by Andrew Trask

**Twitter**: @iamtrask**Blog**: http://iamtrask.github.io

- neural networks, forward and back-propagation
- stochastic gradient descent
- mean squared error
- and train/test splits

- Re-watch previous Udacity Lectures
- Leverage the recommended Course Reading Material - Grokking Deep Learning (40% Off:
**traskud17**) - Shoot me a tweet @iamtrask

- Intro: The Importance of "Framing a Problem"

- Curate a Dataset
- Developing a "Predictive Theory"
**PROJECT 1**: Quick Theory Validation

- Transforming Text to Numbers
**PROJECT 2**: Creating the Input/Output Data

- Putting it all together in a Neural Network
**PROJECT 3**: Building our Neural Network

- Understanding Neural Noise
**PROJECT 4**: Making Learning Faster by Reducing Noise

- Analyzing Inefficiencies in our Network
**PROJECT 5**: Making our Network Train and Run Faster

- Further Noise Reduction
**PROJECT 6**: Reducing Noise by Strategically Reducing the Vocabulary

- Analysis: What's going on in the weights?

```
In [1]:
```def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()

```
In [2]:
```len(reviews)

```
Out[2]:
```

```
In [3]:
```reviews[0]

```
Out[3]:
```

```
In [4]:
```labels[0]

```
Out[4]:
```

```
In [5]:
```print("labels.txt \t : \t reviews.txt\n")
pretty_print_review_and_label(2137)
pretty_print_review_and_label(12816)
pretty_print_review_and_label(6267)
pretty_print_review_and_label(21934)
pretty_print_review_and_label(5297)
pretty_print_review_and_label(4998)

```
```