LEADER 03339nam 2200493 450 001 9910427053403321 005 20220127163434.0 010 $a1-4842-5967-X 024 7 $a10.1007/978-1-4842-5967-2 035 $a(CKB)4100000011469552 035 $a(MiAaPQ)EBC6355618 035 $a(DE-He213)978-1-4842-5967-2 035 $a(CaSebORM)9781484259672 035 $a(PPN)250223678 035 $a(EXLCZ)994100000011469552 100 $a20210219d2020 uy 0 101 0 $aeng 135 $aurcn####||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine learning concepts with Python and the Jupyter Notebook environment $eusing Tensorflow 2.0 /$fNikita Silaparasetty 205 $a1st ed. 2020. 210 1$a[Place of publication not identified] :$cApress,$d[2020] 210 4$dİ2020 215 $a1 online resource (301 pages) 311 $a1-4842-5966-1 327 $aChapter 1: An Overview of Artificial Intelligence -- Chapter 2: An Overview of Machine Learning -- Chapter 3: Introduction to Deep Learning -- Chapter 4: Machine Learning Versus Deep Learning -- Chapter 5: Machine Learning with Python -- Chapter 6: Introduction to Jupyter Notebooks -- Chapter 7: Python Programming on the Jupyter Notebook -- Chapter 8: The Tensorflow Machine Learning Library -- Chapter 9: Programming with Tensorflow 1.0 -- Chapter 10: Introducing TensorFlow 2.0 -- Chapter 11: Machine Learning Programming with TensorFlow 2.0. 330 $aUnderstand the fundamental concepts of machine learning with Python and TensorFlow 2.0, within the Jupyter Notebook environment. Even if you?re an absolute beginner, develop a strong understanding of the crucial ideas without feeling intimidated by the immensity of the sector. Start with a gentle introduction to artificial intelligence and machine learning to understand how the field has grown over the years and why it is still relevant. Then learn how the notebook interface has become increasingly popular for writing code?with Jupyter Notebook being preferred to a regular text editor or IDE. Once these topics have been covered, you?ll dive into the TensorFlow 2.0 library. Obtain a good understanding of what TensorFlow is, and how it has improved from its initial release. You?ll be able to compare the two versions in a theoretical as well as practical way, and you?ll go through the procedure required to convert code from TensorFlow 1.0 to TensorFlow 2.0. Finally, you will work through projects that use TensorFlow 2.0 with Python and the Jupyter Notebook to help build your own neural networks for deep learning. This will enable you to put everything that you have learned from the book into practice. Each project is given in a step-by-step format for better comprehension. 606 $aTensorFlow 606 $aMachine learning 606 $aPython (Computer program language) 615 0$aTensorFlow. 615 0$aMachine learning. 615 0$aPython (Computer program language) 676 $a006.31 700 $aSilaparasetty$b Nikita$0989769 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910427053403321 996 $aMachine learning concepts with Python and the Jupyter Notebook environment$92263900 997 $aUNINA