README.md in tensor_stream-0.1.2 vs README.md in tensor_stream-0.1.3

@@ -4,14 +4,15 @@
 
 The goal of this gem is to have a high performance machine learning and compute solution for ruby with support for a wide range of hardware and software configuration.
 
 ## Features
 
-- Replicates most of the commonly used low-level tensorflow ops
+- Replicates most of the commonly used low-level tensorflow ops (tf.add, tf.constant, tf.placeholder, tf.matmul, tf.sin etc...)
 - Supports auto-differentiation via tf.gradients (mostly)
 - Provision to use your own opcode evaluator (opencl, sciruby and tensorflow backends planned)
 - Goal is to be as close to TensorFlow in behavior but with some freedom to add ruby specific enhancements (with lots of test cases)
+- eager execution (experimental)
 
 Since this is a pure ruby implementation for now, performance is not there yet. However it should be a good enough environment to learn about tensorflow and experiment with some models.
 
 ## Installation
 
@@ -89,9 +90,79 @@
     puts("Optimization Finished!")
     training_cost = sess.run(cost, feed_dict: { X => train_X, Y => train_Y})
     puts("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
     puts("time elapsed ", Time.now.to_i - start_time.to_i)
 end
+```
+
+## python to ruby guide
+
+Not all ops are available. Available ops are defined in lib/tensor_stream/ops.rb, corresponding gradients are found at lib/tensor_stream/math_gradients.
+
+There are also certain differences with regards to naming conventions, and named parameters:
+
+# Variables
+
+To make referencing python examples easier it is recommended to use "tf" as the TensorStream namespace
+
+At the beginning
+```ruby
+tf = TensorStream # recommended to use tf since most sample models on the net use this
+ts = TensorStream # use this if you plan to use TensorStream only features, so other devs will know about that
+```
+
+Note the difference in named and optional parameters
+
+Python
+
+```python
+w = tf.Variable(0, name='weights')
+w = tf.Variable(0, 'weights')
+```
+
+Ruby
+
+```ruby
+w =tf.variable(0, name: 'weights')
+```
+
+# Shapes
+
+Python
+```python
+x = tf.placeholder(tf.float32, shape=(1024, 1024))
+x = tf.placeholder(tf.float32, shape=(None, 1024))
+```
+
+ruby supports symbols for specifying data types, nil can be used for None
+
+Ruby
+```ruby
+x = tf.placeholder(:float32, shape: [1024, 1024])
+x = tf.placeholder(:float32, shape: [nil, 1024])
+```
+
+For debugging, each operation or tensor supports the to_math method
+
+```ruby
+X = tf.placeholder("float")
+Y = tf.placeholder("float")
+W = tf.variable(rand, name: "weight")
+b = tf.variable(rand, name: "bias")
+pred = X * W + b
+cost = tf.reduce_sum(tf.pow(pred - Y, 2)) / ( 2 * 10)
+cost.to_math # "(reduce_sum(|((((Placeholder: * weight) + bias) - Placeholder_2:)^2)|) / 10.0)"
+```
+
+breakpoints can also be set, block will be evaluated during computation
+
+```ruby
+a = tf.constant([2,2])
+b = tf.constant([3,3])
+
+f = tf.matmul(a, b).breakpoint! { |tensor, a, b, result_value| binding.pry }
+
+tf.session.run(f)
 ```
 
 ## Roadmap
 
 - Docs