Learning the basics of TensorFlow!

During the past week, I learned some of the basics of TensorFlow specifically starting with machine learning classification basics and tutorials. I first learned that neural networks are created by using connected, dense neural layers that contain nodes. By using the layers.dense method, you can create nodes which would take data as input and combine them with the specific weights assigned to each of the data inputs which would enable the model to calculate and learn the algorithm. Through the knowledge I gained about using TensorFlow, I also found out several methods such as model.fit or .Softmax that would prove to be useful to create simple machine learning projects in the future when I actually start programming models. Still there are a huge amount of resources for learning about TensorFlow and Machine learning which makes me very excited to continue learning and start implementing machine learning in the future.

My goal for this week is to gain a deeper understanding of using TensorFlow by going further than classification problems in machine learning. Furthermore, I still need to work on finishing my Google Machine Learning Crash Course to fully understand neural networks and I continue to strive to learn about machine learning algorithms. So see you next week, same place, same time.