Download or read book Deep Learning with C Net and Kelp Net written by Cole Matt R. and published by BPB Publications. This book was released on 2019-09-20 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get hands on with Kelp.Net, Microsoft's latest Deep Learning frameworkKey features Deep Learning Basics The ultimate Kelp.Net reference guide Develop state of the art deep learning applications C# deep learning code Develop advanced deep learning models with minimal code Develop your own advanced deep learning models Loading and Saving Deep Learning Models Comprehensive Kelp.Net reference Sample Deep Learning Models and Tests penCL Reference Easily add deep learning to your applications Many sample models and tests Intuitive and user friendly Description Deep Learning with Kelp.Net is the ultimate reference for C# .Net developers who are passionate about deep learning. Readers will learn all the skills necessary to develop powerful, scalable and flexible deep learning models from a fluid and easy to use API. Upon completing the book the reader will have all the tools necessary to add powerful deep learning capabilities to their new or existing applications.What will you learn In-depth knowledge of Kelp.Net How to develop deep learning models C# deep learning programming Open-Computing Language (OpenCL) Loading and saving deep learning models How to develop and use activation functions How to test deep learning modelsWho this book is for This book targets C# .Net developers who are passionate about deep learning yet want to do so from an easy and intuitive API.Table of contents1. Introduction2. ML/DL Terms and Concepts3. Deep Instrumentation4. Kelp.Net Reference5. Loading and Saving Models6. Model Testing and Training7. Sample Deep Learning Tests8. Creating Your Own Deep Learning Tests9. Appendix A: Evaluation Metrics10. Appendix B: OpenCL About the authorMatt R. Cole is a seasoned developer and published author with over 30 years' experience in Microsoft Windows, C, C++, C# and .Net. Matt is the owner of Evolved AI Solutions, a premier provider of advanced Machine Learning/Bio-AI technologies. Matt developed the first enterprise grade MicroService framework written completely in C# and .Net, which is used in production by a major hedge fund in NYC. Matt also developed the first Bio Artificial Intelligence framework which completely integrates mirror and canonical neurons. He continues to push the limits of Machine Learning, Biological Artificial Intelligence, Deep Learning and MicroServices. In his spare time Matt loves to continue his education and contribute to open source efforts such as Kelp.Net. His Website: www.evolvedaisolutions.comHis LinkedIn Profile: https://www.linkedin.com/in/evolvedai/His Blog: https://evolvedaisolutions.com/blog.html

Download or read book Introduction to Deep Learning written by Eugene Charniak and published by MIT Press. This book was released on 2019-02-19 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.

Download or read book Deep Learning for Beginners written by Steven Cooper and published by Roland Bind. This book was released on 2018-11-06 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: ☆★The Best Deep Learning Book for Beginners★☆ If you are looking for a complete beginners guide to learn deep learning with examples, in just a few hours, then you need to continue reading. This book delves into the basics of deep learning for those who are enthusiasts concerning all things machine learning and artificial intelligence. For those who have seen movies which show computer systems taking over the world like, Terminator, or benevolent systems that watch over the population, i.e. Person of Interest, this should be right up your alley. This book will give you the basics of what deep learning entails. That means frameworks used by coders and significant components and tools used in deep learning, that enable facial recognition, speech recognition, and virtual assistance. Yes, deep learning provides the tools through which systems like Siri became possible. ★★ Grab your copy today and learn ★★ ♦ Deep learning utilizes frameworks which allow people to develop tools which are able to offer better abstraction, along with simplification of hard programming issues. TensorFlow is the most popular tool and is used by corporate giants such as Airbus, Twitter, and even Google. ♦ The book illustrates TensorFlow and Caffe2 as the prime frameworks that are used for development by Google and Facebook. Facebook illustrates Caffe2 as one of the lightweight and modular deep learning frameworks, though TensorFlow is the most popular one, considering it has a lot of popularity, and thus, a big forum, which allows for assistance on main problems. ♦ The book considers several components and tools of deep learning such as the neural networks; CNNs, RNNs, GANs, and auto-encoders. These algorithms create the building blocks which propel deep learning and advance it. ♦ The book also considers several applications, including chatbots and virtual assistants, which have become the main focus for deep learning into the future, as they represent the next frontier in information gathering and connectivity. The Internet of Things is also represented here, as deep learning allows for the integration of various systems via an artificial intelligence system, which is already being used for the home and car functions. ♦ And much more... The use of data science adds a lot of value to businesses, and we will continue to see the need for data scientists grow. This book is probably one of the best books for beginners. It's a step-by-step guide for any person who wants to start learning deep learning and artificial intelligence from scratch. When data science can reduce spending costs by billions of dollars in the healthcare industry, why wait to jump in? If you want to get started on deep learning and the concepts that run artificial technologies, don't wait any longer. Scroll up and click the buy now button to get this book today!

Download or read book Deep Belief Nets in C and Cuda C written by Timothy Masters and published by Createspace Independent Publishing Platform. This book was released on 2016-04-04 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep belief nets are one of the most exciting recent developments in artificial intelligence. The structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a 'thought process' that is capable of learning abstract concepts built from simpler primitives. A typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. This book presents the essential building blocks of a common and powerful form of deep belief net: convolutional nets. These models are especially useful for image processing applications. At each step the text provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the executable CONVNET program which implements these algorithms, are available for free download from the author's website. Source code for the complete CONVNET program is not available, as much of it is highly specialized Windows interface code. Readers are responsible for writing their own main program, with all interface routines. You may freely use all of the core convolutional net routines in this book, as long as you remember that it is experimental code that comes with absolutely no guaranty of correct operation.

Download or read book Deep Learning Illustrated written by Jon Krohn and published by Addison-Wesley Professional. This book was released on 2019-08-05 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Download or read book Multi faceted Deep Learning written by Jenny Benois-Pineau and published by Springer Nature. This book was released on 2021-10-20 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem–oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.

Download or read book Deep Learning and Reinforcement Learning written by and published by BoD – Books on Demand. This book was released on 2023-11-15 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning and reinforcement learning are some of the most important and exciting research fields today. With the emergence of new network structures and algorithms such as convolutional neural networks, recurrent neural networks, and self-attention models, these technologies have gained widespread attention and applications in fields such as natural language processing, medical image analysis, and Internet of Things (IoT) device recognition. This book, Deep Learning and Reinforcement Learning examines the latest research achievements of these technologies and provides a reference for researchers, engineers, students, and other interested readers. It helps readers understand the opportunities and challenges faced by deep learning and reinforcement learning and how to address them, thus improving the research and application capabilities of these technologies in related fields.

Download or read book Deep Learning for Computer Vision written by Jyotsnarani Tripathy and published by Leilani Katie Publication. This book was released on 2024-09-05 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Jyotsnarani Tripathy, Assistant Professor, Department of CSE-AIML & IoT, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology (VNRVJIET), Hyderabad, Telangana, India. Dr.M.Kamal, Assistant Professor, Department of Computer Science, Jamal Mohamed College (Autonomous), Tiruchirappalli, Tamil Nadu, India. G.Ashalatha, Assistant Professor, Department of Artificial Intelligence & Data Science, CSE-Cyber Security, Data Science, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering &Technology (VNR VJIET), Hyderabad, Telangana, India. Mrs.EMN.Sharmila, Research Scholar, Department of Computer Science, CIRD Research Centre (Approved by University of Mysore), Bengaluru, Karnataka, India.

Download or read book Dive into Deep Learning written by Aston Zhang and published by Cambridge University Press. This book was released on 2023-12-07 with total page 581 pages. Available in PDF, EPUB and Kindle. Book excerpt: An approachable text combining the depth and quality of a textbook with the interactive multi-framework code of a hands-on tutorial.

Download or read book Deep Learning for Computer Vision written by Rajalingappaa Shanmugamani and published by Packt Publishing Ltd. This book was released on 2018-01-23 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book Description Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation. What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is for This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.

Download or read book Deep Learning on Edge Computing Devices written by Xichuan Zhou and published by Elsevier. This book was released on 2022-02-02 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are introduced in each section, and a comprehensive example, smart surveillance cameras, is presented at the end of the book, integrating innovation in both algorithm and hardware architecture. Structured into three parts, the book covers core concepts, theories and algorithms and architecture optimization.This book provides a solution for researchers looking to maximize the performance of deep learning models on Edge-computing devices through algorithm-hardware co-design. - Focuses on hardware architecture and embedded deep learning, including neural networks - Brings together neural network algorithm and hardware design optimization approaches to deep learning, alongside real-world applications - Considers how Edge computing solves privacy, latency and power consumption concerns related to the use of the Cloud - Describes how to maximize the performance of deep learning on Edge-computing devices - Presents the latest research on neural network compression coding, deep learning algorithms, chip co-design and intelligent monitoring

Download or read book Deep Learning Convergence to Big Data Analytics written by Murad Khan and published by Springer. This book was released on 2018-12-30 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.

Download or read book Deep Belief Nets in C and Cuda C written by Timothy Masters and published by CreateSpace. This book was released on 2015-02-11 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep belief nets are one of the most exciting recent developments in artificial intelligence. The structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a 'thought process' that is capable of learning abstract concepts built from simpler primitives. A typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. This book presents the essential building blocks of the most common forms of deep belief nets. At each step the text provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the DEEP program which implements these algorithms, are available for free download from the author's website.

