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Deep Learning A-Z™: Hands-On Artificial Neural Networks

Udemy Deep Learning A-Z™: Hands-On Artificial Neural Networks 2020-03-04

Udemy - Deep Learning A-Z™: Hands-On Artificial Neural Networks

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Learn to create Deep Studying Algorithms in Python from two Machine Studying & Knowledge Science specialists. Templates included.

What you will learn

  • Perceive the instinct behind Synthetic Neural Networks
  • Apply Synthetic Neural Networks in follow
  • Perceive the instinct behind Convolutional Neural Networks
  • Apply Convolutional Neural Networks in follow
  • Perceive the instinct behind Recurrent Neural Networks
  • Apply Recurrent Neural Networks in follow
  • Perceive the instinct behind Self-Organizing Maps
  • Apply Self-Organizing Maps in follow
  • Perceive the instinct behind Boltzmann Machines
  • Apply Boltzmann Machines in follow
  • Perceive the instinct behind AutoEncoders
  • Apply AutoEncoders in follow

Requirements

  • Highschool arithmetic degree
  • Fundamental Python programming information

Description

Synthetic intelligence is rising exponentially. There isn't a doubt about that. Self-driving automobiles are clocking up tens of millions of miles, IBM Watson is diagnosing sufferers higher than armies of docs and Google Deepmind's AlphaGo beat the World champion at Go - a sport the place instinct performs a key position.

However the additional AI advances, the extra complicated grow to be the issues it wants to unravel. And solely Deep Studying can remedy such complicated issues and that is why it is on the coronary heart of Synthetic intelligence.

--- Why Deep Studying A-Z? ---

Listed below are 5 causes we predict Deep Studying A-Z™ actually is totally different, and stands out from the gang of different coaching applications on the market:

1. ROBUST STRUCTURE

The primary and most vital factor we centered on is giving the course a strong construction. Deep Studying could be very broad and sophisticated and to navigate this maze you want a transparent and international imaginative and prescient of it.

That is why we grouped the tutorials into two volumes, representing the 2 basic branches of Deep Studying: Supervised Deep Studying and Unsupervised Deep Studying. With every quantity specializing in three distinct algorithms, we discovered that that is the perfect construction for mastering Deep Studying.


2. INTUITION TUTORIALS

So many programs and books simply bombard you with the idea, and math, and coding... However they overlook to clarify, maybe, crucial half: why you're doing what you're doing. And that is how this course is so totally different. We deal with creating an intuitive *really feel* for the ideas behind Deep Studying algorithms.

With our instinct tutorials you may be assured that you just perceive all of the strategies on an instinctive degree. And when you proceed to the hands-on coding workout routines you will notice for your self how way more significant your expertise can be. This can be a game-changer.

3. EXCITING PROJECTS

Are you uninterested in programs primarily based on over-used, outdated knowledge units

Sure? Nicely you then're in for a deal with.

Inside this class we are going to work on Actual-World datasets, to unravel Actual-World enterprise issues. (Positively not the boring iris or digit classification datasets that we see in each course). On this course we are going to remedy six real-world challenges:

  • Synthetic Neural Networks to unravel a Buyer Churn downside
  • Convolutional Neural Networks for Picture Recognition
  • Recurrent Neural Networks to foretell Inventory Costs
  • Self-Organizing Maps to research Fraud
  • Boltzmann Machines to create a Recomender System
  • Stacked Autoencoders* to tackle the problem for the Netflix $1 Million prize
*Stacked Autoencoders is a model new approach in Deep Studying which did not even exist a few years in the past. We have not seen this methodology defined wherever else in ample depth.

4. HANDS-ON CODING

In Deep Studying A-Z™ we code along with you. Each sensible tutorial begins with a clean web page and we write up the code from scratch. This manner you may observe alongside and perceive precisely how the code comes collectively and what every line means.

As well as, we are going to purposefully construction the code in such a method so as to obtain it and apply it in your individual tasks. Furthermore, we clarify step-by-step the place and the way to modify the code to insert YOUR dataset, to tailor the algorithm to your wants, to get the output that you're after.

This can be a course which naturally extends into your profession.


5. IN-COURSE SUPPORT

Have you ever ever taken a course or learn a e book the place you might have questions however can not attain the creator?

Nicely, this course is totally different. We're totally dedicated to creating this probably the most disruptive and {powerful} Deep Studying course on the planet. With that comes a accountability to continuously be there whenever you want our assist.

In truth, since we bodily additionally must eat and sleep now we have put collectively a crew {of professional} Knowledge Scientists to assist us out. Everytime you ask a query you're going to get a response from us inside 48 hours most.

Irrespective of how complicated your question, we can be there. The underside line is we would like you to succeed.


