This course is a comprehensive guide to Deep Learning and Neural Networks. The theories are explained in depth and in a friendly manner. After that, we'll have the hands-on session, where we will be learning how to code Neural Networks in PyTorch, a very advanced and powerful deep learning framework! The course includes the following Sections: -------------------------------------------------------------------------------------------------------- Section 1 - How Neural Networks and Backpropagation Works In this section, you will deeply understand the theories of how neural networks  and the backpropagation algorithm works, in a friendly manner. We will walk through an example and do the calculations step-by-step. We will also discuss the activation functions used in Neural Networks, with their advantages and disadvantages! Section 2 - Loss Functions In this section, we will introduce the famous loss functions that are used in Deep Learning and Neural Networks. We will walk through when to use them and how they work. Section 3 - Optimization In this section, we will discuss the optimization techniques used in Neural Networks, to reach the optimal Point, including Gradient Descent, Stochastic Gradient Descent, Momentum, RMSProp, Adam, AMSGrad, Weight Decay and Decoupling Weight Decay, LR Scheduler and others. Section 4 - Weight Initialization In this section,we will introduce you to the concepts of weight initialization in neural networks, and we will discuss some techniques of weights initialization including Xavier initialization and He norm initialization. Section 5 - Regularization Techniques In this section, we will introduce you to the regularization techniques in neural networks. We will first introduce overfitting and then introduce how to prevent overfitting by using regularization techniques, inclusing L1, L2 and Dropout. We'll also talk about normalization as well as batch normalization and Layer Normalization. Section 6- Introduction to PyTorch In this section, we will introduce the deep learning framework we'll be using through this course, which is PyTorch. We will show you how to install it, how it works and why it's special, and then we will code some PyTorch tensors and show you some operations on tensors, as well as show you Autograd in code! Section 7 - Practical Neural Networks in PyTorch - Application 1 In this section, you will apply what you've learned to build a Feed Forward Neural Network to classify handwritten digits. This is the first application of Feed Forward Networks we will be showing. Section 8 - Practical Neural Networks in PyTorch - Application 2 In this section, we will build a feed forward Neural Network to classify weather a person has diabetes or not. We will train the network on a large dataset of diabetes! Section 9 - Visualize the Learning Process In this section, we will visualize how neural networks are learning, and how good they are at separating non-linear data! Section 10 - Implementing a Neural Network from Scratch with Python and Numpy In this section, we will understand and code up a neural network without using any deep learning library (from scratch using only python and numpy). This is necessary to understand how the underlying structure works. Section 11 - Convolutional Neural Networks In this section, we will introduce you to Convolutional Networks that are used for images. We will show you first the relationship to Feed Forward Networks, and then we will introduce you the concepts of Convolutional Networks one by one! Section 12 - Practical Convolutional Networks in PyTorch In this section, we will apply Convolutional Networks to classify handwritten digits. This is the first application of CNNs we will do. Section 13- Deeper into CNN: Improving and Plotting In this section, we will improve the CNN that we built in the previous section, as well show you how to plot the results of training and testing! Moreover, we will show you how to classify your own handwritten images through the network! Section 14 - CNN Architectures In this section, we will introduce the CNN architectures that are widely used in all deep learning applications. These architectures are: AlexNet, VGG net, Inception Net, Residual Networks and Densely Connected Networks. We will also discuss some object detection architectures. Section 15- Residual Networks In this section, we will dive deep into the details and theory of Residual Networks, and then we'll build a Residual Network in PyTorch from scratch! Section 16 - Transfer Learning in PyTorch - Image Classification In this section, we will apply transfer learning on a Residual Network, to classify ants and bees. We will also show you how to use your own dataset and apply image augmentation. After completing this section, you will be able to classify any images you want! Section 17- Convolutional Networks Visualization In this section, we will visualize what the neural networks output, and what they are really learning. We will observe the feature maps of the network of every layer! Section 18 - YOLO Object Detection (Theory) In this section, we will learn one of the most famous Object Detection Frameworks: YOLO!! This section covers the theory of YOLO in depth. Section 19 - Autoencoders and Variational Autoencoders In this section, we will cover Autoencoders and Denoising Autoencoders. We will then see the problem they face and learn how to mitigate it with Variational Autoencoders. Section 20 - Recurrent Neural Networks In this section, we will introduce you to Recurrent Neural Networks and all their concepts. We will then discuss the Backpropagation through  time, the vanishing gradient problem, and finally about Long Short Term Memory (LSTM) that solved the problems RNN suffered from. Section 21 - Word Embeddings In this section, we will discuss how words are represented as features. We will then show you some Word Embedding models.  