Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges. Key Features Learn the fundamentals of Convolutional Neural Networks Harness Python and Tensorflow to train CNNs Build scalable deep learning models that can process millions of items Book Description Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. What you will learn Train machine learning models with TensorFlow Create systems that can evolve and scale during their life cycle Use CNNs in image recognition and classification Use TensorFlow for building deep learning models Train popular deep learning models Fine-tune a neural network to improve the quality of results with transfer learning Build TensorFlow models that can scale to large datasets and systems Who this book is for This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.
Do you have questions about getting orthodontic treatment for yourself or your child? Smiling For Success is a consumer's guide to orthodontics. It goes through all the reasons to get braces or Invisalign and what you can expect when you get started. It answers many of the most frequently asked questions such as "why is it important to have straight teeth" and "what are the signs my young child needs braces?" Many people don't realize the effect of a smile on self-confidence and self-esteem. This book explores all the psychological, dental, and health benefits of having a sensational smile.
This book reports innovations in the preclinical study of stroke, including - novel tools and findings in animal models of stroke, - novel biochemical mechanisms through which ischemic damage may be both generated and limited, - novel pathways to neuroprotection. Although hypothermia has been so far the sole "neuroprotection" treatment that has survived the translation from preclinical to clinical studies, progress in both preclinical studies and in the design of clinical trials will hopefully provide more and better treatments for ischemic stroke. This book aims at providing the preclinical scientist with innovative knowledge and tools to investigate novel mechanisms of, and treatments for, ischemic brain damage.
Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges. Key Features Learn the fundamentals of Convolutional Neural Networks Harness Python and Tensorflow to train CNNs Build scalable deep learning models that can process millions of items Book Description Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. What you will learn Train machine learning models with TensorFlow Create systems that can evolve and scale during their life cycle Use CNNs in image recognition and classification Use TensorFlow for building deep learning models Train popular deep learning models Fine-tune a neural network to improve the quality of results with transfer learning Build TensorFlow models that can scale to large datasets and systems Who this book is for This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.
This book focuses on various aspects of nanotechnology based gene silencing. The initial chapters detail the techniques available for in vitro and in vivo characterization of nanoparticles. In the later chapters, exhaustive details about various polymeric systems employed for gene silencing has been accounted.
While there have been a large number of ongoing research projects and publications on the treatment of cancer, there is a limited number of books related to nanotechnology with emphasis on polymeric nanoparticles in cancer therapy. The book, covering all of the recent research work done on this basis, enlightens researchers and students with the benefits of polymeric nanomaterials in cancer. This book provides exhaustive knowledge on polymeric nanoparticles and cancer. Provides a complete overview of the application of different polymeric nanoparticles in cancer therapeutics: from characterization of nanoparticles to in vitro and in vivo studies. Contains a detailed description of the delivery of therapeutic molecules with different polymers realized with nanomedicine in cancer therapeutics. Discusses newly developed promising polymers in cancer. Describes polymeric nanoparticle and their role in theranostics pertaining to cancer. Reviews clinical relevance of different polymeric formulations in the field of cancer
Advances in Nanomedicine for the Delivery of Therapeutic Nucleic Acids addresses several issues related to safe and effective delivery of nucleic acids (NAs) using nanoparticles. A further emphasis would be laid on the mechanism of delivery of NAs, the barriers encountered and the strategies adapted to combat them. An exhaustive account of the advantages as well shortcomings of all the delivery vectors being employed in delivery of various NAs will be provided. On final note the regulatory aspects of nanoparticles mediated NA would be discussed, with focus on their clinical relevance. The design and development of nucleic acid-based therapeutics for the treatment of diseases arising from genetic abnormalities has made significant progress over the past few years. NAs have been widely explored for the treatment of cancer and infectious diseases or to block cell proliferation and thereby caused diseases. Advances in synthetic oligonucleotide chemistry resulted in synthesis of NAs that are relatively stable in in vivo environments. However, cellular targeting and intracellular delivery of NAs still remains a challenge. Further development of NA-based therapeutics depends on the progress of safe and effective carriers for systemic administration. Nanomedicine has facilitated availability of vectors with diminished cytotoxicity and enhanced efficacy which are rapidly emerging as systems of choice. These vectors protect NAs from enzymatic degradation by forming condensed complexes along with targeted tissue and cellular delivery. During the past few years, a myriad reports have appeared reporting delivery of NAs mediated by nanoparticles. This book will provide an overview of nanoparticles being employed in the in vitro and in vivo delivery of therapeutically relevant NAs like DNA, siRNA, LNA, PNA, etc. Provides a complete overview of the applicatiosn of nanomedicine in the delivery of nucleic acids, from characterization of nanoparticles, to in vitro and in vivo studies Discusses delivery issues of less well explored nucleic acids, like PNAs, Ribozymes, DNAzymes, etc. Summarizes the current state of research in nucleic acid delivery and underscores the future of nanomedicine in this field
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