Deep learning coursera github 






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Therefore, we can say that Deep Learning is: 1. Because object detection is so important for autonomous driving I really enjoyed learning, step-by-step. By analyzing the comutation graph, we can easily compute all deviatives.
Another popular choice of activation function:. A programming framework allows you to code up deep learning algorithms with typically fewer lines of code than a lower-level language such as Python. Think of anchor boxes as the shape categories for different objects.
Question 10 You find that a team of ornithologists debating and discussing an image gets an even better 0. Needing two weeks to train will limit the speed at which you can iterate. Hard core math, but great if you love theory and want to do research etc. Question 11 You also evaluate your model on the test set, and find the following: Human-level performance 0. Get a bigger training set to reduce variance. Other topics will be needed, but are not part of the pre-requisites, so I will devote an appropriate amount of lecture time to them. I think this is the main reason why both these courses can be taken without an in-depth knowledge of calculus and algebra, you can get a pretty good high-level understanding of the concepts and algorithms.
Deep Learning on Azure - Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. If you don't yet have this background, then I think it's good to spend a bit more time on the basics.
The course is taught by Andrew Ng. The note combines knowledge from course and some of my understanding of these konwledge. In this note, I will keep all functions and equations vectorized without for loop as far as possible. Stuctures of Deep Learning We start with supervised learning. Firstly, deep learning models performs better when deep learning coursera github with big data. Here is a comparation of deep learning models and classic machine learing models: Comparation between deep learning and machine learning Secondly, thanks to the booming development of hardware and advanced algorithm, computing is much faster than before. Thus, we can implement our idea and know whether it works or not in short time. As a result, we can run the following circle much faster than we image. Output y Neural Network Reviewing the whole course, there are several common concepts between logistic regression and neural network including both shallow and deep neural network. Thus, I draw conclusions on each concept and then apply them to both logistic regression and neural network. Logistic Regression and Neural Network First of all, here are pictures of logistic regression and neural network. Neural Network As we can see, logistic regression is also a kind of neural network, which has input layer and output layer and does not have hidden layers, so that it is also called mini neural network. Computation Graph Computation graph is one of basic concepts in deep learning. By analyzing it, we could understand the whole process of computation process of neural network. This process is called computing derivatives. By analyzing the comutation graph, we can easily compute all deviatives. Forward Propagation Computation on single neuron Computation on single neuron For every single neuron, the computing process is the same as the logistic regression. Logistic regression is basically the combination of linear regression and logistic function such as sigmoid. The forward propagation process means that we compute the graph from left to the right in this picture. It is called activation function. For each layer, it just repeats what previous layers do until the last layer output layer. Cost function Here is a definition of loss function and cost function. Compute Gradients Backward Propagation As we can see in the picture, it is a simplified computation graph. The neural network is on the right-top, which is almost the same as the neural network we discussing in previous section. Backward Propagation is computing derivatives from the right to the left. Update parameters After computing deep learning coursera github, we can update our parameters quickly. Activation Functions In the course, Prof. Andrew Ng only introduces the first four activation functions. In other case, you should not use it. Parameters and Hyperparameters Comparation of shallow and deep neural network.