Starting in deep learning research requires understanding fundamental concepts, such as neural networks, optimization, and backpropagation. Learn frameworks like TensorFlow or PyTorch, which are commonly used for experimentation.
Choose a specific area of interest, like computer vision, NLP, or generative models, and study related research papers on platforms like arXiv. Reimplement existing papers to understand methods and build a foundation for novel contributions.
Collaborate with academic or industry mentors and participate in challenges, such as Kaggle or NeurIPS competitions, to gain exposure. Staying updated with the latest advancements through conferences and workshops is also crucial for progress.