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MTG NCERT physics 11th n 12th

MTG NCERT physics 11th n 12th

₹500

1. Best-selling study guide and well-structured study resource for NEET, AIIMS, JIPMER. 2. NEET Objective Physics Vol 1. – for class 11 3. The book follows the NCERT pattern for MBBS & BDS entrance preparation along with their school studies. 4. Diagrams, tables, figures etc support theory 5. Practice exercises after every chapter 6. Coverage of last 8 Years Questions of NEET, CBSEE AIPMT and Other Medical Entrances. The “NEET Objective Physics Volume – 01” is a complete comprehensive book designed for the medical students preparing for NEET. As the title suggests the volume -1 covers the complete NEET syllabus along with NCERT Textbook of class 11th into 17 Chapters for the simultaneous preparation of both school & exam. Every chapter is well supported by theories, diagrams, tables, figures. Important points and Notes are given in the topics to enrich students. In order to help, Check Point Exercises are given in between the text of all chapters to make students linked with the topic. Solved Examples are given with the different concepts of chapters to make students learn the problem solving skills. Exercises provided in the chapters are divided into 3 parts. Part – A: Taking it Together deals with objective questions arranged according to level of difficulty for the systematic practice. Part – B: Medical Entrance Special Format Questions – covers all special types of questions, generally asked in NEET & other Medical Entrances, Part – C: Medical Entrances’ Gallery – asked questions in Last 10 years’ (2020-2011) in NEET and other medical entrances. TOC Basic Mathematics, Units, Dimensions and Error Analysis, Vectors, Motion in One Dimension, Motion in a Plane and Projectile Motion, Laws of Motion, Work, Power and Energy, Circulation Motion, Rotation, Gravitation, Simple Harmonic Motion, Elasticity, Fluid Mechanics, Thermometry, Thermal Expansion and Kinetic Theory of Gases, Laws of Thermodynamics, Calorimetry and Heat Transfer, Wave Motion.

1 year ago
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Digital design by john f wakerly

Digital design by john f wakerly

₹450

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features Build a strong foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniques and their applications across computer vision and NLP Learn how a computer can navigate in complex environments with reinforcement learning Book Description With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you'll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You'll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You'll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you'll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications. What you will learn Grasp the mathematical theory behind neural networks and deep learning processes Investigate and resolve computer vision challenges using convolutional networks and capsule networks Solve generative tasks using variational autoencoders and Generative Adversarial Networks Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention mode

1 year ago
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