Blood pressure (BP) is a vital sign of the human body and an important parameter for early detection of cardiovascular diseases. It is usually measured using cuff-based devices or monitored invasively in critically-ill patients. This paper presents two techniques that enable continuous and noninvasive cuff-less BP estimation using photoplethysmography (PPG) signals with Convolutional Neural Networks (CNNs). The first technique is calibration-free. The second technique achieves a more accurate measurement by estimating BP changes with respect to a patient's PPG and ground truth BP values at calibration time. For this purpose, it uses Siamese network architecture. When trained and tested on the MIMIC-II database, it achieves mean absolute difference in the systolic and diastolic BP of 5.95 mmHg and 3.41 mmHg respectively. These results almost comply with the AAMI recommendation and are as accurate as the values estimated by many home BP measuring devices.