In this course, intended to expand upon your knowledge of neural networks and deep learning, you’ll harness these concepts for computer vision using convolutional neural networks. Going in-depth on the concept of convolution, you’ll discover its wide range of applications, from generating image effects to modeling artificial organs.
- Access 25 lectures & 3 hours of content 24/7
- Explore the StreetView House Number (SVHN) dataset using convolutional neural networks (CNNs)
- Build convolutional filters that can be applied to audio or imaging
- Extend deep neural networks w/ just a few functions
- Test CNNs written in both Theano & TensorFlow
Note: we strongly recommend taking The Deep Learning & Artificial Intelligence Introductory Bundle before this course.
In this course, you’ll dig deep into deep learning, discussing principal components analysis and a popular nonlinear dimensionality reduction technique known as t-distributed stochastic neighbor embedding (t-SNE). From there you’ll learn about a special type of unsupervised neural network called the autoencoder, understanding how to link many together to get a better performance out of deep neural networks.
- Access 30 lectures & 3 hours of content 24/7
- Discuss restricted Boltzmann machines (RBMs) & how to pretrain supervised deep neural networks
- Learn about Gibbs sampling
- Use PCA & t-SNE on features learned by autoencoders & RBMs
- Understand the most modern deep learning developments
A recurrent neural network is a class of artificial neural network where connections form a directed cycle, using their internal memory to process arbitrary sequences of inputs. This makes them capable of tasks like handwriting and speech recognition. In this course, you’ll explore this extremely expressive facet of deep learning and get up to speed on this revolutionary new advance.
- Access 32 lectures & 4 hours of content 24/7
- Get introduced to the Simple Recurrent Unit, also known as the Elman unit
- Extend the XOR problem as a parity problem
- Explore language modeling
- Learn Word2Vec to create word vectors or word embeddings
- Look at the long short-term memory unit (LSTM), & gated recurrent unit (GRU)
- Apply what you learn to practical problems like learning a language model from Wikipedia data
In this course you’ll explore advanced natural language processing – the field of computer science and AI that concerns interactions between computer and human languages. Over the course you’ll learn four new NLP architectures and explore classic NLP problems like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. By course’s end, you’ll have a firm grasp on natural language processing and its many applications.
- Access 40 lectures & 4.5 hours of content 24/7
- Discover Word2Vec & how it maps words to a vector space
- Explore GLoVe’s use of matrix factorization & how it contributes to recommendation systems
- Learn about recursive neural networks which will help solve the problem of negation in sentiment analysis