to let it install cleanly on M1 macOS

Working with python3.9.5 and tensorflow-metal optimized for the M1 arm architecture, including the integrated GPU.

Fix totalgood#44 .

README.md

@@ -86,11 +86,11 @@ You have two alternative package managers you can use to install `nlpia`:
 
 5.1. [`conda`](https://github.com/totalgood/nlpia/blob/master/README.md#alternative-51-conda)
 5.2. [`pip`](https://github.com/totalgood/nlpia/blob/master/README.md#alternative-52-pip)
+5.3. [`docker`](https://github.com/totalgood/nlpia/blob/master/README.md#alternative-53-docker)
+5.4. [`miniforge`](https://github.com/totalgood/nlpia/blob/master/README.md#alternative-54-miniforge)
 
 A helpful [NLPIA](http://bit.ly/gh-readme-nlpia-book) reader, [Hoang Chung Hien](https://github.com/hoangchunghien), created a Dockerfile you can use for a third way to manage your environment:
 
-5.3. [`docker`](https://github.com/totalgood/nlpia/blob/master/README.md#alternative-53-docker)
-
 In most cases, `conda` will be able to install python packages faster and more reliably than `pip`. Without `conda` Some packages, such as `python-levenshtein`, require you to compile a C library during installation. Windows doesn't have a a compiler and python package installer that will "just work."
 
 #### Alternative 5.1. `conda`
@@ -191,6 +191,24 @@ docker run -p 8888:8888 -v ~:/home/jovyan/work nlpia
 
 Then open a new notebook and test your code. Make sure save it inside `work` directory so it's accessible outside the container.
 
+
+## Alternative 5.3. `miniforge`
+
+### 5.4.1 Install Python3.9.5 and tensorflow-metal with miniforge
+
+Follow this [tutorial](https://makeoptim.com/en/deep-learning/tensorflow-metal)
+
+### 5.4.2 Install all requirements with pip
+
+Miniforge will give you a Python3.9.5 (or more) virtual environment, capable to run the accelerated tensorflow-metal , optimized to use the M1 integrated GPU hardware.
+
+    pip install -r requirements.txt
+
+### 5.4.3 Install mlpia
+
+    python setup.py install
+
+
 ### 6. Have Fun!
 
 Check out the code examples from the book in `nlpia/nlpia/book/examples` to get ideas: