Become an authority on machine learning. Take advantage of AI by studying the foundations of deep learning. Recently upgraded using state-of-the-art methods!
The Deep Learning Specialization is a comprehensive program that aims to equip you with the necessary knowledge and skills to understand and contribute to the advancement of cutting-edge AI technology. Throughout this specialization, you will have the opportunity to construct and train various neural network architectures, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Transformers. Additionally, you will learn techniques such as Dropout, BatchNorm, Xavier/He initialization, and more, to enhance the performance of these networks. Theoretical concepts will be thoroughly covered, and you will gain practical experience by utilizing Python and TensorFlow to tackle real-world applications such as speech recognition, music synthesis, chatbots, machine translation, and natural language processing.
The impact of AI on various industries is undeniable, and by completing the Deep Learning Specialization, you will position yourself to excel in this rapidly evolving field. Not only will you acquire the necessary expertise, but you will also receive valuable career guidance from industry and academic experts in deep learning.
Furthermore, the specialization includes an Applied Learning Project that will enable you to:
- Construct and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply deep learning to your own applications.
- Utilize best practices for training and developing test sets, analyze bias/variance in building deep learning applications, employ standard neural network techniques, implement optimization algorithms, and utilize TensorFlow to create neural networks.
- Employ strategies to reduce errors in machine learning systems, comprehend complex machine learning scenarios, and apply end-to-end, transfer, and multi-task learning.
- Develop a Convolutional Neural Network, apply it to visual detection and recognition tasks, utilize neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data.
- Construct and train recurrent neural networks (RNNs) and its offshoots (GRUs, LSTMs), utilize RNNs for character-level language modeling, collaborate with natural language processing (NLP) and word embeddings, and carry out Named Entity Recognition and Question Answering using HuggingFace tokenizers and transformers.
By the end of this specialization, you will possess the necessary skills to excel in the field of deep learning and contribute to the development of AI technology.
What You’ll Learn
- Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications
- Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow
- Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data
- Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering
