Ways of coping come in all shapes and sizes. Some examples of coping skills and strategies include distraction, self-soothing strategies, mindfulness activities, and using positive self-statements. Remaining connected to family and friends is another way to cope with stressful experiences. Due to COVID, it may be more challenging to meaningfully connect with family and friends, so we must be creative!
Thursday, March 5, Family and Friends All series. Family and Friends. The free version cannot be published In public for along time. Post a response to report a link that does not work. Post a response for a free copy via email. Family and Friends Starter. Family and Friends Starter Class Book pdf. Family and Friends Starter Workbook pdf. Family and Friends Starter Audio. Family and Friends Starter Video. To get a Free copy, put your email address in the responses and a free copy will be sent to you as soon as possible.
Post a response to report a link that does not work Post a response for a free copy via email. Family and Friends 1. Family and Friends 1 , Video Fluency. Family and Friends 1 , Video Skills. Family and Friends 1 , Video CB. Family and Friends 2. Family and Friends 2 Class Book pdf. Family and Friends 2 Workbook pdf. Family and Friends 2 Flashcards pdf. Family and Friends 3.
Family and Friends 3 Class book. Family and Friends 3 Audio CD1. Family and Friend s 3 Audio CD2. Family and Friend s 3 Video Skills. Family and Friend s 3 Video Fluency. Family and Friends 4. Family and Friends 5. Family and Friends 6 Family and Friends - English for kids. Family and Friends 6. To get a F ree copy, put your email address in the responses and a free copy will be sent to you as soon as possible.
If you have problems extracting files. Download the activated version of WinRAR. How to download from File-up. Learn about some ways you can help your child or adolescent to cope and stay connected with family and friends while they are separated from them. Give one of these a try! Share funny stories or memories. Write a story where you are the main character and ask someone else to make up an ending to the story. Decorators implemented as classes and as functions 2. Copying the docstring and other attributes of the original function 2.
Examples in the standard library 2. Deprecation of functions 2. A while -loop removing decorator 2. A plugin registration system 2. Context managers 2. Catching exceptions 2.
Using generators to define context managers 2. Advanced NumPy 2. Life of ndarray 2. Block of memory 2. Data types The descriptor Example: reading. Findings in dissection 2.
Universal functions 2. What they are? Parts of an Ufunc Making it easier 2. Exercise: building an ufunc from scratch 2. Solution: building an ufunc from scratch 2. Generalized ufuncs 2. Interoperability features 2.
Sharing multidimensional, typed data 2. The old buffer protocol 2. Array interface protocol 2. Array siblings: chararray , maskedarray , matrix 2. Summary 2. Why 2. Reporting bugs Good bug report 2. Contributing to documentation 2. Contributing features 2. How to help, in general 2. Debugging code 2. Avoiding bugs 2. Coding best practices to avoid getting in trouble 2.
Debugging workflow 2. Using the Python debugger 2. Invoking the debugger Postmortem Step-by-step execution Other ways of starting a debugger 2. Debugger commands and interaction Getting help when in the debugger 2. Debugging segmentation faults using gdb 2.
Optimizing code 2. Optimization workflow 2. Profiling Python code 2. Timeit 2. Profiler 2. Line-profiler 2. Making code go faster 2.
Algorithmic optimization Example of the SVD 2. Writing faster numerical code 2. Additional Links 2. Sparse Matrices in SciPy 2. Introduction 2. Why Sparse Matrices? Sparse Matrices vs. Sparse Matrix Storage Schemes 2.
Typical Applications 2. Prerequisites 2. Sparsity Structure Visualization 2. Storage Schemes 2. Common Methods 2. Linear System Solvers 2. Sparse Direct Solvers Examples 2. Eigenvalue Problem Solvers The eigen module 2. Other Interesting Packages 2. Image manipulation and processing using Numpy and Scipy 2. Opening and writing to image files 2. Displaying images 2. Basic manipulations 2. Statistical information 2. Geometrical transformations 2.
Image filtering 2. Sharpening 2. Denoising 2. Mathematical morphology 2. Feature extraction 2. Edge detection 2. Segmentation 2. Measuring objects properties: ndimage.
Full code examples 2. Examples for the image processing chapter 2. Mathematical optimization: finding minima of functions 2.
Knowing your problem 2. Convex versus non-convex optimization 2. Smooth and non-smooth problems 2. Noisy versus exact cost functions 2. Constraints 2. A review of the different optimizers 2. Getting started: 1D optimization 2. Gradient based methods Some intuitions about gradient descent Conjugate gradient descent 2. Newton and quasi-newton methods Newton methods: using the Hessian 2nd differential Quasi-Newton methods: approximating the Hessian on the fly 2.
Examples for the mathematical optimization chapter 2. Global optimizers Brute force: a grid search 2. Practical guide to optimization with scipy 2.
Choosing a method 2. Making your optimizer faster 2. Computing gradients 2. Synthetic exercices 2. Special case: non-linear least-squares 2. Minimizing the norm of a vector function 2. Curve fitting 2. Optimization with constraints 2. Box bounds 2. General constraints 2. Interfacing with C 2. Python-C-Api 2. Example 2. Numpy Support 2. Ctypes 2. SWIG 2. Cython 2. Further Reading and References 2.
Exercises 2. Python-C-API 2. Cython 3. Packages and applications 3. Statistics in Python 3. Data representation and interaction 3. Data as a table 3. The pandas data-frame Creating dataframes: reading data files or converting arrays Manipulating data Plotting data 3.
Hypothesis testing: comparing two groups 3. Paired tests: repeated measurements on the same individuals 3. Linear models, multiple factors, and analysis of variance 3. Multiple Regression: including multiple factors 3. More visualization: seaborn for statistical exploration 3. Pairplot: scatter matrices 3. Testing for interactions 3. Full code for the figures 3. Sympy : Symbolic Mathematics in Python 3. First Steps with SymPy 3. Using SymPy as a calculator 3. Symbols 3.
Algebraic manipulations 3. Expand 3. Simplify 3. Calculus 3. Limits 3. Differentiation 3. Series expansion 3. Integration 3. Equation solving 3. Linear Algebra 3. Matrices 3. Differential Equations 3. Scikit-image: image processing 3.
Introduction and concepts 3. Data types 3. Colorspaces 3. Local filters 3. Non-local filters 3. Mathematical morphology 3.
Image segmentation 3. Marker based methods Watershed segmentation Random walker segmentation 3. Data visualization and interaction 3. Feature extraction for computer vision 3. Full code examples 3. Examples for the scikit-image chapter 3. Traits: building interactive dialogs 3.
Introduction 3. Example 3. What are Traits 3. Initialisation 3. Validation 3. Documentation 3. Visualization: opening a dialog 3. Deferral 3. Notification 3. Some more advanced traits 3. Mlab: the scripting interface 3. Figures and decorations Figure management Changing plot properties Decorations 3. Interactive work 3. The script recording button 3. Slicing and dicing data: sources, modules and filters 3.
An example: inspecting magnetic fields 3. Different views on data: sources and modules Different sources: scatters and fields Transforming data: filters mlab. Animating the data 3. Making interactive dialogs 3. A simple dialog 3. Making it interactive 3. Putting it together 3. Introduction: problem settings 3. What is machine learning? Basic principles of machine learning with scikit-learn 3. Introducing the scikit-learn estimator object Fitting on data 3. Supervised Learning: Classification and regression 3.
Regularization: what it is and why it is necessary Prefering simpler models Simple versus complex models for classification 3. Supervised Learning: Classification of Handwritten Digits 3. The nature of the data 3. Visualizing the Data on its principal components 3. Gaussian Naive Bayes Classification 3. Quantitative Measurement of Performance 3.
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