TensorFlow for Deep Learning Bootcamp

<div>Just launched with all modern best practices for building neural networks with TensorFlow and becoming a <b><u>TensorFlow &amp; Deep Learning Expert!</u></b></div><div><br></div><div>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.</div><div><br></div><div>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!</div><div><br></div><div>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!):</div><div><br></div><div>The goal of this course is to teach you all the skills necessary for you to become a top 10% TensorFlow Developer.</div><div><br></div><div>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.</div><div><span style="font-size: 1rem;"><br></span></div><div><span style="font-size: 1rem;">0 — TensorFlow Fundamentals</span></div><div><ul><li><span style="font-size: 1rem;">Introduction to tensors (creating tensors)</span></li><li><span style="font-size: 1rem;">Getting information from tensors (tensor attributes)</span></li><li><span style="font-size: 1rem;">Manipulating tensors (tensor operations)</span></li><li><span style="font-size: 1rem;">Tensors and NumPy</span></li><li><span style="font-size: 1rem;">Using @tf.function (a way to speed up your regular Python functions)</span></li><li><span style="font-size: 1rem;">Using GPUs with TensorFlow</span></li></ul></div><div><span style="font-size: 1rem;">1 — Neural Network Regression with TensorFlow</span></div><div><ul><li><span style="font-size: 1rem;">Build TensorFlow sequential models with multiple layers</span></li><li><span style="font-size: 1rem;">Prepare data for use with a machine learning model</span></li><li><span style="font-size: 1rem;">Learn the different components which make up a deep learning model (loss function, architecture, optimization function)</span></li><li><span style="font-size: 1rem;">Learn how to diagnose a regression problem (predicting a number) and build a neural network for it</span></li></ul></div><div><span style="font-size: 1rem;">2 — Neural Network Classification with TensorFlow</span></div><div><ul><li><span style="font-size: 1rem;">Learn how to diagnose a classification problem (predicting whether something is one thing or another)</span></li><li><span style="font-size: 1rem;">Build, compile &amp; train machine learning classification models using TensorFlow</span></li><li><span style="font-size: 1rem;">Build and train models for binary and multi-class classification</span></li><li><span style="font-size: 1rem;">Plot modelling performance metrics against each other</span></li><li><span style="font-size: 1rem;">Match input (training data shape) and output shapes (prediction data target)</span></li></ul></div><div><span style="font-size: 1rem;">3 — Computer Vision and Convolutional Neural Networks with TensorFlow</span></div><div><ul><li><span style="font-size: 1rem;">Build convolutional neural networks with Conv2D and pooling layers</span></li><li><span style="font-size: 1rem;">Learn how to diagnose different kinds of computer vision problems</span></li><li><span style="font-size: 1rem;">Learn to how to build computer vision neural networks</span></li><li><span style="font-size: 1rem;">Learn how to use real-world images with your computer vision models</span></li></ul></div><div><span style="font-size: 1rem;">4 — Transfer Learning with TensorFlow Part 1: Feature Extraction</span></div><div><ul><li><span style="font-size: 1rem;">Learn how to use pre-trained models to extract features from your own data</span></li><li><span style="font-size: 1rem;">Learn how to use TensorFlow Hub for pre-trained models</span></li><li><span style="font-size: 1rem;">Learn how to use TensorBoard to compare the performance of several different models</span></li></ul></div><div><span style="font-size: 1rem;">5 — Transfer Learning with TensorFlow Part 2: Fine-tuning</span></div><div><ul><li><span style="font-size: 1rem;">Learn how to setup and run several machine learning experiments</span></li><li><span style="font-size: 1rem;">Learn how to use data augmentation to increase the diversity of your training data</span></li><li><span style="font-size: 1rem;">Learn how to fine-tune a pre-trained model to your own custom problem</span></li><li><span style="font-size: 1rem;">Learn how to use Callbacks to add functionality to your model during training</span></li></ul></div><div><span style="font-size: 1rem;">6 — Transfer Learning with TensorFlow Part 3: Scaling Up (Food Vision mini)</span></div><div><ul><li><span style="font-size: 1rem;">Learn how to scale up an existing model</span></li><li><span style="font-size: 1rem;">Learn to how evaluate your machine learning models by finding the most wrong predictions</span></li><li><span style="font-size: 1rem;">Beat the original Food101 paper using only 10% of the data</span></li></ul></div><div><span style="font-size: 1rem;">7 — Milestone Project 1: Food Vision</span></div><div><ul><li><span style="font-size: 1rem;">Combine 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. Our model well and truly beats the original Food101 paper.