Everyone wants to minimize losses and maximize profits. AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Thanks to Deep Learning and improved methodologies to analyze data, Data Analysts and Data Scientists are increasingly using data to make informed decisions. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It’s something that’s moving beyond the realm of data science – if you’re a developer, this course gives you a great opportunity to expand your skillset. In this course, you will learn: What is deep learning and how to implement it The core concepts of deep learning Basic of Deep Learning and modern best practices with a digit classification problem of MNIST The types of problems deep learning/AI solves Learn about Data Science, its challenges and how to tackle them Apply deep learning to other domains like Language Modeling, ChatBots and Machine Translation using the one of the powerful architectures of DL, RNN At the end of this course, you will learn all the essentials needed to explore and understand what is deep learning and will perform deep learning tasks first hand.

Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way! This course covers a variety of topics, including Neural Network Basics TensorFlow Basics Artificial Neural Networks Densely Connected Networks Convolutional Neural Networks Recurrent Neural Networks AutoEncoders Reinforcement Learning OpenAI Gym and much more! There are many Deep Learning Frameworks out there, so why use TensorFlow? TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google! Become a machine learning guru today! We'll see you inside the course!

WHAT IS DATA SCIENCE As the world is progressing in science and technology, there is an enormous increase in the need for advanced tools to store information and mine data that is being produced indefinitely. And the key to this problem is data science. Data science is a field of study that develops scientific and systematic methods to record, process and analyze data to withdraw significant and useful information that can be both structured and unstructured. Unstructured data is the one that is generated by mobile devices and websites while structured data is an organized data which is mostly generated by the users e.g. emails, chats, telephone calls etc. Data science uses scientific methods and algorithms to extract knowledge. Industries require the use of this field immensely and the industrialists now realize the value of data science and the benefits it can provide to the business, thus, it has become very popular currently. The need for data science An immediate question that rises in the mind after hearing about data science is why is there a need to dig into depths for such a tool? So, it’s important to understand that previously the data produced used to be structured and thus it was somehow easy to extract the meaningful information and process it. However, contemporarily the data that is being produced is mostly unstructured as there are multiple sources of its generation such as multimedia files, logs, documents etc. and data science provides aid in turning raw data into consequential one. Human brains possess the intelligence to perceive things as they are i.e. processing the information that we see and store it. It is just a power that we humans have and because we are doing it constantly without any deliberate effort, it is trivial to us. However, parallel to this brilliant power that we hold, it is also to have a clear understanding of the fact that it is limited. Human brain is also prone to forgetting, and impeding memory, perceptions and predictions. Here, the computer’s prowess proves to be helpful. With the advance improvement in technology, we are now able to leave our locations through our smartphones for example uber that makes our traveling so much convenient, our movements can be tracked easily, our online behaviors and patterns of search are constantly being recorded through our acceptance of cookies, the steps we take during a day can be tracked by the help of some specific apps, even our health can be tracked. All of this is possible due to the invention of technology, softwares and the ability to record and process information adequately. Our information is being stored and without us even noticing. This tracking ability is not only applicable on the people sitting at home, but government agencies, stock markets, civil officers, intelligence agencies, business owners are all the concerned parties and is useful for each one of the occupations. Data science is used in providing systematic methods to give useful insights from the enormous data that is being generated. Therefore, data science is indeed important. Other than the general importance, data science is extremely beneficial to the business in numerous ways: · It provides immense help in decision taking. As discusses, data science analyzes the data in a proper way to solve the problems arising in the business by providing healthy opportunities and unleashing ideas to remove the threats creating issues for the business. It foresees the trends that might surface in the future. · Data science helps in the business to flourish by improving the product in every which way possible. By tracking the patterns of the consumer’s purchase and knowing about the likes and dislikes, the managers will know what improvisations are required, what kind of product is outdated and what basically is the trend prevailing. Also, by tracing the online trends, the business will be better able to identify the wants of the consumers and produce accordingly to satisfy their needs in an ample manner. · Similarly, the business will be able to be managed efficiently. Due to the help of data science, the owners will be able to understand the needs of the customers in better way and this will lead to increasing the number of customers. Satisfying the customers efficiently will result in business optimization. · Data science owns the capability of predicting trends which proves to be a great benefit to the business. To be able to read the patterns and predict the future tendency of peoples’ wants is going to be lucrative in every which way possible. · Advertisement holds are huge part in the business being able to thrive and the product reaching its target audience. Data science helps in better marketing. Companies need good marketing strategies every day and they analyze their data to create impactful advertisements. Data science can make it easier by making smarter decisions for them and run a campaign for them for the specific purpose. · Data science holds the future. Industries are becoming