Welcome to the best online course for learning about Deep Learning with Python and PyTorch! PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is rapidly becoming one of the most popular deep learning frameworks for Python. Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. This course focuses on balancing important theory concepts with practical hands-on exercises and projects that let you learn how to apply the concepts in the course to your own data sets! When you enroll in this course you will get access to carefully laid out notebooks that explain concepts in an easy to understand manner, including both code and explanations side by side. You will also get access to our slides that explain theory through easy to understand visualizations. In this course we will teach you everything you need to know to get started with Deep Learning with Pytorch, including: NumPy Pandas Machine Learning Theory Test/Train/Validation Data Splits Model Evaluation - Regression and Classification Tasks Unsupervised Learning Tasks Tensors with PyTorch Neural Network Theory Perceptrons Networks Activation Functions Cost/Loss Functions Backpropagation Gradients Artificial Neural Networks Convolutional Neural Networks Recurrent Neural Networks and much more! By the end of this course you will be able to create a wide variety of deep learning models to solve your own problems with your own data sets. So what are you waiting for? Enroll today and experience the true capabilities of Deep Learning with PyTorch! I'll see you inside the course! -Jose

THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH TENSORFLOW & KERAS IN PYTHON! It is a full 7-Hour Python Tensorflow & Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning  using two of the most important Deep Learning frameworks- Tensorflow and Keras. HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE: This course is your complete guide to practical machine & deep learning using the Tensorflow & Keras framework in Python.. This means, this course covers the important aspects of Keras and Tensorflow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow and Keras based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow and Keras is revolutionizing Deep Learning... By gaining proficiency in Keras and and Tensorflow, you can give your company a competitive edge and boost your career to the next level. THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL KERAS & TENSORFLOW BASED DATA SCIENCE! But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources  using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.. This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the Tensorflow framework. Unlike other Python courses, we dig deep into the statistical modeling features of Tensorflow & Keras and give you a one-of-a-kind grounding in these frameworks! DISCOVER 8 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON BASED TENSORFLOW DATA SCIENCE: • A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda • Getting started with Jupyter notebooks for implementing data science techniques in Python • A comprehensive presentation about Tensorflow & Keras installation and a brief introduction to the other Python data science packages • Brief introduction to the working of Pandas and Numpy • The basics of the Tensorflow syntax and graphing environment • The basics of the Keras syntax • Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow & Keras frameworks • You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow & Keras BUT,  WAIT! THIS ISN'T JUST ANY OTHER DATA SCIENCE COURSE: You’ll start by absorbing the most valuable Python Tensorflow and Keras basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real -life. After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python along with gaining fluency in Tensorflow and Keras. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !! The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today, start analyzing  data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities. This course will take students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different  techniques on real data and interpret the results.. After each video you will learn a new concept or technique which you may apply to your own projects! JOIN THE COURSE NOW!

This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free. This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited. Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture! If you are a programmer and you want to enhance your coding abilities by learning about data science , then this course is for you. If you have a technical or mathematical background, and you want use your skills to make data-driven decisions and optimize your business using scientific principles, then this course is for you. 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: calculus (taking derivatives) matrix arithmetic probability Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file 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 a practitioner of Deep Learning, I am trying to bring many relevant topics under one umbrella in the following topics. Deep Learning has been most talked about for the last few years and the knowledge has been spread across multiple places. 1. The content (80% hands-on and 20% theory) will prepare you to work independently on Deep Learning projects 2. Foundation of Deep Learning TensorFlow 2.x 3. Use TensorFlow 2.x for Regression (2 models) 4. Use TensorFlow 2.x for Classifications (2 models) 5. Use Convolutional Neural Net (CNN) for Image Classifications (5 models) 6. CNN with Image Data Generator 7. Use Recurrent Neural Networks (RNN) for Sequence data (3 models) 8. Transfer learning 9. Generative Adversarial Networks (GANs) 10. Hyperparameters Tuning 11. How to avoid Overfitting 12. Best practices for Deep Learning and Award-winning Architectures

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.

*** 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.

This video course will get you up-and-running with one of the most cutting-edge deep learning libraries: PyTorch. Written in Python, PyTorch is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs. In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you will implement various mechanisms of the PyTorch framework to do these tasks. By the end of the video course, you will have developed a good understanding of, and feeling for, the algorithms and techniques used. You'll have a good knowledge of how PyTorch works and how you can use it in to solve your daily machine learning problems. This course uses Python 3.6, and PyTorch 0.3, while not the latest version available, it provides relevant and informative content for legacy users of Python, and PyTorch. About the Author Anand Saha is a software professional with 15 years' experience in developing enterprise products and services. Back in 2007, he worked with machine learning to predict call patterns at TATA Communications. At Symantec and Veritas, he worked on various features of an enterprise backup product used by Fortune 500 companies. Along the way he nurtured his interests in Deep Learning by attending Coursera and Udacity MOOCs. He is passionate about Deep Learning and its applications; so much so that he quit Veritas at the beginning of 2017 to focus full time on Deep Learning practices. Anand built pipelines to detect and count endangered species from aerial images, trained a robotic arm to pick and place objects, and implemented NIPS papers. His interests lie in computer vision and model optimization.