etc. These functions of artificial neural network are widely employed in deep learning, helping tasks like image and speech recognition.

Convolution Neural Network Basics

An important role in deep learning is played by Convolution Neural Network, which is profusely used in object detections, facial recognition, image recognition and classifications, etc. In deep learning, CNN models work by passing the input image through a string of convoluted layers before classifying it with probabilistic values.

Knowing Sequence Models

If you want to go deeper into deep learning, it is crucial to know how to develop models such as Recurrent Neural Networks (RNNs), and make use of alternatives, like Long Short Term Memory (LSTMs) and Gated Recurrent Unit (GRU). Working with audio applications and music synthesis becomes easier when you understand these models.

Unsupervised Deep Learning

A complex topic, but learning it helps crack otherwise unsolvable problems. Problems that remain unclear even after applying supervised learning methods like biasing can be explained with unsupervised deep learning. One popular algorithm of unsupervised deep learning is Autoencoder neural network.

Know Natural Language Processing

NPL deals with understanding human speech and has many benefits. With the help of computational algorithms, NPL analyzes and represents human language. It can also be employed in dialogue generation, machine translation, etc.

Deep Reinforcement Learning

Deep reinforcement learning has immense potential in deep learning. Reinforcement learning algorithms united with deep learning created AlphaGo, which was successful in defeating the strongest Go players!

Theory isn’t enough; you must implement your deep learning knowledge. And to do that properly, you must be able to use Python.

There’s no need to panic if Python looks like Hebrew at the moment! DexLab Analytics is here to offer expert guidance by skilled industry experts. We offer comprehensive and industry-driven deep learning certification in Gurgaon. You can also check our popular Python certification courses.

Reference: https://www.analyticsindiamag.com/the-best-resources-for-learning-deep-learning-for-beginners/

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