TensorGo

Well, topic is techno-ish, isn't it? I've been involved in training people within the company to apply deep learning in their work. It is truly delightful to see talented people quickly grasp the concepts and tools and proceed to create very nice applications quite soon afterwards. There is something special about deep learning which adds spark to the eyes of the cognoscenti. Having done my Ph.D. in computational physics, I was at first surprised by the attitude about this particular class of algorithms, both that of others as well as my own. I remember feeling child-like joy seeing a sequence model producing a good time series prediction for the first time. I also remember feeling rather less enthusiastic about some other algorithms during the Ph.D. research. Way back, if I may add. There are t heoretical reasons why deep learning is a special case. Most likely also the general enthusiasm about the discipline adds to one's excitement. Tooling such as Spyder IDE and

Machine learning has many new aspects that need to be understood by the society since it is expected to be widely used in the society. A variant of machine learning called supervised learning is used for applications like object detection in pictures as well as for classification. Let's take a quick look at some of the basic concepts below. Model Model defines how input data is processed. Depending on the use case, input data may consist of digital pictures, digitized sounds, or numerical data, for example. The model consists of mathematical operations which transform the input data into desired output (e.g. classification "cat" / "dog" in picture). There are many algorithms which can be used for the model, ranging from simple mathematical formulae to fashionable deep learning networks. A model is basically neutral and generic, so that an individual model may be used for classifying cats vs. dogs on the one hand, or types of trees on the other. For dema