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Deep Learning Course Overview

The deep learning course with tensorflow training in Sydney includes the language and basic concepts of artificial neural networks, PyTorch, autoencoders, etc. Upon completion of the deep learning course with tensorflow training in Sydney, you can create a deep learning model, interpret the results, and create a learning project.

Deep Learning Course Key Features

100% Money Back Guarantee
No questions asked refund*

At Simplilearn, we value the trust of our patrons immensely. But, if you feel that this Deep Learning course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
  • 8X higher live interaction in live online classes by industry experts
  • Real-life industry-based projects
  • Flexibility to choose classes
  • Dedicated mentoring session from our Industry expert faculties

Skills Covered

  • Keras and TensorFlow Framework
  • PyTorch and its elements
  • Image Classification
  • Artificial Neural Networks
  • Autoencoders
  • Deep Neural Networks
  • Conventional Neural Networks
  • Recurrent Neural Networks
  • ADAM Adagrad and Momentum

Benefits

With a deep learning course with tensorflow training in Sydney, the global deep learning system market is poised to touch a mammoth USD 93.34 billion in 2028 with a compound annual growth rate of 39.1%. With a deep learning course with tensorflow training in Sydney, one can gain skills required for the field.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Data Scientist
    Annual Salary
    $83KMin
    $113KAverage
    $154KMax
    Source: Glassdoor
    Hiring Companies
    Accenture hiring for Data Scientist professionals in Sydney
    Oracle hiring for Data Scientist professionals in Sydney
    Microsoft hiring for Data Scientist professionals in Sydney
    Walmart hiring for Data Scientist professionals in Sydney
    Amazon hiring for Data Scientist professionals in Sydney
    Source: Indeed
  • AI Engineer
    Annual Salary
    $51KMin
    $72KAverage
    $110KMax
    Source: Glassdoor
    Hiring Companies
    Qualcomm hiring for AI Engineer professionals in Sydney
    Nvidia hiring for AI Engineer professionals in Sydney
    LarsenAndTurbo hiring for AI Engineer professionals in Sydney
    Honeywell hiring for AI Engineer professionals in Sydney
    Source: Indeed

Training Options

online Bootcamp

A$ 1,285

  • 90 days of flexible access to online classes
  • Lifetime access to high-quality self-paced e-learning content and live class recordings
  • 24x7 learner assistance and support
  • Classes starting in Sydney from:-
10th Dec: Weekday Class
Show all classes

Corporate Training

Customized to your team's needs

  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Deep Learning Course Curriculum

Eligibility

The demand for skilled deep learning engineers in all walks of life is booming, so this deep learning course with tensorflow training in Sydney with Keras and Tensorflow certification training is very suitable for intermediate and advanced professionals. We especially recommend this deep learning course with tensorflow training in Sydney to software engineers, data scientists, data analysts and statisticians who are interested in deep learning.
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Pre-requisites

Participants of the deep learning course with tensorflow training in Sydney should be familiar with the basics of programming, have a solid understanding of the basics of statistics and mathematics, and have a firm grasp on the vital learning areas of machine learning. Candidates with this deep learning course with tensorflow training in Sydney are highly preferred by the companies for the jobs.
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Course Content

  • Section 1 - Deep Learning with Tensor Flow (Self Learning)

    Preview

    • Lesson 1 - Welcome!

      02:06Preview
      • 1.1 Welcome!
        02:06
      • 1.2 Learning Objectives

    • Lesson 2 - Introduction to Tensorflow

      32:48Preview
      • 2.1 Learning Objectives
        07:00
      • 2.2 Introduction to TensorFlow
        07:00
      • 2.3 TF2x and Eager Execution
        04:21
      • 2.4 Tensorflow Hello World
      • 2.5 Linear Regression With Tensorflow
      • 2.6 Logistic Regression With Tensorflow
      • 2.7 Intro to Deep Learning
        02:39
      • 2.8 Deep Neural Networks
        11:48