data driven and they need data scientists to process and analyze the data for them and make smarter decision by predicting information. Therefore, it holds the career for tomorrow. · Reading resumes and appointing the right person for the job is a daily task in a company. This exhausting task can be done easily and efficiently through the amount of data available online. Social media and job search websites can be searched thoroughly by the data scientists and select the perfect candidate according to his talents and capabilities. · Data science can also provide beneficial aid in identifying the right target audience. Data science can prove to be helpful in collecting customer data and gaining insights onto their liking and disliking. The company can learn more about their audience and gain in depth knowledge to target the right group of audience and increase profit margins. Advantages and disadvantages Data science is a highly prestigious and versatile career. It also holds great scope in personal growth. It is highly in demand and it holds the future. All the industries realize the importance of the field and all the benefits that it can reap for them and is held as an important position for the company, so it is a highly paid profession. The job is extremely interesting. There are no repetitive tasks to be performed and thus it is not boring at all. Data science is a field that aims in making data meaningful for the company by improving its quality. It makes computers smart enough to read the behaviors and patterns of the customers though their historical purchases and search history. This machine learning phenomenon helps the company in producing better products. However, the field has its disadvantages as well. Data science is a very vague term and it is not easily understandable. Mastering the degree of data science is nearly impossible. To hold proficiency in the field, you require large amount of domain knowledge. Data science helps in predicting future trends but sometimes the results do not yield to be the one as expected. It can happen due to numerous reasons like poor management or scarce resources. Business intelligence vs Data Science Data science is commonly mistaken with business intelligence. Business intelligence focuses on analyzing the previous data and run research on it to explain the business trends. It manages, arranges and produces information from the data to answer business problems. It is much simpler than data science. Data science uses complex tools and statistics and analyzes data based on past or current to forecast the future trends. it answers open ended questions as to how and what could happen in the future while BI focuses on the question that asks what happened only. BI has a limited scope as it focuses on past and present, data science focuses on present and future and has unlimited scope. BI contains data that is only structured, while data science contains both structured and unstructured data. BI helps companies in solving their problems while data scientists raise the problems and solve them too. Tools that are used in BI are MS excel, SAS BI, MicroStrategy. Tools used by data science are Hadoop, Qlikview, Python, TensorFlow. Artificial intelligence vs Data science Data science makes use of artificial intelligence but they are not entirely the same. Artificial intelligence is known as to counterfeit human intelligence into machines to make them capable of imitating humans and be able to solve problems and make decisions. Data science on the other hand is the process of analyzing, pre-processing and maintaining data for analytics and visualization to forecast future trends and patterns. Data science uses statistical techniques whereas artificial intelligence makes use of algorithms. Data science does not involve scientific processing as much as artificial intelligence. Data science uses data analytics technique while artificial intelligence uses machine learning. Big data vs Data Science These two terms are often heard together but they are quite distinguished from one another. Big data is focused on handling large data while Data science focuses on analyzing the data and predicting future outcomes. Big data includes the process of handling large volumes of data and generating insights while data science predicts the outcomes and analyzes the trends and makes rational and smart decisions accordingly. E-commerce, telecommunication and security service industries use big data. While data science plays a huge role in industries involving science, risk analytics, advertisements etc. Data scientist Being a data scientist holds great responsibility, proficiency and knowledge in the domain. A data scientist requires to be adept in statistics and mathematics to be able to analyze and process data properly. A data scientist is supposed to be good at machine learning . It is also important that the scientist has great understanding of the domain he is working in to be able to do he is work appropriately. He should be able to apply algorithms where requires and have good knowledge of coding skills . He should also have sufficient experience in working in the field. He should also be good at programming and CS fundamentals. A scientist should also have apt communication skills , as he will be directly in contact with the upper management and will have to communicate his results to them. Its important for a data scientist to understand his projects and fulfill them properly by asking the right questions, using the right resources and tools and then brief the entire process to the stakeholders appropriately to achieve accurate results. Pay scale of a data scientist According to glassdoor average salary of a data scientist is $113,309 per year. The pay varies from $83,000 as lowest to $154,000. As for the additional cash compensation, it ranges from $3,850 - $26,084, with an average of $11,258. However, the salary varies according to the industry as well. For example, Facebook pays its data scientist an average of $146,221 per year according to 115 salaries it pays and Microsoft data scientist makes an average of $129,435 per year as per the 79 salaries it gives. Conclusion Data science holds its pros and cons and it might take time for it to gain proficiency and momentum but it is without doubt an ever-evolving industry and holds the future. With the humungous outbreak of data, the need for data science will elevate and thus will provide more opportunities to make key businesses decisions. With this evolution, it is also important that data scientists stay motivated to perform their job sincerely and efficiently.

