Ivanbh214 / DeepLearning

Essential deep learning algorithms, concepts, examples and visualizations with TensorFlow. Popular and custom neural network architectures. Applications of neural networks.

Deep Learning

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.

Deep learning models are loosely related to information processing and communication patterns in a biological nervous system, such as neural coding that attempts to define a relationship between various stimuli and associated neuronal responses in the brain.

Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics and drug design, where they have produced results comparable to and in some cases superior to human experts.

Table of contents

• Introduction
  • TensorFlow Basics
  • Tensors, Variables and Placeholders
  • Regression
    • Linear Regression Review
    • Chirps of Crickets
    • Logistic Regression Review
  • Activation Functions
  • Model a Neural Network
• Convolutional Neural Networks (CNNs)
  • Convolutional Neural Network Review
  • Convolution on Images
  • Convolutional Neural Network Application
• Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory (LSTM) Review
  • Sequence Classification with LSTM
  • Language Modelling with LSTM
  • Character Modelling with LSTM
• Restricted Boltzmann Machines (RBMs)
  • Restricted Boltzmann Machine Review
  • Recommendation System
• Autoencoders
  • Autoencoders Review
  • Deep Belief Networks (DBNs) Review