Neural Networks for Machine Learning (Coursera)

by Deep Learning

Lecture 1-1. Why do we need Machine Learning? Deep Learning

Lecture 1-2. What are Neural Networks? Deep Learning

Lecture 1-3. Some simple models of neurons Deep Learning

Lecture 1-4. A simple example of learning Deep Learning

Lecture 1-5. Three types of learning Deep Learning

Lecture 2-1. Types of neural network architectures Deep Learning

Lecture 2-2. Perceptions: The first generation of neural networks Deep Learning

Lecture 2-3. A geometrical view of perceptrons Deep Learning

Lecture 2-4. Why the learning works Deep Learning

Lecture 2-5. Whar perceptrons can't do Deep Learning

Lecture 3-1. Learning the weights of a linear neuron Deep Learning

Lecture 3-2. The error surface for a linear neuron Deep Learning

Lecture 3-3. Learning the weights of a logistic output neuron Deep Learning

Lecture 3-4. The backpropagation algorithm Deep Learning

Lecture 3-5. Using the derivatives computed by backpropagation Deep Learning

Lecture 4-1. Learning to predict the next word Deep Learning

Lecture 4-2. A brief diversion into cognitive science Deep Learning

Lecture 4-3. Another diversion: The softmax output function Deep Learning

Lecture 4-4. Neuro-probabilistic language models Deep Learning

Lecture 4-5. Way to deal with the large number of possible outputs Deep Learning

Lecture 5-1. Why object recognition is difficult Deep Learning

Lecture 5-2. Achieving viewpoint invariance Deep Learning

Lecture 5-3. Convolutional nets for digit recognition Deep Learning

Lecture 5-4. Convolutional nets for object recognition Deep Learning

Lecture 6-1. Overview of mini-batch gradient descent Deep Learning

Lecture 6-2. A bag of tricks for mini-batch gradient descent Deep Learning

Lecture 6-3. The momentum method Deep Learning

Lecture 6-4. Adaptive learning rates for each connection Deep Learning

Lecture 6-5. Rmsprop: Divide the gradient by a running average of its recent magnitude Deep Learning

Lecture 7-1. Modeling sequences: A brief overview Deep Learning

Lecture 7-2. Training RNNs with back propagation Deep Learning

Lecture 7-3. A toy example of training an RNN Deep Learning

Lecture 7-4. Why it is difficult to train an RNN Deep Learning

Lecture 7-5. Long-term Short-term-memory Deep Learning

Lecture 8-1. A brief overview of Hessian Free optimization Deep Learning

Lecture 8-2. Modeling character strings with multiplicative connections Deep Learning

Lecture 8-3. Learning to predict the next character using HF Deep Learning

Lecture 8-4. Echo State Networks Deep Learning

Lecture 9-1. Overview of ways to improve generalization Deep Learning

Lecture 9-2. Limiting the size of the weights Deep Learning

Lecture 9-3. Using noize as a regularizer Deep Learning

Lecture 9-4. Introduction to the full Bayesian approach Deep Learning

Lecture 9-5. The Bayesian interpretation of weight decay Deep Learning

Lecture 9-6. MacKay's quick and dirty method of setting weight costs Deep Learning

Lecture 10-1. Why it helps to combine models Deep Learning

Lecture 10-2. Mixtures of Experts Deep Learning

Lecture 10-3. The idea of full Bayesian learning Deep Learning

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