The Advanced Guide to Deep Learning and Artificial Intelligence

Have you been introduced to deep learning and artificial intelligence and want to learn more? Do you want to go deeper into learning about Python? Then you need the Advanced Guide to Deep Learning and Artificial Intelligence. This bundle covers deep learning in neural networks, autoencoders, speech recognition, and natural language processing.

The following four courses are included in this bundle.

Deep Learning: Convolutional Neural Networks in Python – Take a look at the concepts behind computer vision and expand on what you know about neural networks and deep learning.

  • Twenty-five lectures and three hours of content
  • Use convolutional neural networks (CNNs) to explore the StreetView House Number (SVHN) dataset
  • Create convolutional filters to be applied to audio or imaging
  • Grow deep neural networks with just a few functions
  • Test CNNs written in both Theano and TensorFlow

Unsupervised Deep Learning in Python – Learn about the power of autoencoders and restricted Boltzmann machines and discuss principal components analysis and a popular nonlinear dimensionality reduction technique and then learn about autoencoders.

  • Thirty lectures and three hours of content
  • Discover restricted Boltzmann machines (RBMs) and how to pre-train supervised deep neural networks
  • Learn about Gibbs sampling
  • Use PCA and t-SNE on features that were learned by autoencoders and RBMs
  • Learn modern deep learning developments


Deep Learning: Recurrent Neural Networks in Python – Learn about futuristic sciences such as the artificial science behind speech recognition.

  • Thirty-two lectures and four hours of content
  • Discover the Simple Recurrent Unit, aka the Elman unit
  • Extend the XOR problem as a parity problem
  • Learn language modeling
  • Become adept at Word2Vec to create word vectors or word embeddings
  • Examine the long short-term memory unit (LSTM) and gated recurrent unit (GRU)
  • Use what you learn on practical problems such as learning a language model from Wikipedia data

Natural Language Processing with Deep Learning in Python – Explore advanced natural language processing and learn about deriving and implementing Word2Vec, GloVe, word embeddings and sentiment analysis.

  • Forty lectures and four-and-a-half hours of content
  • Explore Word2Vec and learn how it maps words to a vector space
  • Learn about GLoVe’s use of matrix factorization and how it contributes to recommendation systems
  • Discover recursive neural networks to help solve the problem of negation in sentiment analysis

Get this deep learning bundle at 91% off for just $42.

The Advanced Guide to Deep Learning and Artificial Intelligence

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