This course is designed to assist you with learning the capacities, difficulties, and outcomes of deep learning and set you up to take part in the improvement of driving edge AI innovation. In this Specialization, you will fabricate neural organization structures like Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and figure out how to improve them with methodologies like Dropout, BatchNorm, Xavier/He instatement, and that’s just the beginning. You will dominate these theoretical ideas and their industry applications utilizing Python and TensorFlow. You will handle genuine contextual investigations, for example, self-governing driving, gesture-based communication perusing, music age, PC vision, discourse acknowledgment, and characteristic language preparing.
Upon completion of the program and final exam, students will be presented with a Certificate of Completion that can be uploaded from the course.
What you’ll learn in this course:
- Build and train deep neural organizations, execute vectorized neural organizations, distinguish critical boundaries in engineering, and apply deep figuring out how to your applications
- Use the prescribed procedures to prepare and create test sets and break down inclination/change for building DL applications, utilize standard neural organization methods, apply advancement calculations, and execute a neural organization in TensorFlow
- Diagnose and use procedures for diminishing mistakes in ML frameworks, comprehend complex ML settings, and apply start to finish learning, move to learn, and perform multiple tasks learning
- Build a CNN, apply it to visual location and acknowledgment errands, utilize neural style move to create artistry, and apply these calculations to picture video, and other 2D/3D data
- Build and train RNNs, GRUs, and LSTMs, apply RNNs to character-level language demonstrating, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform NER and Question Answering