machine learning with tensorflow book

Installation use Official Document Connection: Https://www.tensorflow.org/get_started/get_started_for_beginnersIn accordance with the text of the GitHub connection to download files directly GG, Hung ladder or clone do not move, helpless, had to go to that page to use the example of the py file copy came to the local, need to copy two files: https://github.com/tensorflow/models/tree/master/samples/core/get_started/iris_data.py https://github.com/

In the previous article we brought out the network structure of Googlenet InceptionV1, in this article we will detail inception V2/V3/V4 's development process and their network structure and highlights.Googlenet Inception V2Googlenet Inception V2 in "Batch normalization:accelerating deep Network Training by reducing Internal covariate Shift" appears, the largest The highlight is the batch normalization method, which plays the following role: use larger

Introduction of Style migration Style Transfer is one of the most interesting applications of deep learning, as shown in this way, we can use this method to "migrate" the style of a picture to another picture: However, the speed of the original style migration (click to view the paper) is very slow. On the GPU, it takes about 10 minutes to generate a picture, and it may take several hours if you use only the CPU without using the GPU to run the progr

Model optimization is important for both traditional machine learning and deep learning, especially in deep learning, and it is likely that more difficult challenges will need to be addressed during training. At present, the popular and widely used optimization algorithm has a random gradient descent, with the momentum

1. Google Cloud Machine learning Platform Introduction:The three elements of machine learning are data sources, computing resources, and models. Google has a strong support in these three areas: Google not only has a rich variety of data resources, but also has a strong computer group to provide data storage in the dat

Machine learning and its application 2013 content introduction BooksComputer BooksMachine learning is a very important area of research in computer science and artificial intelligence. In recent years, machine learning has not only been a great skill in many fields of comput

Awesome series Awesome Machine Learning Awesome Deep Learning Awesome TensorFlow Awesome TensorFlow implementations Awesome Torch Awesome Computer Vision Awesome Deep Vision Awesome RNN Awesome NLP Awesome AI Awesome Deep

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us int

learning to organize the daily learning of machine learning algorithms, and practical problems, do more experiments, and strive to get a better learning effect, I will be firm belief, more efforts to catch up with the pace of excellence.Reprint please indicate the author Ja

wrong classification point is not, then the value of the loss function is definitely 0.The Perceptual machine learning algorithm is driven by mis-classification and adopts random gradient descent method. First, arbitrarily select a super-planar w,b and then minimize the target function. The definitions are given in the author's book. Not a wordy.The original for

First, let's talk about gossip. 　If you go to machine learning now, will you go? Is it because you are not interested in this aspect, or because you think this thing is too difficult, you will not learn? If you feel too difficult, very good, believe that after reading this article, you will have the courage to step into the field of machine

Learning notes for "Machine Learning Practice": Implementation of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- The main learning and research tasks of the last semester were pattern recognition, signal theor