Deep learning improves performance in tasks like image and speech recognition through its ability to automatically learn hierarchical representations of data. In image recognition, deep learning models can learn to automatically extract features at different levels of abstraction, allowing them to discern intricate patterns within images. This enables the models to recognize objects, shapes, and textures with greater accuracy than traditional machine learning methods.

Similarly, in speech recognition, deep learning models can analyze audio data at various levels of abstraction, learning to recognize phonemes, words, and language patterns. This hierarchical representation learning allows deep learning models to improve performance in speech recognition tasks by capturing complex relationships within the audio data.

Overall, the capability of deep learning models to automatically learn intricate features and representations from raw data plays a crucial role in enhancing performance in image and speech recognition tasks.