Cocalc 01 Intro Ipynb

Hello, and thank you for letting us join you on your deep learning journey, however far along that you may be! In this chapter, we will tell you a little bit more about what to expect in this book, introduce the key concepts behind deep learning, and train our first models on different tasks. It doesn't matter if you don't come from a technical or a mathematical background (though it's okay if you do too!); we wrote this book to make deep learning accessible to as many people... A lot of people assume that you need all kinds of hard-to-find stuff to get great results with deep learning, but as you'll see in this book, those people are wrong. <> is a list of a few thing you absolutely don't need to do world-class deep learning. Deep learning is a computer technique to extract and transform data–-with use cases ranging from human speech recognition to animal imagery classification–-by using multiple layers of neural networks.

Each of these layers takes its inputs from previous layers and progressively refines them. The layers are trained by algorithms that minimize their errors and improve their accuracy. In this way, the network learns to perform a specified task. We will discuss training algorithms in detail in the next section. Deep learning has power, flexibility, and simplicity. That's why we believe it should be applied across many disciplines.

These include the social and physical sciences, the arts, medicine, finance, scientific research, and many more. To give a personal example, despite having no background in medicine, Jeremy started Enlitic, a company that uses deep learning algorithms to diagnose illness and disease. Within months of starting the company, it was announced that its algorithm could identify malignant tumors more accurately than radiologists. Here's a list of some of the thousands of tasks in different areas at which deep learning, or methods heavily using deep learning, is now the best in the world: Welcome to the first optional lab! Optional labs are available to:

provide information - like this notebook reinforce lecture material with hands-on examples provide working examples of routines used in the graded labs Get a brief introduction to Jupyter notebooks From this picture we can now see some fundamental things about training a deep learning model: A model can only learn to operate on the patterns seen in the input data used to train it.

This learning approach only creates predictions, not recommended actions. It's not enough to just have examples of input data; we need labels for that data too (e.g., pictures of dogs and cats aren't enough to train a model; we need a label for... Generally speaking, we've seen that most organizations that say they don't have enough data, actually mean they don't have enough labeled data. If any organization is interested in doing something in practice with a model, then presumably they have some inputs they plan to run their model against. And presumably they've been doing that some other way for a while (e.g., manually, or with some heuristic program), so they have data from those processes! For instance, a radiology practice will almost certainly have an archive of medical scans (since they need to be able to check how their patients are progressing over time), but those scans may not...

We'll be discussing labeling approaches a lot in this book, because it's such an important issue in practice. intermediate level Python starting from a basic programming background basic knowledge in at least one object oriented programming language must know: variables, types, functions, basic I/O handling good to know: objects, classes, attributes, function arguments Official Github repository: https://github.com/tuw-python/tuw-python-2022WS

Overview 01: Linear Programming and Pyomo formulate a linear program for a given problem scenario. determine if the four assumptions of linear program are satisfied. identify corner point feasible solutions, optimal solutions, the feasible region, and feasible solutions for linear programs with either bounded or unbounded feasible regions. solve a small linear program graphically. J.R.

Johansson (jrjohansson at gmail.com) The latest version of this IPython notebook lecture is available at http://github.com/jrjohansson/scientific-python-lectures. The other notebooks in this lecture series are indexed at http://jrjohansson.github.io. Python code is usually stored in text files with the file ending ".py": Every line in a Python program file is assumed to be a Python statement, or part thereof. Python code is usually stored in text files with the file ending ".py":

Every line in a Python program file is assumed to be a Python statement, or part thereof. The only exception is comment lines, which start with the character # (optionally preceded by an arbitrary number of white-space characters, i.e., tabs or spaces). Comment lines are usually ignored by the Python interpreter. To run our Python program from the command line we use: On UNIX systems it is common to define the path to the interpreter on the first line of the program (note that this is a comment line as far as the Python interpreter is... Linear regression relates a continuous response (dependent) variable to one or more predictors (features, independent variables), using the assumption that the relationship is linear in nature:

The relationship between each feature and the response is a straight line when we keep other features constant. The slope of this line does not depend on the values of the other variables. The effects of each variable on the response are additive (but we can include new variables that represent the interaction of two variables). In other words, the model assumes that the response variable can be explained or predicted by a linear combination of the features, except for random deviations from this linear relationship.

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