TensorFlow - Intro to TensorFlow for Deep Learning

Duration

Duration:

Only 2 Days

Method

Method:

Classroom / Online / Hybrid

Next date

Next date:

24/6/2024 (Monday)

Overview

On this accelerated Intro to TensorFlow for Deep Learning course, you’ll learn the foundation of Machine Learning (ML) and Deep Learning, and how they are applied to the TensoFlow platform.

In just 2 days, you’ll learn how to build deep-learning models as well as learn how to use your TensorFlow models in the real-world on mobile devives, in the cloud, and in browsers. You’l build knowledge on:

  • Image recognition, object detection, text recognition algorithms with deep neural networks and convolutional neural networks
  • Applying neural networks to solve natural language processing problems using TensorFlow
  • Strategies to prevent overfitting, including augmentation and dropouts

At the end of the course, you’ll sit the Intro to TensorFlow for Deep Learning exam and achieve your certification. Through Firebrand’s Lecture | Lab | Review methodology, you’ll get access to courseware, learn from certified instructors, and train in a distraction-free environment.

Audience

If you’re a Software Developer, this course is ideal for you.

Benefits

Seven reasons why you should sit your course with Firebrand Training

  1. Two options of training. Choose between residential classroom-based, or online courses
  2. You'll be certified fast. With us, you’ll be trained in record time
  3. Our course is all-inclusive. A one-off fee covers all course materials, exams**, accommodation* and meals*. No hidden extras.
  4. Pass the first time or train again for free. This is our guarantee. We’re confident you’ll pass your course the first time. But if not, come back within a year and only pay for accommodation, exams and incidental costs
  5. You’ll learn more. A day with a traditional training provider generally runs from 9 am – 5 pm, with a nice long break for lunch. With Firebrand Training you’ll get at least 12 hours/day of quality learning time, with your instructor
  6. You’ll learn faster. Chances are, you’ll have a different learning style to those around you. We combine visual, auditory and tactile styles to deliver the material in a way that ensures you will learn faster and more easily
  7. You’ll be studying with the best. We’ve been named in the Training Industry’s “Top 20 IT Training Companies of the Year” every year since 2010. As well as winning many more awards, we’ve trained and certified over 135,000 professionals
  • * For residential training only. Doesn't apply for online courses
  • ** Some exceptions apply. Please refer to the Exam Track or speak with our experts

Curriculum

  • Section 1: Introduction to Machine Learning
  • Section 2: Your First Model: Fashion MNIST
  • Section 3: Introduction to Convolutional Neural Networks ("CNNs")
  • Section 4: Going Further with CNNs
  • Section 5: Transfer Learning
  • Section 6: Saving and Loading Models
  • Section 7: Time Series Forecasting
  • Section 8: Introduction to TensorFlow Lite

Exam Track

At the end of this accelerated course, you’ll sit the following exam at the Firebrand Training centre, covered by your Certification Guarantee:

Intro to TensorFlow for Deep Learning exam

  • Format: Multiple-choice
  • Number of questions: 60
  • Duration: 2 hours
  • Passing score: 70%

What's Included

Your accelerated course includes:

  • Accommodation *
  • Meals, unlimited snacks, beverages, tea and coffee *
  • On-site exams **
  • Exam vouchers **
  • Practice tests **
  • Certification Guarantee ***
  • Courseware
  • Up-to 12 hours of instructor-led training each day
  • 24-hour lab access
  • Digital courseware **
  • * For residential training only. Accommodation is included from the night before the course starts. This doesn't apply for online courses.
  • ** Some exceptions apply. Please refer to the Exam Track or speak with our experts
  • *** Pass first time or train again free as many times as it takes, unlimited for 1 year. Just pay for accommodation, exams, and incidental costs.

Prerequisites

Before attending this accelerated, TensorFlow recommend you have the following experience in:

  • Calculating the probability of an event
  • Programming (at least 40hrs)
  • Libraries like NumPy and pandas is a plus

You should also have knowledge in:

  • Algebra
  • Calculating the mean and variance of a probability distribution is a plus
  • Probability and statistics
  • Data structures like dictionaries and lists
  • Python syntax, including variables, functions, classes, and object-oriented programming

Unsure whether you meet the prerequisites? Don’t worry. Your training consultant will discuss your background with you to understand if this course is right for you.

Reviews

Here's the Firebrand Training review section. Since 2001 we've trained exactly 134,561 students and asked them all to review our Accelerated Learning. Currently, 96.41% have said Firebrand exceeded their expectations.

Read reviews from recent accelerated courses below or visit Firebrand Stories for written and video interviews from our alumni.


"I had a great time. The instructor is very knowledgeable and his training is on point. Two exams in 2 days is a lot. But he makes it doable."
Juan van Gom, Ministry of Defense Netherlands. (19/1/2024 (Friday) to 21/1/2024 (Sunday))

"It was a tough but interesting 5 days. Learnt new skills and have a better understanding of the tools that can be used by a Business Analyst. Met new people from different companies with different expertise."
Neil Wyborn. (8/1/2024 (Monday) to 12/1/2024 (Friday))

"Great residential place of study. Great tuition with a great Trainer. Great facilities to learn. Looking forward to the next residential!! Looking forward to applying knowledge gained!"
SL, Macmillan Support Cancer. (8/1/2024 (Monday) to 12/1/2024 (Friday))

"Very organised and clear. Enjoyed the teaching. Excellent standard."
MH, Orange cyber defense. (30/10/2023 (Monday) to 1/11/2023 (Wednesday))

"Detailed and thorough training in a great environment."
Anonymous, SYP. (8/1/2024 (Monday) to 12/1/2024 (Friday))

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