Download or read book Artificial Intelligence and Deep Learning Essentials written by James Russell and published by Independently Published. This book was released on 2018-05-12 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the essentials of deep learning by leveraging the power of PythonKey Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who This Book Is For Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python. Table of Contents 1. What is artificial intelligence 2. Why is the artificial intelligence important ? 3. Applications of Machine Learning 4. Semantics, Probability and IA 5. Numerical Computation 6. Sequence Modeling, Recurrent and Recursive Nets 7. Autoencoders 8. Markov Chains, Monte Carlo Methods, and Machine Learning

Download or read book An Introduction to Deep Learning written by Eugene Charniak and published by Addison-Wesley Professional. This book was released on 2018-09-28 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Neural Nets Deep Learning a Complete Guide written by Gerardus Blokdyk and published by 5starcooks. This book was released on 2018-09-18 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Does Deep Neural Nets Deep Learning analysis isolate the fundamental causes of problems? How will variation in the actual durations of each activity be dealt with to ensure that the expected Deep Neural Nets Deep Learning results are met? What would happen if Deep Neural Nets Deep Learning weren't done? What are all of our Deep Neural Nets Deep Learning domains and what do they do? How likely is the current Deep Neural Nets Deep Learning plan to come in on schedule or on budget? This powerful Deep Neural Nets Deep Learning self-assessment will make you the credible Deep Neural Nets Deep Learning domain standout by revealing just what you need to know to be fluent and ready for any Deep Neural Nets Deep Learning challenge. How do I reduce the effort in the Deep Neural Nets Deep Learning work to be done to get problems solved? How can I ensure that plans of action include every Deep Neural Nets Deep Learning task and that every Deep Neural Nets Deep Learning outcome is in place? How will I save time investigating strategic and tactical options and ensuring Deep Neural Nets Deep Learning costs are low? How can I deliver tailored Deep Neural Nets Deep Learning advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Deep Neural Nets Deep Learning essentials are covered, from every angle: the Deep Neural Nets Deep Learning self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Deep Neural Nets Deep Learning outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Deep Neural Nets Deep Learning practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Deep Neural Nets Deep Learning are maximized with professional results. Your purchase includes access details to the Deep Neural Nets Deep Learning self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard, and... - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation ...plus an extra, special, resource that helps you with project managing. INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.

Download or read book Mastering Deep Learning Fundamentals written by Ai Publishing and published by AI Publishing. This book was released on 2019-06-09 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: ** ONE HOUR FREE VIDEO COURSE IN DEEP LEARNING INCLUDED** **Get your copy now, the price will change soon**You are interested in deep learning, but don't know how to get startedLet us help youWho are the book for? Are a college student and want more than your university course offers Are you a student interested in a career in Data science? Are you a programmer who wants to make a career switch into data science and AI? Are you an engineer who wants to use new data science techniques at your current job? Are you an entrepreneur who dreams of a data science but do not yet know the basics? Are you a hobbyist who wants to build cool data science projects? Are you a data scientist practitioner and want to broaden your area of expertise? If the answer to any of the above questions is a YES, this book is for you.We have designed this book for beginners in mind and our goal is to prepare students with practical skills to solve real-world problems and to stand out in the job market.This book are not for shallow learners who simply want to copy-paste code. This book will require your time and commitment.Our book is different from other books?If you are searching for a step by step guide to learn deep learning and AI from scratch or are interested in the current updates of the AI world, our book is just the right one for you. This book paves beginners' road towards the path of deep learning concepts and algorithms without any traditional complexity of mathematical formulas.With the help of graphs and images, this books is the easiest to comprehend even by those who have no previous technological knowledge of machine learning. Moreover, our book, with its comprehensive content, prepares the readers for higher advanced courses.We strive to provide world-class data science and AI education at reasonable prices. To achieve that, we have put in a lot of planning and efforts to provide a rich learning experience for the students.What's Inside This Book? Part I: Fundamentals of Deep learning Fundamentals of Probability Fundamentals of Statistics Fundamentals of Linear Algebra Introduction to Machine Learning and Deep Learning Fundamentals of Machine Learning Fundamentals of Neural Networks and Deep Learning Deep Learning Parameters and Hyper-parameters Deep Neural Networks Layers Deep Learning Activation Functions Deep Learning Loss Functions Deep Learning Optimization Algorithms Convolutional Neural Network Recurrent Neural Networks LSTM Recursive Neural Networks Bonus Course Conclusion Part II: Deep Learning in Practice (In Jupyter notebooks) Python for Beginners Python Data Structures Python Function Object Oriented Programming in Python Best practices in Python and Zen of Python Installing Python Numpy, Pandas, Matplotlib and Scikit-learn Evaluating a model's performance Keras and Tensorflow Deep learning workstation: Jupyter Notebooks and Getting Binary Classification Building Deep Learning Model Convolutional Neural Networks in Keras Data Preparation Model Building Training and Testing Deep learning for text and sequences Brief introduction to Google Colab Data Preparation Data Wrangling and Analysis Recurrent Neural Network (RNN) ** MONEY BACK GUARANTEE BY AMAZON **If you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform or contact us (our email inside the book).