We will also show you how to implement word embedding in PyTorch! Section 22 - Practical Recurrent Networks in PyTorch In this section, we will apply Recurrent Neural Networks using LSTMs in PyTorch to generate text similar to the story of Alice in Wonderland! You can just replace the story with any other text you want, and the RNN will be able to generate text similar to it! Section 23 - Sequence Modelling In this section, we will learn about Sequence-to-Sequence Modelling. We will see how Seq2Seq models work and where they are applied. We'll also talk about Attention mechanisms and see how they work. Section 24 - Practical Sequence Modelling in PyTorch - Build a Chatbot In this section, we will apply what we learned about sequence modeling and build a Chatbot with Attention Mechanism. Section 25 - Saving and Loading Models In this section, we will show you how to save and load models in PyTorch, so you can use these models either for later testing, or for resuming training! Section 26 - Transformers In this section, we will cover the Transformer, which is the current state-of-art model for NLP and language modeling tasks. We will go through each component of a transformer. Section 27 - Build a Chatbot with Transformers In this section, we will implement all what we learned in the previous section to build a Chatbot using Transformers.

Learn deep learning regression through a practical course with R statistical software using S&P 500® Index ETF prices historical data for algorithm learning. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business forecasting research. All of this while exploring the wisdom of best academics and practitioners in the field. Become a Deep Learning Regression Expert in this Practical Course with R Read or download S&P 500® Index ETF prices data and perform deep learning regression operations by installing related packages and running script code on RStudio IDE. Create target and predictor algorithm features for supervised regression learning task. Select relevant predictor features subset through Student t-test and ANOVA F-test univariate filter methods and extract predictor features transformations through principal component analysis. Train algorithm for mapping optimal relationship between target and predictor features through artificial neural network, deep neural network and recurrent neural network. Regularize algorithm learning through nodes connections weight decay, visible or hidden layers dropout fractions and stochastic gradient descent algorithm learning rate. Extract algorithm predictor features through stacked autoencoders, restricted Boltzmann machines and deep belief network. Minimize recurrent neural network vanishing gradient problem through long short-term memory units. Test algorithm for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics. Assess mean absolute error, root mean squared error for scale-dependent metrics and mean absolute percentage error, mean absolute scaled error for scale-independent metrics. Become a Deep Learning Regression Expert and Put Your Knowledge in Practice Learning deep learning regression is indispensable for data mining applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data mining, applied statistical learning or artificial intelligence. And its necessary for business forecasting research. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500® Index ETF prices historical data for algorithm learning to achieve greater effectiveness. Content and Overview This practical course contains 33 lectures and 4 hours of content. It’s designed for all deep learning regression knowledge levels and a basic understanding of R statistical software is useful but not required. At first, you’ll learn how to read or download S&P 500® Index ETF prices historical data to perform deep learning regression operations by installing related packages and running script code on RStudio IDE. Then, you’ll define algorithm features by creating target and predictor variables for supervised regression learning task. Next, you’ll only include relevant predictor features subset or transformations in algorithm learning through features selection and features extraction procedures. For features selection, you’ll implement Student t-test and ANOVA F-test univariate filter methods. For features extraction, you’ll implement principal components analysis. After that, you’ll define algorithm training through mapping optimal relationship between target and predictor features within training range. For algorithm training, you’ll define optimal parameters estimation or fine tuning, bias-variance trade-off, optimal model complexity and artificial neural network regularization. For artificial neural network regularization, you’ll define node connection weights, visible and hidden layers dropout fractions, stochastic gradient descent algorithm learning and momentum rates. Later, you’ll define algorithm testing through evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics. For scale-dependent metrics, you’ll define mean absolute error and root mean squared error. For scale-independent metrics, you’ll define mean absolute percentage error and mean absolute scaled error. Next, you’ll define artificial neural network. Then, you’ll implement algorithm training for mapping optimal relationship between target and predictor features. For algorithm training, you’ll use only relevant predictor features subset or transformations through principal components analysis procedure and nodes connections weight decay regularization. After that, you’ll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics. After that, you’ll define deep neural network. Next, you’ll implement algorithm training for mapping optimal relationship between target and predictor features. For algorithm training, you’ll use only relevant features subset or transformations and visible or hidden dropout fractions regularization. For features extraction, you’ll use principal components analysis, stacked autoencoders, restricted Boltzmann machines and deep belief network. Later, you’ll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics. Later, you’ll define recurrent neural network and long short-term memory. Next, you’ll implement algorithm training for mapping optimal relationship between target and predictor features. For algorithm training, you’ll use stochastic gradient descent algorithm learning rate regularization. Then, you’ll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics. Finally, you’ll compare deep learning regression algorithms training and testing.

