This speciallization course contains 3 other courses:

• Build Basic Generative Adversarial Networks (GANs). The course covers the below topics:
  • Building basic GANs using PyTorch framework.
  • Building Deep Convolutional GAN (DCGAN).
  • Different Loss Functions (BCE loss, Wasserstein Loss)
  • W-GAN and SN-GAN
  • Conditional and Controllable Generation
• Build Better Generative Adversarial Networks (GANs). The course covers the below topics:
  • Evaluation Methods.
  • Bias and Fairness.
  • StyleGAN
• Apply Generative Adversarial Networks (GANs). The course covers the below topics:
  • Using GANs for Data Augmentation and Privacy.
  • Image-to-Image Translation (U-Net and Pix2Pix).
  • CycleGAN
  • StyleGAN

This speciallization course contains 4 other courses:

• Natural Language Processing with Classification and Vector Spaces.
  • Perform sentiment analysis of tweets using logistic regression and then naïve Bayes,
  • Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships.
  • Write a simple English to French translation algorithm using pre-computed word embeddings and locality-sensitive hashing to relate words via approximate k-nearest neighbor search.
• Natural Language Processing with Probabilistic Models.
  • Create a simple auto-correct algorithm using minimum edit distance and dynamic programming.
  • Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics.
  • Write a better auto-complete algorithm using an N-gram language model.
  • Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model.
• Natural Language Processing with Sequence Models.
  • Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets.
  • Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model.
  • Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers.
  • Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning.
• Natural Language Processing with Attention Models.
  • Translate complete English sentences into German using an encoder-decoder attention model.
  • Build a Transformer model to summarize text.
  • Use T5 and BERT models to perform question-answering.
  • Getting familiar with Hugging Face, load it’s datasets, and utilize the pre-trained models and pipelines.

This speciallization course contains 5 other courses:

• Neural Networks and Deep Learning.
  • Utilize Numpy to interact with vector operations.
  • Build, train, and use fully connected deep neural networks from scratch efficiently.
  • Identify important parameters within a neural network’s architecture.
  • Apply deep learning to solve real-world problems. This course is considered the first level in a set of 5 courses for the Deep Learning Specialization track offered by DeepLearning.AI.
• Improving Deep Neural Networks; Hyperparameter Tuning, Regularization and Optimization.
  • the best practices to train/develop test sets and analyze bias/variance for building DL applications.
  • use standard NN techniques such as initialization, L2 and dropout regularization, hyper-parameter tuning, and batch normalization.
  • implement and apply a mini-batch gradient descent, Momentum, RMSprop and Adam optimizers.
• Structuring Machine Learning Projects. Learn to split data for training, dev, and test, tailored for small and large datasets. Setting up a single evaluation metric using various metrics and understand the difference between satisfactory metrics and optimization metrics, diagnose errors, prioritize error-reduction strategies ,and understand complex ML settings. Apply end-to-end learning, transfer learning, and multi-task learning.
• Convolutional Neural Networks (CNN).
  • Edge dectection, Transfer learning and pre-trained models (ResNet,MobileNet,Inception) and data Augmentation.
  • Object detection and segmentation.
  • computer vision applications (autonomous driving, face recognition, reading radiology images, and more.
  • Neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.
• Sequence Models.
  • Recurrent Neural Networks (RNNs).
  • Natural Language Processing (NLP) & Word Embeddings.
  • Sequence Models & Attention Mechanism.
  • Transformer Network.