Deep Learning Architectures 2023

Clear and simple deep learning tutorial, you will love it. Will be adding new topics on a weekly basis.

In the landscape of artificial intelligence and machine learning, deep learning stands as a revolutionary force that is shaping the future of technology. "Mastering Deep Learning" is your ultimate guide to comprehending and harnessing the potential of deep neural networks, empowering you to create intelligent solutions that drive innovation.

This reprint highlights the importance of Deep Learning (DL), which has garnered significant attention in science, industry, and academia. It draws inspiration from the functioning of the human brain and the concept of learning. Unlike traditional and machine learning methods, deep learning techniques emulate the human brain's neural networks at a lower scale, allowing them to process and analyze substantial quantities of unstructured data. The remarkable proficiency of deep learning in unveiling intricate structures within extensive datasets genuinely resembles the extraordinary aptitude of the brain to recognize patterns and form complex connections. This unique characteristic allows DL to excel in modeling and solving complex problems across various scientific and technological fields. Just as the brain learns from experience, DL architectures learn through algorithms from data by adjusting numerous parameters during training to optimize their performance and accuracy. This concept of learning and adaptation is fundamental to DL's success.

Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models such as variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy based models, and diffusion models.

Computer Vision with Python using Deep Learning for Classification, Instance & Semantic Segmentation, & Object Detection

Object Detection for Computer Vision using Deep Learning with PyTorch & Python. Train & Deploy Models (Detectron2, RCNN)

Tensorflow & Keras + FFN, CNN, RNN, LSTM, GRU, GAN, Autoencoders, Transfer Learning, Data Augmentation, Text/Image Model