In this complete deep reinforcement learning course you will learn a repeatable framework for reading and implementing deep reinforcement learning research papers. You will read the original papers that introduced the Deep Q learning , Double Deep Q learning , and Dueling Deep Q learning algorithms. You will then learn how to implement these in pythonic and concise PyTorch code, that can be extended to include any future deep Q learning algorithms. These algorithms will be used to solve a variety of environments from the Open AI gym's Atari library, including Pong, Breakout, and Bankheist. You will learn the key to making these Deep Q Learning algorithms work, which is how to modify the Open AI Gym's Atari library to meet the specifications of the original Deep Q Learning papers. You will learn how to: Repeat actions to reduce computational overhead Rescale the Atari screen images to increase efficiency Stack frames to give the Deep Q agent a sense of motion Evaluate the Deep Q agent's performance with random no-ops to deal with model over training Clip rewards to enable the Deep Q learning agent to generalize across Atari games with different score scales If you do not have prior experience in reinforcement or deep reinforcement learning, that's no problem. Included in the course is a complete and concise course on the fundamentals of reinforcement learning. The introductory course in reinforcement learning will be taught in the context of solving the Frozen Lake environment from the Open AI Gym. We will cover: Markov decision processes Temporal difference learning The original Q learning algorithm How to solve the Bellman equation Value functions and action value functions Model free vs. model based reinforcement learning Solutions to the explore-exploit dilemma, including optimistic initial values and epsilon-greedy action selection Also included is a mini course in deep learning using the PyTorch framework. This is geared for students who are familiar with the basic concepts of deep learning, but not the specifics, or those who are comfortable with deep learning in another framework, such as Tensorflow or Keras. You will learn how to code a deep neural network in Pytorch as well as how convolutional neural networks function. This will be put to use in implementing a naive Deep Q learning agent to solve the Cartpole problem from the Open AI gym.

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

*** 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 course covers the general workflow of a deep learning project, implemented using PyTorch in Google Colab. At the end of the course, students will be proficient at using Google Colab as well as PyTorch in their own projects. Students will also learn about the theoretical foundations for various deep learning models and techniques, as well as how to implement them using PyTorch. Finally, the course ends by offering an overview on general deep learning and how to think about problems in the field; students will gain a high-level understanding of the role deep learning plays in the field of AI. Learn how to utilize Google Colab as an online computing platform in deep learning projects, including running Python code, using a free GPU, and working with external files and folders Understand the general workflow of a deep learning project Examine the various APIs (datasets, modeling, training) PyTorch offers to facilitate deep learning Learn about the theoretical basis for various deep learning models such as convolutional networks or residual networks and what problems they address Gain an overview understanding of deep learning in the context of the artificial intelligence field and its best practices