It’s hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing). A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you. So what is this course all about, and how have things changed since then? In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs ( recurrent neural networks ), CNNs ( convolutional neural networks ), and word embedding algorithms such as word2vec and GloVe. This course takes you to a higher systems level of thinking. Since you know how these things work, it’s time to build systems using these components. At the end of this course, you'll be able to build applications for problems like: text classification (examples are sentiment analysis and spam detection) neural machine translation question answering We'll take a brief look chatbots and as you’ll learn in this course, this problem is actually no different from machine translation and question answering. To solve these problems, we’re going to look at some advanced Deep NLP techniques, such as: bidirectional RNNs seq2seq (sequence-to-sequence) attention memory networks All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Python libraries such as Keras , Numpy , Tensorflow , and Matpotlib to make things super easy and focus on the high-level concepts. I am always available to answer your questions and help you along your data science journey. 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. See you in class! "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 scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After 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: Decent Python coding skills Understand RNNs, CNNs, and word embeddings Know how to build, train, and evaluate a neural network in Keras WHAT 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)

Welcome to Cutting-Edge AI! This is technically Deep Learning in Python part 11 of my deep learning series, and my 3rd reinforcement learning course. Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks) . While both of these have been around for quite some time, it’s only been recently that Deep Learning has really taken off, and along with it, Reinforcement Learning. The maturation of deep learning has propelled advances in reinforcement learning, which has been around since the 1980s, although some aspects of it, such as the Bellman equation, have been for much longer. Recently, these advances have allowed us to showcase just how powerful reinforcement learning can be. We’ve seen how AlphaZero can master the game of Go using only self-play. This is just a few years after the original AlphaGo already beat a world champion in Go. We’ve seen real-world robots learn how to walk, and even recover after being kicked over, despite only being trained using simulation. Simulation is nice because it doesn’t require actual hardware, which is expensive. If your agent falls down, no real damage is done. We’ve seen real-world robots learn hand dexterity, which is no small feat. Walking is one thing, but that involves coarse movements. Hand dexterity is complex - you have many degrees of freedom and many of the forces involved are extremely subtle. Imagine using your foot to do something you usually do with your hand, and you immediately understand why this would be difficult. Last but not least - video games. Even just considering the past few months, we’ve seen some amazing developments. AIs are now beating professional players in CS:GO and Dota 2 . So what makes this course different from the first two? Now that we know deep learning works with reinforcement learning, the question becomes: how do we improve these algorithms? This course is going to show you a few different ways: including the powerful A2C (Advantage Actor-Critic) algorithm, the DDPG (Deep Deterministic Policy Gradient) algorithm, and evolution strategies . Evolution strategies is a new and fresh take on reinforcement learning, that kind of throws away all the old theory in favor of a more "black box" approach, inspired by biological evolution. What’s also great about this new course is the variety of environments we get to look at. First, we’re going to look at the classic Atari environments. These are important because they show that reinforcement learning agents can learn based on images alone. Second, we’re going to look at MuJoCo , which is a physics simulator. This is the first step to building a robot that can navigate the real-world and understand physics - we first have to show it can work with simulated physics. Finally, we’re going to look at Flappy Bird , everyone’s favorite mobile game just a few years ago. Thanks for reading, and I’ll see you in class! "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 scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After 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: Calculus Probability Object-oriented programming Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations Linear regression Gradient descent Know how to build a convolutional neural network (CNN) in TensorFlow Markov Decision Proccesses (MDPs) WHAT 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)

