TensorFlow for Deep Learning Bootcamp: Zero to Mastery

What Makes Learning TensorFlow a Smart Choice?

Here are the top three reasons to dive into TensorFlow:

1. Plenty of Job Opportunities: Leading companies like Google, Airbnb, Uber, DeepMind, Intel, IBM, and Twitter are utilizing TensorFlow, resulting in a high demand for skilled developers. TensorFlow is rapidly gaining an edge over other machine learning tools like PyTorch in the job marketplace.
2. High Demand Equals High Earnings: On average, TensorFlow developers earn about US$148,000, with some exceeding US$200,000, based on recent ZipRecruiter data.
3. Fun Learning Experience: By learning TensorFlow, you'll have the chance to create deep learning models for various applications, including regression, computer vision (detecting image patterns), natural language processing (analyzing text patterns), and time series forecasting (predicting future events based on historical data). How exciting is that?

This course is designed to equip you with the essential skills recruiters are hunting for in TensorFlow developers.

You’ll be learning TensorFlow alongside a vibrant community.

Graduates of Zero To Mastery have successfully landed positions at prestigious companies like Google, Tesla, Amazon, Apple, IBM, Uber, and Facebook, among others.

You could be one of them.

By signing up today, you’ll join our exclusive live online community classroom where you can learn with thousands of peers, mentors, TAs, and instructors.

Most importantly, you're learning TensorFlow from an industry expert with hands-on Machine Learning experience.

A Glimpse Into Your Learning Journey with TensorFlow:

This course is highly interactive and project-oriented—you won’t be passively watching lectures. Unlike many other courses, we focus on active participation.

You’ll engage in experiments, exercises, and develop real-world machine learning projects that mirror actual challenges.

We’ll also provide insights into what to expect in the TensorFlow examination.

Ultimately, you’ll acquire the skills needed to create cutting-edge deep learning solutions that major tech firms utilize.

Just a heads-up, this course is extremely thorough. But don’t worry; we’ll start from square one!

0. Fundamentals of TensorFlow

• Introduction to tensors (how to create them)
• Extracting data from tensors (tensor characteristics)
• Manipulating tensors (operations on tensors)
• Using Tensors with NumPy
• Utilizing @tf.function (enhancing your Python functions)
• Leveraging GPUs with TensorFlow

1. Regression using Neural Networks in TensorFlow

• Create TensorFlow sequential models with multiple layers
• Prepare data for machine learning applications
• Understand the components of deep learning models (loss functions, architecture, optimization)
• Diagnose regression issues (predicting values) and develop a neural network accordingly

2. Classification via Neural Networks in TensorFlow

• Diagnose classification problems (deciding between options)
• Build, compile, and train classification models using TensorFlow
• Create models for binary and multi-class classifications
• Visualize performance metrics of different models
• Ensure matching shapes for input and output data

3. Computer Vision with Convolutional Neural Networks in TensorFlow

• Create convolutional neural networks with Conv2D and pooling layers
• Identify various computer vision challenges
• Develop computer vision-specific neural networks
• Incorporate real-world images into your vision models

4. Transfer Learning in TensorFlow: Feature Extraction

• Extract features from your data using pre-trained models
• Utilize TensorFlow Hub for accessing pre-trained models
• Employ TensorBoard to assess the performance of various models

5. Transfer Learning in TensorFlow: Fine-tuning

• Conduct multiple machine learning experiments
• Apply data augmentation to enhance training data diversity
• Fine-tune pre-trained models for your specific needs
• Use Callbacks to add features to your model during training

6. Transfer Learning in TensorFlow: Scaling Up (Food Vision mini)

• Expand an existing model to enhance performance
• Evaluate your machine learning models by identifying incorrect predictions
• Exceed the original Food101 paper results using only 10% of the data

7. Milestone Project 1: Food Vision

• Integrate all your knowledge from previous modules to create Food Vision, a model that classifies 101 food types and surpasses the original Food101 paper.

8. NLP Foundations in TensorFlow

You’ll learn to:

• Prepare text data for neural networks
• Create word embeddings with TensorFlow
• Build neural networks for binary and multi-class classification using:
  • RNNs (recurrent neural networks)
  • LSTMs (long short-term memory networks)
  • GRUs (gated recurrent units)
  • CNNs
• Assess your NLP models' performance

9. Milestone Project 2: SkimLit

• Recreate the model featured in the PubMed 200k study to classify sequences in medical abstracts, aiding researchers in reviewing material efficiently.

10. Time Series Basics in TensorFlow

• Identify time series challenges (predicting based on data trends over time, e.g., forecasting AAPL stock)
• Prepare data for time series analysis (features and labels)
• Explore various time series evaluation techniques
  • MAE — mean absolute error
• Construct time series forecasting models using TensorFlow
  • RNNs (recurrent neural networks)
  • CNNs (convolutional neural networks)

11. Milestone Project 3: (Surprise)

• If you've read this far, you’re likely keen on the course. This final project will be worth your while... so see you inside!

What’s the takeaway?

The rise of TensorFlow is creating a surge in job opportunities for professionals skilled in this technology.

Companies like Google, Airbnb, Uber, DeepMind, Intel, IBM, and Twitter are already leveraging TensorFlow. It's even beginning to outpace other ML tools like PyTorch in the employment arena.

We assure you this is the most detailed and current TensorFlow bootcamp available online.

So why hesitate? Elevate your career and boost your earning potential by becoming a Deep Learning Engineer and adding TensorFlow to your skillset 💪.

You’ve nothing to risk, as you can start learning immediately. If this course doesn't meet your expectations, we offer a full refund within 30 days, hassle-free.