    • Lesson 3 - Convolutional Networks

      21:51
      • 3.1 Learning Objectives
      • 3.2 Intro to Convolutional Networks
        04:37
      • 3.3 CNN for Classifications
        04:09
      • 3.4 CNN Architecture
        13:05
      • 3.5 Understanding Convolutions
      • 3.6 CNN with MNIST Dataset

    • Lesson 4 - Recurrent Neural Network

      24:43
      • 4.1 Learning Objectives
        03:06
      • 4.2 The Sequential Problem
        03:06
      • 4.3 The RNN Model
        05:28
      • 4.4 The LSTM Model
        05:25
      • 4.5 LTSM Basics
      • 4.6 Applying RNNs to Language Modeling
        07:38
      • 4.7 LSTM Language Modelling

    • Lesson 5 - Restricted Boltzmann Machines (RBM)

      14:14Preview
      • 5.1 Learning Objectives
        04:29
      • 5.2 Intro to RBMs
        04:29
      • 5.3 Training RBMs
        05:16
      • 5.4 RBM with MNIST

    • Lesson 6 - Autoencoders

      13:52
      • 6.1 Learning Objectives
        04:51
      • 6.2 Intro to Autoencoders
        04:51
      • 6.3 Autoencoder Structure
        04:10
      • 6.4 Autoencoders

    • Lesson 7 - Course Summary

      02:17
      • 7.1 Course Summary
        02:17
      • Unlocking IBM Certificate

  • Section 2 - Deep Learning with Keras and Tensor Flow (Live Classes)

    Preview

    • Lesson 1 - Course introduction

      03:11Preview
      • Introduction
      • Accessing Practice Lab
        03:11

    • Lesson 2 - AI and Deep learning introduction

      • What is AI and Deep learning
      • Brief History of AI
      • Recap: SL, UL and RL
      • Deep learning : successes last decade
      • Demo & discussion: Self driving car object detection
      • Applications of Deep learning
      • Challenges of Deep learning
      • Demo & discussion: Sentiment analysis using LSTM
      • Fullcycle of a deep learning project
      • Key Takeaways
      • Knowledge Check

    • Lesson 3 - Artificial Neural Network

      • Biological Neuron Vs Perceptron
      • Shallow neural network
      • Training a Perceptron
      • Demo code: Perceptron ( linear classification) (Assisted)
      • Backpropagation
      • Role of Activation functions & backpropagation
      • Demo code: Backpropagation (Assisted)
      • Demo code: Activation Function (Unassisted)
      • Optimization
      • Regularization
      • Dropout layer
      • Key Takeaways
      • Knowledge Check
      • Lesson-end Project (MNIST Image Classification)

    • Lesson 4 - Deep Neural Network & Tools

      • Deep Neural Network : why and applications
      • Designing a Deep neural network
      • How to choose your loss function?
      • Tools for Deep learning models
      • Keras and its Elements
      • Demo Code: Build a deep learning model using Keras (Assisted)
      • Tensorflow and Its ecosystem
      • Demo Code: Build a deep learning model using Tensorflow (Assisted)
      • TFlearn
      • Pytorch and its elements
      • Key Takeaways
      • Knowledge Check
      • Lesson-end Project: Build a deep learning model using Pytorch with Cifar10 dataset

    • Lesson 5 - Deep Neural Net optimization, tuning, interpretability

      • Optimization algorithms
      • SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
      • Batch normalization
      • Demo Code: Batch Normalization (Assisted)
      • Exploding and vanishing gradients
      • Hyperparameter tuning
      • Interpretability
      • Key Takeaways
      • Knowledge Check
      • Lesson-end Project: Hyperparameter Tunning With Keras Tuner

    • Lesson 6 - Convolutional Neural Network

      • Success and history
      • CNN Network design and architecture
      • Demo code: CNN Image Classification (Assisted)
      • Deep convolutional models
      • Key Takeaways
      • Knowledge Check
      • Lesson-end Project: Image Classification