Latest update : Instead of SSD, I show you how to use RetinaNet, which is better and more modern. I show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab . This is one of the most exciting courses I’ve done and it really shows how fast and how far deep learning has come over the years. When I first started my deep learning series, I didn’t ever consider that I’d make two courses on convolutional neural networks . I think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover. Let me give you a quick rundown of what this course is all about: We’re going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as VGG , ResNet , and Inception (named after the movie which by the way, is also great!) We’re going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots. In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label. You can imagine that such a task is a basic prerequisite for self-driving vehicles . (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time) We’ll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors. Another very popular computer vision task that makes use of CNNs is called neural style transfer . This is where you take one image called the content image, and another image called the style image, and you combine these to make an entirely new image, that is as if you hired a painter to paint the content of the first image with the style of the other. Unlike a human painter, this can be done in a matter of seconds. I will also introduce you to the now-famous GAN architecture ( Generative Adversarial Networks ), where you will learn some of the technology behind how neural networks are used to generate state-of-the-art, photo-realistic images. Currently, we also implement object localization , which is an essential first step toward implementing a full object detection system. I hope you’re excited to learn about these advanced applications of CNNs, I’ll see you in class! AWESOME FACTS: One of the major themes of this course is that we’re moving away from the CNN itself, to systems involving CNNs. Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on high-level building blocks. The result? Almost zero math . Another result? No complicated low-level code such as that written in Tensorflow , Theano , or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written 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 scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After 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 how to build, train, and use a CNN using some library (preferably in Python) Understand basic theoretical concepts behind convolution and neural networks Decent Python coding skills, preferably in data science and the Numpy Stack WHAT 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)

This 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. You 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 scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After 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 descent Probability and statistics Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file Know how to write a neural network with Numpy WHAT 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)

*** As seen on Kickstarter *** Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence. --- Why Deep Learning A-Z? --- Here are five reasons we think Deep Learning A-Z™ really is different, and stands out from the crowd of other training programs out there: 1. ROBUST STRUCTURE The first and most important thing we focused on is giving the course a robust structure. Deep Learning is very broad and complex and to navigate this maze you need a clear and global vision of it. That's why we grouped the tutorials into two volumes, representing the two fundamental branches of Deep Learning: Supervised Deep Learning and Unsupervised Deep Learning. With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering Deep Learning. 2. INTUITION TUTORIALS So many courses and books just bombard you with the theory, and math, and coding... But they forget to explain, perhaps, the most important part: why you are doing what you are doing. And that's how this course is so different. We focus on developing an intuitive *feel* for the concepts behind Deep Learning algorithms. With our intuition tutorials you will be confident that you understand all the techniques on an instinctive level. And once you proceed to the hands-on coding exercises you will see for yourself how much more meaningful your experience will be. This is a game-changer. 3. EXCITING PROJECTS Are you tired of courses based on over-used, outdated data sets? Yes? Well then you're in for a treat. Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges: Artificial Neural Networks to solve a Customer Churn problem Convolutional Neural Networks for Image Recognition Recurrent Neural Networks to predict Stock Prices Self-Organizing Maps to investigate Fraud Boltzmann Machines to create a Recomender System Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize *Stacked Autoencoders is a brand new technique in Deep Learning which didn't even exist a couple of years ago. We haven't seen this method explained anywhere else in sufficient depth. 4. HANDS-ON CODING In Deep Learning A-Z™ we code together with you. Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means. In addition, we will purposefully structure the code in such a way so that you can download it and apply it in your own projects. Moreover, we explain step-by-step where and how to modify the code to insert YOUR dataset, to tailor the algorithm to your needs, to get the output that you are after. This is a course which naturally extends into your career. 