Intro To TensorFlow For Deep Learning

On this accelerated Intro to TensorFlow for Deep Learning course, you’ll learn the foundation of Machine Learning (ML) and Deep Learning, and how they are applied to the TensoFlow platform.

In just 2 days, you’ll learn how to build deep-learning models as well as learn how to use your TensorFlow models in the real-world on mobile devives, in the cloud, and in browsers. You’l build knowledge on:

Image recognition, object detection, text recognition algorithms with deep neural networks and convolutional neural networks

Applying neural networks to solve natural language processing problems using TensorFlow
Strategies to prevent overfitting, including augmentation and dropouts

At the end of the course, you’ll sit the Intro to TensorFlow for Deep Learning exam and achieve your certification. Through Firebrand’s Lecture | Lab | Review methodology, you’ll get access to courseware, learn from certified instructors, and train in a distraction-free environment.

Audience:

If you’re a Software Developer, this course is ideal for you.

Curriculum:

• Section 1: Introduction to Machine Learning
• Section 2: Your First Model: Fashion MNIST
• Section 3: Introduction to Convolutional Neural Networks ("CNNs")
• Section 4: Going Further with CNNs
• Section 5: Transfer Learning
• Section 6: Saving and Loading Models
• Section 7: Time Series Forecasting
• Section 8: Introduction to TensorFlow Lite

Prerequisites:

• Before attending this accelerated, TensorFlow recommend you have the following experience in:
• Calculating the probability of an event
• Programming (at least 40hrs)
• Libraries like NumPy and pandas is a plus
• You should also have knowledge in:
• Algebra
• Calculating the mean and variance of a probability distribution is a plus
• Probability and statistics
• Data structures like dictionaries and lists
• Python syntax, including variables, functions, classes, and object-oriented programming