</span></li></ul></div><div><span style="font-size: 1rem;">8 — NLP Fundamentals in TensorFlow</span></div><div><span style="font-size: 1rem;">Learn to:</span></div><div><ul><li><span style="font-size: 1rem;">Preprocess natural language text to be used with a neural network</span></li><li><span style="font-size: 1rem;">Create word embeddings (numerical representations of text) with TensorFlow</span></li><li><span style="font-size: 1rem;">Build neural networks capable of binary and multi-class classification using:</span></li><li><span style="font-size: 1rem;">RNNs (recurrent neural networks)</span></li><li><span style="font-size: 1rem;">LSTMs (long short-term memory cells)</span></li><li><span style="font-size: 1rem;">GRUs (gated recurrent units)</span></li><li><span style="font-size: 1rem;">CNNs</span></li><li><span style="font-size: 1rem;">Learn how to evaluate your NLP models</span></li></ul></div><div><span style="font-size: 1rem;">9 — Milestone Project 2: SkimLit</span></div><div><ul><li><span style="font-size: 1rem;">Replicate a the model which powers the PubMed 200k paper to classify different sequences in PubMed medical abstracts (which can help researchers read through medical abstracts faster)</span></li></ul></div><div><span style="font-size: 1rem;">10 — Time Series fundamentals in TensorFlow</span></div><div><ul><li><span style="font-size: 1rem;">Learn how to diagnose a time series problem (building a model to make predictions based on data across time, e.g. predicting the stock price of AAPL tomorrow)</span></li><li><span style="font-size: 1rem;">Prepare data for time series neural networks (features and labels)</span></li><li><span style="font-size: 1rem;">Understanding and using different time series evaluation methods</span></li><li><span style="font-size: 1rem;">MAE — mean absolute error</span></li><li><span style="font-size: 1rem;">Build time series forecasting models with TensorFlow</span></li><li><span style="font-size: 1rem;">RNNs (recurrent neural networks)</span></li><li><span style="font-size: 1rem;">CNNs (convolutional neural networks)</span></li></ul></div><div><span style="font-size: 1rem;">11 — Milestone Project 3: (Surprise)</span></div><div><ul><li><span style="font-size: 1rem;">If you've read this far, you are probably interested in the course. This last project will be good... we promise you, so see you inside the course ;)</span></li></ul></div><div><br></div><div>TensorFlow is growing in popularity and more and more job openings are appearing for this specialized knowledge. As a matter of fact, TensorFlow is outgrowing other popular ML tools like PyTorch in job market. Google, Airbnb, Uber, DeepMind, Intel, IBM, Twitter, and many others are currently powered by TensorFlow. There is a reason these big tech companies are using this technology and you will find out all about the power that TensorFlow gives developers.</div><div><br></div><div><span style="font-size: 1rem;">We guarantee you this is the most comprehensive online course on TensorFlow. So why wait? Make yourself stand out by becoming a TensorFlow Expert and advance your career.</span></div><div><br></div><div>See you inside the course!</div>

What you'll learn:

• Build TensorFlow models using Computer Vision, Convolutional Neural Networks and Natural Language Processing
• Complete access to ALL interactive notebooks and ALL course slides as downloadable guides
• Increase your skills in Machine Learning, Artificial Intelligence, and Deep Learning
• Understand how to integrate Machine Learning into tools and applications
• Learn to build all types of Machine Learning Models using the latest TensorFlow 2
• Build image recognition, text recognition algorithms with deep neural networks and convolutional neural networks
• Using real world images to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy
• Applying Deep Learning for Time Series Forecasting
• Gain the skills you need to become a TensorFlow Developer
• Be recognized as a top candidate for recruiters seeking TensorFlow developers