List the concepts that should be learned before approaching the given complex concept. Deep Learning

Before approaching the concept of deep learning , it is important to have a basic understanding of the following concepts : \n \n 1 . Machine Learning : Deep learning is a subset of machine learning , which is the process of teaching computers to learn from data and make predictions or decisions without being explicitly program med . \n 2 . Art ificial Ne ural Network s : Deep learning rel ies on artificial neural networks , which are comput ational models inspired by the structure and function of the human brain . \n 3 . Super vised Learning : Deep learning typically involves super vised learning , where the algorithm is trained on labeled data to make predictions or class ifications . \n 4 . Un super vised Learning : Deep learning can also involve un super vised learning , where the algorithm is trained on un label ed data to discover patterns or relationships . \n 5 . Deep Learning Architect ure : Deep learning algorithms consist of multiple layers of inter connected nodes , each performing a simple computation and passing the output to the next layer . \n 6 . Con v olut ional Ne ural Network s ( C NN s ): CNN s are a type of deep learning architecture commonly used for image recognition and classification tasks . \n 7 . Rec ur rent Ne ural Network s ( R NN s ): R NN s are another type of deep learning architecture commonly used for natural language processing and time - series analysis tasks . \n 8 . Back prop agation : Back prop agation is an algorithm used to train deep learning models by adjust ing the weights of the connections between nodes based on the error of the model ' s predictions . \n 9 . Tensor Flow and Py Tor ch : Tensor Flow and Py Tor ch are popular deep learning framework s used for building and training deep learning models . </s>