--- The Instruments ---

Tensorflow and Pytorch are the 2 hottest open-source libraries for Deep Studying. On this course you'll study each!

TensorFlow was developed by Google and is used of their speech recognition system, within the new google images product, gmail, google search and way more. Firms utilizing Tensorflow embrace AirBnb, Airbus, Ebay, Intel, Uber and dozens extra.

PyTorch is as simply as {powerful} and is being developed by researchers at Nvidia and main universities: Stanford, Oxford, ParisTech. Firms utilizing PyTorch embrace Twitter, Saleforce and Fb.

So which is healthier and for what?

Nicely, on this course you should have a possibility to work with each and perceive when Tensorflow is healthier and when PyTorch is the best way to go. All through the tutorials we evaluate the 2 and offer you suggestions and concepts on which may work finest in sure circumstances.

The attention-grabbing factor is that each these libraries are barely over 1 yr previous. That is what we imply once we say that on this course we educate you probably the most leading edge Deep Studying fashions and strategies.


--- Extra Tools ---

Theano is one other open supply deep studying library. It is similar to Tensorflow in its performance, however however we are going to nonetheless cowl it.

Keras is an unimaginable library to implement Deep Studying fashions. It acts as a wrapper for Theano and Tensorflow. Due to Keras we will create {powerful} and sophisticated Deep Studying fashions with just a few traces of code. That is what is going to can help you have a worldwide imaginative and prescient of what you're creating. All the things you make will look so clear and structured due to this library, that you'll actually get the instinct and understanding of what you're doing.


--- Even Extra Tools ---

Scikit-learn probably the most sensible Machine Studying library. We'll primarily use it:

  • to guage the efficiency of our fashions with probably the most related approach, k-Fold Cross Validation
  • to enhance our fashions with efficient Parameter Tuning
  • to preprocess our knowledge, in order that our fashions can study in the perfect circumstances
And naturally, now we have to say the same old suspects. This complete course relies on Python and in each single part you may be getting hours and hours of invaluable hands-on sensible coding expertise.

Plus, all through the course we can be utilizing Numpy to do excessive computations and manipulate excessive dimensional arrays, Matplotlib to plot insightful charts and Pandas to import and manipulate datasets probably the most effectively.


--- Who Is This Course For? ---

As you may see, there are many totally different instruments within the area of Deep Studying and on this course we make certain to point out you crucial and most progressive ones in order that whenever you're performed with Deep Studying A-Z™ your abilities are on the reducing fringe of in the present day's know-how.

If you're simply beginning out into Deep Studying, then you'll find this course extraordinarily helpful. Deep Studying A-Z™ is structured round particular coding blueprint approaches which means that you just will not get slowed down in pointless programming or mathematical complexities and as a substitute you may be making use of Deep Studying strategies from very early on within the course. You'll construct your information from the bottom up and you will notice how with each tutorial you're getting increasingly assured.

If you have already got expertise with Deep Studying, you'll find this course refreshing, inspiring and really sensible. Inside Deep Studying A-Z™ you'll grasp a number of the most cutting-edge Deep Studying algorithms and strategies (a few of which did not even exist a yr in the past) and thru this course you'll achieve an immense quantity of precious hands-on expertise with real-world enterprise challenges. Plus, inside you'll find inspiration to discover new Deep Studying abilities and purposes.

--- Actual-World Case Research ---

Mastering Deep Studying is not only about figuring out the instinct and instruments, it is also about with the ability to apply these fashions to real-world situations and derive precise measurable outcomes for the enterprise or mission. That is why on this course we're introducing six thrilling challenges:


#1 Churn Modelling Downside

On this half you may be fixing a knowledge analytics problem for a financial institution. You'll be given a dataset with a big pattern of the financial institution's clients. To make this dataset, the financial institution gathered info resembling buyer id, credit score rating, gender, age, tenure, stability, if the shopper is lively, has a bank card, and so on. Throughout a interval of 6 months, the financial institution noticed if these clients left or stayed within the financial institution.

Your objective is to make an Synthetic Neural Community that may predict, primarily based on geo-demographical and transactional info given above, if any particular person buyer will depart the financial institution or keep (buyer churn). In addition to, you're requested to rank all the shoppers of the financial institution, primarily based on their likelihood of leaving. To try this, you will want to make use of the proper Deep Studying mannequin, one that's primarily based on a probabilistic strategy.

In case you succeed on this mission, you'll create important added worth to the financial institution. By making use of your Deep Studying mannequin the financial institution might considerably scale back buyer churn.

#2 Picture Recognition

On this half, you'll create a Convolutional Neural Community that is ready to detect numerous objects in pictures. We'll implement this Deep Studying mannequin to acknowledge a cat or a canine in a set of images. Nevertheless, this mannequin will be reused to detect the rest and we are going to present you the way to do it - by merely altering the photographs within the enter folder.