5. IN-COURSE SUPPORT Have you ever taken a course or read a book where you have questions but cannot reach the author? Well, this course is different. We are fully committed to making this the most disruptive and powerful Deep Learning course on the planet. With that comes a responsibility to constantly be there when you need our help. In fact, since we physically also need to eat and sleep we have put together a team of professional Data Scientists to help us out. Whenever you ask a question you will get a response from us within 48 hours maximum. No matter how complex your query, we will be there. The bottom line is we want you to succeed. --- The Tools --- Tensorflow and Pytorch are the two most popular open-source libraries for Deep Learning. In this course you will learn both! TensorFlow was developed by Google and is used in their speech recognition system, in the new google photos product, gmail, google search and much more. Companies using Tensorflow include AirBnb, Airbus, Ebay, Intel, Uber and dozens more. PyTorch is as just as powerful and is being developed by researchers at Nvidia and leading universities: Stanford, Oxford, ParisTech. Companies using PyTorch include Twitter, Saleforce and Facebook. So which is better and for what? Well, in this course you will have an opportunity to work with both and understand when Tensorflow is better and when PyTorch is the way to go. Throughout the tutorials we compare the two and give you tips and ideas on which could work best in certain circumstances. The interesting thing is that both these libraries are barely over 1 year old. That's what we mean when we say that in this course we teach you the most cutting edge Deep Learning models and techniques. --- More Tools --- Theano is another open source deep learning library. It's very similar to Tensorflow in its functionality, but nevertheless we will still cover it. Keras is an incredible library to implement Deep Learning models. It acts as a wrapper for Theano and Tensorflow. Thanks to Keras we can create powerful and complex Deep Learning models with only a few lines of code. This is what will allow you to have a global vision of what you are creating. Everything you make will look so clear and structured thanks to this library, that you will really get the intuition and understanding of what you are doing. --- Even More Tools --- Scikit-learn the most practical Machine Learning library. We will mainly use it: to evaluate the performance of our models with the most relevant technique, k-Fold Cross Validation to improve our models with effective Parameter Tuning to preprocess our data, so that our models can learn in the best conditions And of course, we have to mention the usual suspects. This whole course is based on Python and in every single section you will be getting hours and hours of invaluable hands-on practical coding experience. Plus, throughout the course we will be using Numpy to do high computations and manipulate high dimensional arrays, Matplotlib to plot insightful charts and Pandas to import and manipulate datasets the most efficiently. --- Who Is This Course For? --- As you can see, there are lots of different tools in the space of Deep Learning and in this course we make sure to show you 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. If you already have experience with Deep Learning, you will find this course refreshing, inspiring and very practical. Inside Deep Learning A-Z™ you will master some of the most cutting-edge Deep Learning algorithms and techniques (some of which didn't even exist a year ago) and through this course you will gain an immense amount of valuable hands-on experience with real-world business challenges. Plus, inside you will find inspiration to explore new Deep Learning skills and applications. --- Real-World Case Studies --- Mastering Deep Learning is not just about knowing the intuition and tools, it's also about being able to apply these models to real-world scenarios and derive actual measurable results for the business or project. That's why in this course we are introducing six exciting challenges: #1 Churn Modelling Problem In this part you will be solving a data analytics challenge for a bank. You will be given a dataset with a large sample of the bank's customers. To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc. During a period of 6 months, the bank observed if these customers left or stayed in the bank. Your goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Besides, you are asked to rank all the customers of the bank, based on their probability of leaving. To do that, you will need to use the right Deep Learning model, one that is based on a probabilistic approach. If you succeed in this project, you will create significant added value to the bank. By applying your Deep Learning model the bank may significantly reduce customer churn. #2 Image Recognition In this part, you will create a Convolutional Neural Network that is able to detect various objects in images. We will implement this Deep Learning model to recognize a cat or a dog in a set of pictures. However, this model can be reused to detect anything else and we will show you how to do it - by simply changing the pictures in the input folder. For example, you will be able to train the same model on a set of brain images, to detect if they contain a tumor or not. But if you want to keep it fitted to cats and dogs, then you will literally be able to a take a picture of your cat or your dog, and your model will predict which pet you have. We even tested it out on Hadelin’s dog! #3 Stock Price Prediction In this part, you will create one of the most powerful Deep Learning models. We will even go as far as saying that you will create the Deep Learning model closest to “Artificial Intelligence” . Why is that? Because this model will have