introduction to machine learning and deep learning although these advancements are peripheral to deep learningthey have played big role in enabling advances in deep learning rerequisites the key prerequisites for reading this book include working knowledge of python and some coursework in linear algebracalculusand probability readers should refer to the following in case they need to cover these prerequisites dive into pythonby mark pilgrim apress publications ( introduction to linear algebra (fifth edition)by gilbert strang wellesley-cambridge press calculusby gilbert strang wellesley-cambridge press all of statistics (section - )by larry wasserman springer ( the approach ahead this book focuses on the key concepts of deep learning and its practical implementation using pytorch in order to use pytorchyou should possess basic understanding of python programming introduces pytorchand the subsequent discuss additional important constructs within pytorch before delving into deep learningwe need to discuss the basic constructs of machine learning in the remainder of this we will explore the baby steps of machine learning with dummy example to implement the constructswe will use python and again implement the same using pytorch