TensorFlow for Deep Learning Bootcamp

Just launched with all modern best practices for building neural networks with TensorFlow and becoming a TensorFlow & Deep Learning Expert. Join a live online community of over 900,000+ students and a course taught by a TensorFlow expert. This course will take you from absolute beginner with TensorFlow, to creating state-of-the-art deep learning neural networks.

TensorFlow experts earn up to $204,000 USD a year, with the average salary hovering around $148,000 USD. By taking this course you will be joining the growing Machine Learning industry and becoming a top paid TensorFlow Developer. Here is a full course breakdown of everything we will teach (yes, it's very comprehensive, but don't be intimidated, as we will teach you everything from scratch.

):The goal of this course is to teach you all the skills necessary for you to become a top 10% TensorFlow Developer. This course will be very hands on and project based. You won't just be staring at us teach, but you will actually get to experiment, do exercises, and build machine learning models and projects to mimic real life scenarios.

By the end of it all, you will develop skillsets needed to develop modern deep learning solutions that big tech companies encounter. 0 - TensorFlow FundamentalsIntroduction to tensors (creating tensors)Getting information from tensors (tensor attributes)Manipulating tensors (tensor operations)Tensors and NumPyUsing @tf. function (a way to speed up your regular Python functions)Using GPUs with TensorFlow1 - Neural Network Regression with TensorFlowBuild TensorFlow sequential models with multiple layersPrepare data for use with a machine learning modelLearn the different components which make up a deep learning model (loss function, architecture, optimization function)Learn how to diagnose a regression problem (predicting a number) and build a neural network for it2 - Neural Network Classification with TensorFlowLearn how to diagnose a classification problem (predicting whether something is one thing or another)Build, compile & train machine learning classification models using TensorFlowBuild and train models for binary and multi-class classificationPlot modelling performance metrics against each otherMatch input (training data shape) and output shapes (prediction data target)3 - Computer Vision and Convolutional Neural Networks with TensorFlowBuild convolutional neural networks with Conv2D and pooling layersLearn how to diagnose different kinds of computer vision problemsLearn to how to build computer vision neural networksLearn how to use real-world images with your computer vision models4 - Transfer Learning with TensorFlow Part 1: Feature ExtractionLearn how to use pre-trained models to extract features from your own dataLearn how to use TensorFlow Hub for pre-trained modelsLearn how to use TensorBoard to compare the performance of several different models5 - Transfer Learning with TensorFlow Part 2: Fine-tuningLearn how to setup and run several machine learning experimentsLearn how to use data augmentation to increase the diversity of your training dataLearn how to fine-tune a pre-trained model to your own custom problemLearn how to use Callbacks to add functionality to your model during training6 - Transfer Learning with TensorFlow Part 3: Scaling Up (Food Vision mini)Learn how to scale up an existing modelLearn to how evaluate your machine learning models by finding the most wrong predictionsBeat the original Food101 paper using only 10% of the data7 - Milestone Project 1: Food VisionCombine everything you've learned in the previous 6 notebooks to build Food Vision: a computer vision model able to classify 101 different kinds of foods.