long-term memory, just like us, humans. The branch of Deep Learning which facilitates this is Recurrent Neural Networks. Classic RNNs have short memory, and were neither popular nor powerful for this exact reason. But a recent major improvement in Recurrent Neural Networks gave rise to the popularity of LSTMs (Long Short Term Memory RNNs) which has completely changed the playing field. We are extremely excited to include these cutting-edge deep learning methods in our course! In this part you will learn how to implement this ultra-powerful model, and we will take the challenge to use it to predict the real Google stock price. A similar challenge has already been faced by researchers at Stanford University and we will aim to do at least as good as them. #4 Fraud Detection According to a recent report published by Markets & Markets the Fraud Detection and Prevention Market is going to be worth $33.19 Billion USD by 2021. This is a huge industry and the demand for advanced Deep Learning skills is only going to grow. That’s why we have included this case study in the course. This is the first part of Volume 2 - Unsupervised Deep Learning Models. The business challenge here is about detecting fraud in credit card applications. You will be creating a Deep Learning model for a bank and you are given a dataset that contains information on customers applying for an advanced credit card. This is the data that customers provided when filling the application form. Your task is to detect potential fraud within these applications. That means that by the end of the challenge, you will literally come up with an explicit list of customers who potentially cheated on their applications. #5 & 6 Recommender Systems From Amazon product suggestions to Netflix movie recommendations - good recommender systems are very valuable in today's World. And specialists who can create them are some of the top-paid Data Scientists on the planet. We will work on a dataset that has exactly the same features as the Netflix dataset: plenty of movies, thousands of users, who have rated the movies they watched. The ratings go from 1 to 5, exactly like in the Netflix dataset, which makes the Recommender System more complex to build than if the ratings were simply “Liked” or “Not Liked”. Your final Recommender System will be able to predict the ratings of the movies the customers didn’t watch. Accordingly, by ranking the predictions from 5 down to 1, your Deep Learning model will be able to recommend which movies each user should watch. Creating such a powerful Recommender System is quite a challenge so we will give ourselves two shots. Meaning we will build it with two different Deep Learning models. Our first model will be Deep Belief Networks, complex Boltzmann Machines that will be covered in Part 5. Then our second model will be with the powerful AutoEncoders, my personal favorites. You will appreciate the contrast between their simplicity, and what they are capable of. And you will even be able to apply it to yourself or your friends. The list of movies will be explicit so you will simply need to rate the movies you already watched, input your ratings in the dataset, execute your model and voila! The Recommender System will tell you exactly which movies you would love one night you if are out of ideas of what to watch on Netflix! --- Summary --- In conclusion, this is an exciting training program filled with intuition tutorials, practical exercises and real-World case studies. We are super enthusiastic about Deep Learning and hope to see you inside the class! Kirill & Hadelin

Deep learning is the next big thing. It’s a part of machine learning. Its favorable results in applications with huge and complex data is remarkable. R programming language is very popular among data miners and statisticians. Deep learning refers to artificial neural networks that are composed of many layers. Deep learning is a powerful set of techniques for finding accurate information from raw data. This comprehensive 2-in-1 course will help you explore and create intelligent systems using deep learning techniques. You’ll understand the usage of multiple applications like Natural Language Processing, bioinformatics, recommendation engines, etc. where deep learning models are implemented. You’ll get hands on with various deep learning scenarios and get mind blowing insights from your data. You’ll be able to master the intricacies of R deep learning packages such as TensorFlow. You’ll also learn deep learning in different domains using practical examples from text, image, and speech. Contents and Overview This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Deep Learning with R, covers videos that will teach you how to leverage deep learning to make sense of your raw data by exploring various hidden layers of data. Each video in this course provides a clear and concise introduction of a key topic, one or more example of implementations of these concepts in R, and guidance for additional learning, exploration, and application of the skills learned therein. You’ll start by understanding the basics of deep learning and artificial neural networks and move on to exploring advanced ANN’s and RNN’s. You’ll dive deep into convolutional neural networks and unsupervised learning. You’ll also learn about the applications of deep learning in various fields and understand the practical implementations of Scalability, HPC and Feature Engineering. Finally, starting out at a basic level, you’ll be learning how to develop and implement deep learning