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    "# Sentiment Classification & How To \"Frame Problems\" for a Neural Network\n",
    "\n",
    "by Andrew Trask\n",
    "\n",
    "- **Twitter**: @iamtrask\n",
    "- **Blog**: http://iamtrask.github.io"
   ]
  },
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    "### What You Should Already Know\n",
    "\n",
    "- neural networks, forward and back-propagation\n",
    "- stochastic gradient descent\n",
    "- mean squared error\n",
    "- and train/test splits\n",
    "\n",
    "### Where to Get Help if You Need it\n",
    "- Re-watch previous Udacity Lectures\n",
    "- Leverage the recommended Course Reading Material - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) (40% Off: **traskud17**)\n",
    "- Shoot me a tweet @iamtrask\n",
    "\n",
    "\n",
    "### Tutorial Outline:\n",
    "\n",
    "- Intro: The Importance of \"Framing a Problem\"\n",
    "\n",
    "\n",
    "- Curate a Dataset\n",
    "- Developing a \"Predictive Theory\"\n",
    "- **PROJECT 1**: Quick Theory Validation\n",
    "\n",
    "\n",
    "- Transforming Text to Numbers\n",
    "- **PROJECT 2**: Creating the Input/Output Data\n",
    "\n",
    "\n",
    "- Putting it all together in a Neural Network\n",
    "- **PROJECT 3**: Building our Neural Network\n",
    "\n",
    "\n",
    "- Understanding Neural Noise\n",
    "- **PROJECT 4**: Making Learning Faster by Reducing Noise\n",
    "\n",
    "\n",
    "- Analyzing Inefficiencies in our Network\n",
    "- **PROJECT 5**: Making our Network Train and Run Faster\n",
    "\n",
    "\n",
    "- Further Noise Reduction\n",
    "- **PROJECT 6**: Reducing Noise by Strategically Reducing the Vocabulary\n",
    "\n",
    "\n",
    "- Analysis: What's going on in the weights?"
   ]
  }
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