Modern Deep Learning in Python

Modern Deep Learning in PythonThis course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you.Get immediately download Modern Deep Learning in PythonYou already learned about backpropagation, but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGrad, RMSprop, and Adam which can also help speed up your training.Because you already know about the fundamentals of neural networks, we are going to talk about more modern techniques, like dropout regularization and batch normalization, which we will implement in both TensorFlow and Theano. The course is constantly being updated and more advanced regularization techniques are coming in the near future.In my last course, I just wanted to give you a little sneak peak at TensorFlow. In this course we are going to start from the basics so you understand exactly what’s going on – what are TensorFlow variables and expressions and how can you use these building blocks to create a neural network? We are also going to look at a library that’s been around much longer and is very popular for deep learning – Theano. With this library we will also examine the basic building blocks – variables, expressions, and functions – so that you can build neural networks in Theano with confidence.Theano was the predecessor to all modern deep learning libraries today. Today, we have almost TOO MANY options. Keras, PyTorch, CNTK (Microsoft), MXNet (Amazon / Apache), etc. In this course, we cover all of these! Pick and choose the one you love best.Because one of the main advantages of TensorFlow and Theano is the ability to use the GPU to speed up training, I will show you how to set up a GPU-instance on AWS and compare the speed of CPU vs GPU for training a deep neural network.With all this extra speed, we are going to look at a real dataset – the famous MNIST dataset (images of handwritten digits) and compare against various benchmarks. This is THE dataset researchers look at first when they want to ask the question, “does this thing work?”These images are important part of deep learning history and are still used for testing today. Every deep learning expert should know them well.This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.“If you can’t implement it, you don’t understand it”Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?Get immediately download Modern Deep Learning in PythonAfter doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…Suggested Prerequisites:Know about gradient descentProbability and statisticsPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operations, loading a CSV fileKnow how to write a neural network with NumpyWHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)Who this course is for:Students and professionals who want to deepen their machine learning knowledgeData scientists who want to learn more about deep learningData scientists who already know about backpropagation and gradient descent and want to improve it with stochastic batch training, momentum, and adaptive learning rate procedures like RMSpropThose who do not yet know about backpropagation or softmax should take my earlier course, deep learning in Python, first Here’s What You’ll Get in Modern Deep Learning in Python– Download Sample files “Modern Deep Learning in Python”Course Requirement: Modern Deep Learning in PythonReal Value: $139.9900One time cost: USD42.0000Frequently Asked Questions For “Modern Deep Learning in Python”How to make payment for “Modern Deep Learning in Python” ?Please add to cart on this page and go to checkout page. You can also add as many other products as you like and make a one-time payment.We accept several type of Stripe payments such as Visa, Mastercard, American Express, Discover, Diners Club, Google Pay, Apple Pay and JCB, payments from customers worldwide. Paypal & Bitcoin please contact us.We strongly recommend our customers to make a payment through Stripe & Paypal . 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Your patience is appreciated.How long do I have access to the course? How does lifetime access download?After enrolling, you have unlimited download to this |Modern Deep Learning in Python|  for as long as you like – across any and all devices you own.How to download “Modern Deep Learning in Python” ?Enjoy “Modern Deep Learning in Python” in your account page.Download only one file at a time. Sometimes doing all of the files at once will lead to them all freezing.Also, please do not attempt to download to a mobile device. These should be saved to a computer and then synced to devices such as phones and tablets.You can also learn online instead of downloading, but we encourage you to download for better results and viewing quality during your learn. Lastly, download times are much quicker in the mornings, before noon, Pacific time. during download make sure your device is not sleeping off screen.What is the refund policy “Modern Deep Learning in Python”?We’ll Bear The Risk, You’ll Take The Results…Within 30 days of purchased |Modern Deep Learning in Python  |, if you don’t get anything out of the program, or if your order has any problem, or maybe for some reason, you just don’t like the way it is. Please contact us and we will do our best to assist. Thank you for your understanding.Have More Questions?Our support staff is the best by far! please do not hesitate to contact us at email: [email protected] and we’ll be happy to help!You want to get “Modern Deep Learning in Python” now right?!!!YES! I’M READY TO ADD TO CART BUTTON ON THIS PAGE NOW !