algorithms using R in real world scenarios. The second course, R Deep Learning Solutions, covers powerful, independent videos to build deep learning models in different application areas using R libraries. It will help you resolve problems during the execution of different tasks in deep learning, neural networks, and advanced machine learning techniques. You’ll start with different packages in deep learning, neural networks, and structures. You’ll also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. Finally, you’ll explore complex deep learning algorithms and various deep learning packages and libraries in R. By the end of this training program you’ll be able to to develop and implement deep learning algorithms using R in real world scenarios and have an understanding of different deep learning packages so you’ll have the most appropriate solutions for your problems. About the Authors Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna and founder of (ContinuousAI).com an open source project aiming to connect people and reorganize resources in the context of Continuous Learning and AI. He is also the PhD students' representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: A comparison between CNNs and HTMs on object recognition tasks". Dr. PKS Prakash is a data scientist and an author. He has spent last the 12 years developing many data science solutions to solve problems from leading companies in the healthcare, manufacturing, pharmaceutical, and e-commerce domains. He currently works as data science manager at ZS Associates.  Prakash has a PhD in Industrial and System Engineering from Wisconsin-Madison, U.S. He gained his second PhD in Engineering at the University of Warwick, UK. He has a master’s degree from University of Wisconsin-Madison, U.S., and a bachelor’s degree from National Institute of Foundry and Forge Technology (NIFFT), India. He is co-founder of Warwick Analytics, which is based on his PhD work from the University of Warwick, UK. Prakash has been published widely in research areas of operational research and management, soft computing tools, and advanced algorithms in leading journals such as IEEE-Trans, EJOR, and IJPR among others. He edited an issue on "Intelligent Approaches to Complex Systems" and contributed to books such as Evolutionary Computing in Advanced Manufacturing published by Wiley and Algorithms and Data Structures using R published by Packt Publishing. Achyutuni Sri Krishna Rao is a data scientist, a civil engineer, and an author. He has spent the last four years developing many data science solutions to solve problems from leading companies in the healthcare, pharmaceutical, and manufacturing domains. He currently works as a data science consultant at ZS Associates. Sri Krishna’s background is a master’s in Enterprise Business Analytics and Machine Learning from the National University of Singapore, Singapore. He also has a bachelor’s degree from the National Institute of Technology Warangal, India.  Sri Krishna has been published widely in the research areas of civil engineering. He contributed to the book Algorithms and Data Structures using R published by Packt Publishing.

Self-driving cars have rapidly become one of the most transformative technologies to emerge . Fuelled by Deep Learning algorithms, they are continuously driving our society forward and creating new opportunities in the mobility sector. Deep Learning jobs command some of the highest salaries in the development world . This is the first, and only course which makes practical use of Deep Learning, and applies it to building a self-driving car , one of the most disruptive technologies in the world today . Learn & Master Deep Learning in this fun and exciting course with top instructor Rayan Slim. With over 28000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course. You'll go from beginner to Deep Learning expert and your instructor will complete each task with you step by step on screen. By the end of the course, you will have built a fully functional self-driving car fuelled entirely by Deep Learning. This powerful simulation will impress even the most senior developers and ensure you have hands on skills in neural networks that you can bring to any project or company. This course will show you how to: Use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car . Learn to train a Perceptron-based Neural Network to classify between binary classes. Learn to train Convolutional Neural Networks to identify between various traffic signs. Train Deep Neural Networks to fit complex datasets. Master Keras , a power Neural Network library written in Python. Build and train a fully functional self driving car to drive on its own ! No experience required . This course is designed to take students with no programming/mathematics experience to accomplished Deep Learning developers. This course also comes with all the source code and friendly support in the Q&A area.

The primary objective of this course is to teach you the practical hands-on skills you need to solve image classification problems - and in particular, multi-class classification. And all this, well, we shall be doing without bringing in unnecessary math logic behind it all. In this course you will learn about the most widely used type of deep neural networks (Convolution neural network). As used by top companies all over the world like Facebook and Google. You will learn how to use Keras in your applications to solve problems and package your models. Build a Rest API to serve your deep learning models.