Saron Yitbarek went from Pre-Med to learning to code, to founding CodeNewbie, the most supportive community of programmers and people learning to code. We talked about the things she did while learning to code, reading other people’s code and why she started a small code reading club. We also talked about methods of learning, code quality, and building a successful tech community.

Saron Yitbarek went from Pre-Med to learning to code, to founding CodeNewbie, the most supportive community of programmers and people learning to code. We talked about the things she did while learning to code, reading other people’s code and why she started a small code reading club. We also talked about methods of learning, code quality, and building a successful tech community.

Storytelling is not just something for the movies or TV. It plays an important role in how we interact with applications and brands through our phones and the web. Rachel Nabors, award winning cartoonist, and web developer, compares storytelling through comics with storytelling on the web. We talked about the different areas of design and web animation. Rachel is a Program Manager at Microsoft and just released her book titled Animation at Work.

Storytelling is not just something for the movies or TV. It plays an important role in how we interact with applications and brands through our phones and the web. Rachel Nabors, award winning cartoonist, and web developer, compares storytelling through comics with storytelling on the web. We talked about the different areas of design and web animation. Rachel is a Program Manager at Microsoft and just released her book titled Animation at Work.

What is Data Science? What is the difference between Data Science and Machine Learning? In this episode, Grace Huang, Data Science Manager at Pinterest answers these questions. We talked about complex data sets and building a recommendation system at Pinterest. Grace explained the core algorithms used in a recommendation system and the role of embeddings on this.

What is Data Science? What is the difference between Data Science and Machine Learning? In this episode, Grace Huang, Data Science Manager at Pinterest answers these questions. We talked about complex data sets and building a recommendation system at Pinterest. Grace explained the core algorithms used in a recommendation system and the role of embeddings on this.

We live in a great time to start a company. The resources which were only accessible to big companies are now at our fingertips. Erica Brescia, COO of Bitnami gave advice on how to start a company. We talked about what she learned at YC and the status of the company when it was admitted. Erica explained what Bitnami is building, and pricing strategies. At the end we talked about her role as an investment partner in XFactor Ventures.

We live in a great time to start a company. The resources which were only accessible to big companies are now at our fingertips. Erica Brescia, COO of Bitnami gave advice on how to start a company. We talked about what she learned at YC and the status of the company when it was admitted. Erica explained what Bitnami is building, and pricing strategies. At the end we talked about her role as an investment partner in XFactor Ventures.

Most of us have come across a form of bias when we interact with others. These biases can make their way to a machine learning system, leading to unfair decisions. Rachel Thomas, co-founder of fast.ai and researcher in residence at The University of San Francisco explains the origins and implications of bias in machine learning. We also talked about solutions to limit bias.

Rachel also explained the role of linear algebra in machine learning and how to teach it effectively for people working in ML applications. We talked about the fundamental concepts and how they are applied in machine learning.

Most of us have come across a form of bias when we interact with others. These biases can make their way to a machine learning system, leading to unfair decisions. Rachel Thomas, co-founder of fast.ai and researcher in residence at The University of San Francisco explains the origins and implications of bias in machine learning. We also talked about solutions to limit bias.

Rachel also explained the role of linear algebra in machine learning and how to teach it effectively for people working in ML applications. We talked about the fundamental concepts and how they are applied in machine learning.