added kaggle Deep Learning Regression

Author: zoupeicheng

EfficientCoding.md

@@ -0,0 +1,110 @@
+# Pythonic Code!
+We want to write fast, memory efficient and super readable code.
+Try this out:
+```python
+import this
+```
+
+At the beginning of my career, I did not pay a lot attention to this.
+However, you really need this not just for efficiency but also for being able to understand pythonic code.
+
+
+## Introduction
+
+### Unpacking and list apprehension
+Beyond List Apprehension: Unpacking!
+Convert a range object into a list:
+```python
+my_list = [*range(1,99,2)]
+```
+
+Use enumerate for index and value pairs:
+```python
+indexed_vals_comp = [(i,val) for i, val in enumerate(vals)]
+## But this is not pythonic enough! Do this:
+indexed_vals_unpack = [*enumerate(vals, 1)] ## Begin index at 1 
+
+## try unpack with map()
+
+verbs_map = map(str.upper,verbs)
+verb_uppercase =[*verbs_map]
+```
+
+Summerizing: you only need list apprehension if you are doing not vectorizble actions to entries in a iterator.
+
+### Numpy 
+Boolean Indexing and Broadcasting
+Re-bind the IDs and the features
+```python
+old_features = [*range(2,99,3)]
+
+old_features_np = np.array(old_features)
+new_features = old_features * 9
+
+new_bindings = [(IDs[i], new_feature) for i,new_feature in enumerate(new_features)]
+
+## Use the new bindings for new mappings
+def phrase(index,i):
+    return " ".join([index,"record has val",i])
+
+phrase_map = map(phrase,new_bindings)
+
+phrases = [*phrase_map]
+print(*phrases,sep='\n')
+```
+
+## Examing Run Time
+IPython Magic commands are enhancements on top of normal Python syntax
+Prefixed by the "%" character
+
+Try this out:
+```python
+%lsmagic
+```
+
+Now we focus on %timeit
+```python
+%timeit
+
+%%timeit
+```
+After these, you can discover which codes are more efficient than others!
+
+### runs and times per run
+```python
+%timeit -r9 -n12 formal_dict = dict()
+%timeit -r9 -n12 literal_dict = {}
+```
+
+### cell magic
+```python
+%%timeit
+## Anycode in this cell...
+```
+### actual timings
+```python
+times = %timeit -o set(my_stuff)
+
+## times for each run
+print(times.timings)
+print(times.best)
+print(times.worst)
+print(times.average)
+
+```
+## Code profiling
+Let's see the frequency and timing of each lines of a code
+```python
+!pip install line_profiler
+
+%load_ext line_profiler
+
+## Magic command for line-by-line times
+%lprun
+
+## profile a function
+%lprun -f function_name function_name(arg1,arg2,agr3)
+
+```
+
+

README.md

@@ -58,7 +58,7 @@ There are many great sources to learn Data Science, and here are some advice to
         * Hyperparameter Tuning
         * Pipelining
 
-7. [Basic Deep Learning](DeepLearningBasic.md)
+7. [Basic Deep Learning](https://www.kaggle.com/danielzou/housing-prices-tf-dnn)
     * a very short DNN example
 
 8. [Git](git.md)