We recently finished our first session of Jon Krohn’s Deep Learning Bootcamp, and we’re already excited for part 2. Here are a few highlights from the session, some thoughts from attendees, and what to expect from part 2 and beyond.
Session 1 Recap: How Deep Learning Works
In the first session of the Deep Learning Bootcamp, attendees received a broad overview of deep learning, including how it works, why deep learning is essential, and core neural network theory.
While this session didn’t dive too far into the execution side of things, the first session of the deep learning bootcamp discussed how machine learning evolves into deep learning and discussed core mechanics like feedforward networks, convolutional networks, recurrent networks, reinforcement learning, and more.
Here’s what one attendee had to say after the session:
What an excellent foundation for understanding Deep Learning! Just enough history, what the key breakthroughs were and how they advanced Neural Nets, types of network architectures, and activation functions. A brief mention of backprop and gradient descent (intuition and how they’re used in DL) – if you want details on those I highly recommend Jon’s Foundations of ML (Linear Algebra and Calc bootcamps). Looking forward to the rest of the DL Bootcamp series!
What to Expect from Session 2: Building and Training a Deep Learning Network
In the second session of the deep learning bootcamp, you’ll take a deep dive into actually building a deep learning network using the background and theory from the first session. Some of the core skills you’ll learn include topics like stochastic gradient descent, backpropagation, learning rate, dense layers, TensorFlow, and so on. This will set you up for future sessions on machine vision, NLP, and more.
Join the Deep Learning Bootcamp
It’s not too late to join Jon Krohn’s future sessions! You can register for individual courses, all six courses, or gain access to the entire bootcamp by signing up for a yearly subscription to Ai+ Training. Learn more here!
