GETTING STARTED

Appx A **Installation**

Ch 1 A **machine learning** odyssey

Ch 2 **TensorFlow** essentials

CLASSIC ALGORITHMS

Ch 3 **Linear regression** and beyond

Ch 4 An introduction to **classification**

Ch 5 Automatically **clustering** data

Ch 6 **Hidden Markov** models

NEURAL NETWORKS

Ch 7 A peek into **autoencoders**

Ch 8 Automated **planning**

Ch 9 **Convolutional** neural networks

Ch 10 **Recurrent** neural networks

Ch 11 Advanced **deep** architectures

Installation

Oh, I guess I'll start with the boring chapter on installing TensorFlow on your system to hit the ground running.
To make it less boring, check out that pretty illustration.

It's nice right?

Now that you're feeling inspired, check out what this appendix convers:

It's nice right?

Now that you're feeling inspired, check out what this appendix convers:

- Installing
**TensorFlow**using Docker - Installing
**Matplotlib**

Machine learning

This chapter has no code whatsoever.

It's a beach read, really. Let the fundamental concepts of machine learning sink in before you begin hacking.

Take a deep breath, and follow along to:

It's a beach read, really. Let the fundamental concepts of machine learning sink in before you begin hacking.

Take a deep breath, and follow along to:

- Machine learning
**fundamentals** - Data representation and
**features** - Distance
**metrics** **Supervised**learning**Unsupervised**learning**Reinforcement**learning- Theano, Caffe, Torch, CGT, and
**TensorFlow**

TensorFlow essentials

Turn up emacs to high gear, and drive freely.

Complete this chapter to be a TensorFlow champion. Or, something to that effect.

Use it as a handy reference to the many functionalities of TensorFlow:

Complete this chapter to be a TensorFlow champion. Or, something to that effect.

Use it as a handy reference to the many functionalities of TensorFlow:

- Representing
**tensors** - Creating
**operators** - Executing operators with
**sessions** - Writing code in
**Jupyter** - Using
**variables** **Saving and loading**variables- Visualizing data using
**TensorBoard**

Linear regression

You're going to see dots.

These dots will be connected by a line.

It's going to be a pretty cool line, I guaratee it.

Let's see how to find these lines:

These dots will be connected by a line.

It's going to be a pretty cool line, I guaratee it.

Let's see how to find these lines:

**Formalizing**regression problemsLinear regression **Polynomial**regression**Regularization**- Available
**datasets**

Classification

You know how people say "don't compare apples to oranges." We'll let TensorFlow figure out how to do just that.

Before even jumping into neural networks, let's see what we can do from a couple simple concepts:

Before even jumping into neural networks, let's see what we can do from a couple simple concepts:

**Formalizing**classification problems- Measuring classification
**performance**(ROC curve, precision, recall, etc.) - Using
**linear regression**for classification - Using
**logistic regression**(including multi-dimensional input) **Multiclass**classifiers (such as softmax regression)

Clustering

Unsupervised learning is a romantic idea.

In this chapter, we're going on a date with clustering algorithms.

Here's the itinerary:

In this chapter, we're going on a date with clustering algorithms.

Here's the itinerary:

- Traversing
**files**in TensorFlow - Extracting
**features from audio** **K-means**clustering- Audio
**segmentation** - Clustering using a
**self-organizing map**

Hidden Markov Models

I rarely see HMMs in intro books.

That's probably because it's a difficult concept to teach. Let's see if I did a good job.

Here's what the chapter covers:

That's probably because it's a difficult concept to teach. Let's see if I did a good job.

Here's what the chapter covers:

**Interpretable**models- What is a
**Markov model**? - What is a
**Hidden Markov model**? **Forward**algorithm**Viterbi**decoding algorithm**Uses**of Hidden Markov models

Autoencoders

The autoencoder is the simplest neural network that you can start using immediately.

I mean it. Open your text editor and let's get started.

The chapter starts with basic neural network concepts, and then introduces autoencoders:

I mean it. Open your text editor and let's get started.

The chapter starts with basic neural network concepts, and then introduces autoencoders:

**Neural networks****Autoencoders****Batch**training**Variational/denoising/stacked**autoencoders

Reinforcement learning

Since you made it this far, I'm going to reward you with a million dollars.

Here's how you create a reinforcement learning algorithm to outsmart the stock market.

Follow along closely:

Here's how you create a reinforcement learning algorithm to outsmart the stock market.

Follow along closely:

**Real-world**examples**Formal**definitions**Policy****Utility**- Applying
**reinforcement learning**to the stock market

Convolutional neural networks

The most celebrated progress in neural networks comes from these CNN architectures.

Here's what you need to know:

Here's what you need to know:

**Advantages and disadvantages**of neural networks**Convolutional**neural networks- Preparing
**images** - Generating
**filters** **Convolving**using filters**Max-pooling**- Implementing
**CNN in TensorFlow** - Measuring
**performance** **Tips and tricks**to improve performance

Recurrent neural networks

Do you ever forget what you ate for breakfast?

A recurrent neural network might hold on to that memory. It is a neural architecture which also uses information propagated from the past.

The chapter includes:

A recurrent neural network might hold on to that memory. It is a neural architecture which also uses information propagated from the past.

The chapter includes:

- The idea of
**contextual information** **Recurrent**neural networks**Implementing**it- A predictive model for
**timeseries data**

Other deep architectures

I couldn't fit in all I wanted in the book. But, why stop now?

For example, I haven't even touched upon**vector representation of words**!
How about some **multi-modal embeddings**?
Who's hungry for **sequence-to-sequence models**?

Chapter 11 and all future chapters are free, and will be hosted on the GitHub repo.

For example, I haven't even touched upon

Chapter 11 and all future chapters are free, and will be hosted on the GitHub repo.