The abundance of data and affordable cloud scale has led to an explosion of interest in Deep Learning. Google has released an excellent library called TensorFlow to open-source, allowing state-of-the-art machine learning done at scale, complete with GPU-based acceleration.
Working with TensorFlow is a hands-on course that explores algorithms, machine learning, and data mining concepts, and how TensorFlow implements them, working in a hands-on manner. This “skills-centric” course is about 50% hands-on lab and 50% lecture, integrating practical hands-on labs designed to reinforce fundamental skills, concepts and best practices introduced throughout the course.
Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" into every classroom and hands-on project. This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Throughout the course, led by our expert team, students will explore:
- Core Deep Learning and Machine Learning math essentials
- TensorFlow Overview and Basics.
- TensorFlow Operations
- Neural Networks With TensorFlow
- Deep Learning With TensorFlow
Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate. We offer additional big data, analytics. AI, machine learning, programming, Python/R and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.
This in an intermediate-level course is geared for experienced developers or others (with prior Python experience) intending to start using and working with TensorFlow.
Students should have attended or have incoming skills equivalent to those in this course:
- Strong foundational mathematics in Linear Algebra and Probability; Matrix Transformation, Regressions, Standard Deviation, Statistics, Classification, etc.
- Basic knowledge of machine learning and deep learning algorithms
- Strong basic Python Skills
Take Before: Attending students should have incoming skills equivalent to those in the course(s0 listed below or should have attended the course(s) as a pre-requisite:
- TTPS4800: Introduction to Python Programming
- TTML5504: Machine Learning Foundation: Working with Statistics, Algorithms and Neural Networks
- Or: TTML5506: Machine Learning Essentials
Take Next / Follow-on Courses: This course is a core component of our AI & Machine Learning Skills Path, designed to trainer participants of all skill levels in modern AI, Machine Learning and Analytics skills across the enterprise. We offer courses in next level AI and Machine Learning, Deep Learning, Natural Language Processing, Applied Machine Learning (Chatbots, Intelligent Web) and many more related titles.
Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We will work with you to tune this course and level of coverage to target the skills you need most.
- Machine Learning & Deep Learning Overview
- This is summary of ML/DL Concepts (from the class – Machine Learning & Deep Learning Fundamentals)
- Mathematical Concepts
- ML Overview
- DL Overview
- TensorFlow – Overview & Basics
- TensorFlow – What is it? History & Background
- Use cases & Key Applications
- Machine Learning & Deep Learning Basics
- Environment, Configuration Settings & Installation
- TensorFlow Primitives
- Declaring Tensors
- Declaring Placeholders and Variables
- Working with Matrices
- Declaring Operations
- Operations in Computational Graph
- Nested Operations
- Multiple Layers
- Implementing Loss Functions
- Implementing Back Propagation
- Machine Learning With TensorFlow
- Linear Regression Review
- Linear Regression Using TensorFlow
- Support Vector Machines (SVM) Review
- SVM using TensorFlow
- Nearest Neighbor Method Review
- Nearest Neighbor Method using TensorFlow
- Neural Networks With TensorFlow
- Neural Networks Review
- Optimization and Operational Gates
- Working with Activation Functions
- Implementing One-Layer Neural Network
- Implementing Different Layers
- Implementing Multilayer Neural Networks
- Deep Neural Networks With TensorFlow
- Models and Overview
- Single Hidden Layer
- Multiple Hidden Layer
- Convolutional Neural Network Overview & Implementation
- CNN Architecture
- Recurrent Neural Network Overview & Implementation
- RNN Architecture
- TensorFlow: Additional Topics
- TensorFlow Extensions
- Scikit Flow
- Unit Testing
- Taking your implementation to production
- Other Misc Topics
Student Materials: Each participant will receive a Student Guide with course notes, code samples, software tutorials, step-by-step written lab instructions, diagrams and related reference materials and resource links. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.
Hands-On Setup Made Simple! Our dedicated tech team will work with you to ensure our ‘easy-access’ cloud-based course environment is accessible, fully-tested and verified as ready to go well in advance of the course start date, ensuring a smooth start to class and effective learning experience for all participants. Please inquire for details and options.