This is outline for nanodegree

 Deep Learning - Nanodegree Program

 “Deep Learning - Nanodegree Program” you will build foundational skills in deep learning by designing and training neural networks to solve complex real-world problems. You’ll begin with the essentials of neural networks, advancing to specialized architectures like Convolutional and Recurrent Neural Networks, along with Generative Adversarial Networks. Through projects, create models for applications such as image classification, sentiment analysis, and face generation, gaining hands-on experience with PyTorch and advanced training techniques. Ideal for those aiming to harness the potential of deep learning, this experience prepares you to tackle AI challenges across various domains. Gain highly marketable skills involving: 1) Introduction to Deep Learning - Neural networks • Deep learning • Gradient descent • Overfitting prevention • scikit-learn • Deep learning tools • Model performance metrics • Perceptron • PyTorch • Advanced probability • Training neural networks, 2) Convolutional Neural Networks - Image pre-processing • Image segmentation • Neural network initialization • Bounding boxes • Semantic image segmentation • Convolutional kernels • Training neural networks • U-net • Convolutional neural networks • Autoencoders • Image classification • Transfer learning • Model training • Object detection • PyTorch, 3) RNNs and Transformers - Training neural networks • Backpropagation • NLP transformers • Hyperparameter tuning • Gpt3 • Recurrent neural networks • Bert • Long-short term memory networks • Neural networks • Word2vec, and 4) Building Generative Adversarial Networks - Generative adversarial networks • Model evaluation • Deep learning techniques • Markov games • Deep learning model optimization • Hyperparameter tuning • Cyclegan • Image generation.