For instance, it is possible for you to to coach the identical mannequin on a set of mind pictures, to detect in the event that they comprise a tumor or not. However if you wish to hold it fitted to cats and canines, then you'll actually have the ability to a take an image of your cat or your canine, and your mannequin will predict which pet you might have. We even examined it out on Hadelin’s canine!


#3 Inventory Value Prediction

On this half, you'll create one of the {powerful} Deep Studying fashions. We'll even go so far as saying that you'll create the Deep Studying mannequin closest to “Synthetic Intelligence”. Why is that? As a result of this mannequin can have long-term reminiscence, similar to us, people.

The department of Deep Studying which facilitates that is Recurrent Neural Networks. Basic RNNs have quick reminiscence, and had been neither standard nor {powerful} for this precise motive. However a latest main enchancment in Recurrent Neural Networks gave rise to the recognition of LSTMs (Lengthy Quick Time period Reminiscence RNNs) which has fully modified the taking part in subject. We're extraordinarily excited to incorporate these cutting-edge deep studying strategies in our course!

On this half you'll learn to implement this ultra-powerful mannequin, and we are going to take the problem to make use of it to foretell the true Google inventory value. An identical problem has already been confronted by researchers at Stanford College and we are going to intention to do no less than nearly as good as them.

#4 Fraud Detection

In accordance with a latest report printed by Markets & Markets the Fraud Detection and Prevention Market goes to be value $33.19 Billion USD by 2021. This can be a big business and the demand for superior Deep Studying abilities is just going to develop. That’s why now we have included this case research within the course.

That is the primary a part of Quantity 2 - Unsupervised Deep Studying Fashions. The enterprise problem right here is about detecting fraud in bank card purposes. You'll be making a Deep Studying mannequin for a financial institution and you're given a dataset that incorporates info on clients making use of for a complicated bank card.

That is the information that clients offered when filling the applying kind. Your activity is to detect potential fraud inside these purposes. That signifies that by the top of the problem, you'll actually provide you with an express listing of consumers who doubtlessly cheated on their purposes.


#5 & 6 Recommender Programs

From Amazon product solutions to Netflix film suggestions - good recommender programs are very precious in in the present day's World. And specialists who can create them are a number of the top-paid Knowledge Scientists on the planet.

We'll work on a dataset that has precisely the identical options because the Netflix dataset: loads of motion pictures, 1000's of customers, who've rated the flicks they watched. The scores go from 1 to five, precisely like within the Netflix dataset, which makes the Recommender System extra complicated to construct than if the scores had been merely “Appreciated” or “Not Appreciated”.

Your ultimate Recommender System will have the ability to predict the scores of the flicks the shoppers didn’t watch. Accordingly, by rating the predictions from 5 right down to 1, your Deep Studying mannequin will have the ability to advocate which motion pictures every person ought to watch. Creating such a robust Recommender System is kind of a problem so we are going to give ourselves two pictures. Which means we are going to construct it with two totally different Deep Studying fashions.

Our first mannequin can be Deep Perception Networks, complicated Boltzmann Machines that can be lined in Half 5. Then our second mannequin can be with the {powerful} AutoEncoders, my private favorites. You'll respect the distinction between their simplicity, and what they're able to.

And you'll even have the ability to apply it to your self or your pals. The listing of films can be express so you'll merely must charge the flicks you already watched, enter your scores within the dataset, execute your mannequin and voila! The Recommender System will inform you precisely which motion pictures you'll love one evening you if are out of concepts of what to look at on Netflix!

In conclusion, that is an thrilling coaching program stuffed with instinct tutorials, sensible workout routines and real-World case research.

Who this course is for:

  • Anybody thinking about Deep Studying
  • College students who've no less than highschool information in math and who need to begin studying Deep Studying
  • Any intermediate degree individuals who know the fundamentals of Machine Studying or Deep Studying, together with the classical algorithms like linear regression or logistic regression and extra superior subjects like Synthetic Neural Networks, however who need to study extra about it and discover all of the totally different fields of Deep Studying
  • Anybody who isn't that comfy with coding however who's thinking about Deep Studying and needs to use it simply on datasets
  • Any college students in school who need to begin a profession in Knowledge Science
  • Any knowledge analysts who need to degree up in Deep Studying
  • Any people who find themselves not glad with their job and who need to grow to be a Knowledge Scientist
  • Any individuals who need to create added worth to their enterprise by utilizing {powerful} Deep Studying instruments
  • Any enterprise house owners who need to perceive the way to leverage the Exponential know-how of Deep Studying of their enterprise
  • Any Entrepreneur who desires to create disruption in an business utilizing probably the most leading edge Deep Studying algorithms

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