Course Overview

This course introduces Deep Learning concepts and TensorFlow library to students. Students will work hands-on with TensorFlow technologies to learn to build their own Deep Learning solutions. The abundance of data and affordable cloud scale has led to an explosion of interest in Deep Learning. Google has open sourced a library called TensorFlow which has become the de-facto standard, allowing state-of-the-art machine learning done at scale, complete with GPU-based acceleration.


This is a technical, hands-on course where students will work with the latest technologies in a series of labs to learn how to create their own solutions. To be successful in this course you need to be a seasonsed software developer who is comfortable and fluent in the Python development language. This course also assumes students have a proficient level of knowledge regarding Jupyter notebooks as well. Students are also expected to have a strong knowledge of the concepts which underlie Machine Learning - this course will NOT cover these concepts in-depth!

Course Outline

Chapter 1: Introduction to Machine Learning Understanding Machine Learning How does ML relate to AI? How does DL relate to ML/AI? Supervised versus Unsupervised Learning Regression Classification Clustering Chapter 2: Introducing TensorFlow TensorFlow intro TensorFlow features TensorFlow versions GPU and TPU scalability Lab: Setting up and Running TensorFlow Chapter 3: The Tensor: The Basic Unit of TensorFlow Introducing Tensors TensorFlow Execution Model Lab: Learning about Tensors Chapter 4: Single Layer Linear Perceptron Classifier With TensorFlow Introducing Perceptrons Linear Separability and XOR Problem Activation Functions Softmax output Backpropagation, Loss functions, and Gradient Descent Lab: Single-Layer Perceptron in TensorFlow Chapter 5: Hidden Layers: Intro to Deep Learning Hidden Layers as a solution to XOR problem Distributed Training with TensorFlow Vanishing Gradient Problem and ReLU Loss Functions Lab: Feedforward Neural Network Classifier in TensorFlow Chapter 6: High-level TensorFlow: tf.learn Using high-level TensorFlow Developing a model with tf.learn Lab: Developing a tf.learn model Chapter 7: Convolutional Neural Networks in TensorFlow Introducing CNNs CNNs in TensorFlow Lab: CNN apps Chapter 8: Introducing Keras What is Keras? Using Keras with a TensorFlow Backend Lab: Example with a Keras Chapter 9: Recurrent Neural Networks in TensorFlow Introducing RNNs RNNs in TensorFlow Lab: RNN Chapter 10: Long Short Term Memory (LSTM) in TensorFlow Introducing LSTM LSTM in TensorFlow Lab: LSTM Chapter 11: Conclusion Summarize features and advantages of TensorFlow Summarize Deep Learning and How TensorFlow can help

Duration: 20 Hours

Course Access: 1 Year

Format: Self-Paced Learning

  • Deep Learning with TensorFlow On Demand

Price: CHF 225.00

  • Join over 100,000 students learning with BTA

  • One year Access to Learning Content

  • 15 Hours of Learning Content

Global Leader in Emerging Tech Education

Deep Learning with TensorFlow

Who MTC/BTA train

Other courses you may like