    • Lesson 7 - Recurrent Neural Networks

      • Sequence data
      • Sense of time
      • RNN introduction
      • LSTM ( retail sales dataset kaggle)
      • Demo code: Stock Price Prediction with LSTM (Assisted)
      • Demo code: Multiclass Classification using LSTM (Unassisted)
      • Demo code: Sentiment Analysis using LSTM (Assisted)
      • GRUs
      • LSTM Vs GRUs
      • Key Takeaways
      • Knowledge Check
      • Lesson-end Project: Stock Price Forecasting

    • Lesson 8 - Autoencoders

      • Introduction to Autoencoders
      • Applications of Autoencoders
      • Autoencoder for anomaly detection
      • Demo code: Autoencoder model for MNIST data (Assisted)
      • Key Takeaways
      • Knowledge Check
      • Lesson-end Project: Anomaly detection with Keras

  • Section 3 - Practice Projects

    Preview

    • Practice Projects

      • PUBG Players Finishing Placement Prediction
  • Free Course

  • Math Refresher

    Preview

    • Lesson 01: Course Introduction

      06:23Preview
      • 1.01 About Simplilearn
        00:28
      • 1.02 Introduction to Mathematics
        01:18
      • 1.03 Types of Mathematics
        02:39
      • 1.04 Applications of Math in Data Industry
        01:17
      • 1.05 Learning Path
        00:25
      • 1.06 Course Components
        00:16

    • Lesson 02: Probability and Statistics

      32:38Preview
      • 2.01 Learning Objectives
        00:29
      • 2.02 Basics of Statistics and Probability
        03:08
      • 2.03 Introduction to Descriptive Statistics
        02:12
      • 2.04 Measures of Central Tendencies​
        04:50
      • 2.05 Measures of Asymmetry
        02:24
      • 2.06 Measures of Variability​
        04:55
      • 2.07 Measures of Relationship​
        05:22
      • 2.08 Introduction to Probability
        08:36
      • 2.09 Key Takeaways
        00:42
      • 2.10 Knowledge check

    • Lesson 03: Coordinate Geometry

      06:31Preview
      • 3.01 Learning Objectives
        00:35
      • 3.02 Introduction to Coordinate Geometry​
        03:16
      • 3.03 Coordinate Geometry Formulas​
        01:51
      • 3.04 Key Takeaways
        00:49
      • 3.05 Knowledge Check

    • Lesson 04: Linear Algebra

      29:53Preview
      • 4.01 Learning Objectives
        00:29
      • 4.02 Introduction to Linear Algebra
        03:21
      • 4.03 Forms of Linear Equation
        05:21
      • 4.04 Solving a Linear Equation
        05:21
      • 4.05 Introduction to Matrices
        02:05
      • 4.06 Matrix Operations
        07:07
      • 4.07 Introduction to Vectors
        01:00
      • 4.08 Types and Properties of Vectors
        01:52
      • 4.09 Vector Operations
        02:39
      • 4.10 Key Takeaways
        00:38
      • 4.11 Knowledge Check

    • Lesson 05: Eigenvalues Eigenvectors and Eigendecomposition

      08:56Preview
      • 5.01 Learning Objectives
        00:29
      • 5.02 Eigenvalues
        01:19
      • 5.03 Eigenvectors
        04:09
      • 5.04 Eigendecomposition
        02:21
      • 5.05 Key Takeaways
        00:38
      • 5.06 Knowledge Check

    • Lesson 06: Introduction to Calculus

      09:47Preview
      • 6.01 Learning Objectives
        00:30
      • 6.02 Basics of Calculus
        01:20
      • 6.03 Differential Calculus
        03:01
      • 6.04 Differential Formulas
        01:01
      • 6.05 Integral Calculus
        02:33
      • 6.06 Integration Formulas
        00:47
      • 6.07 Key Takeaways
        00:35
      • 6.08 Knowledge Check

Contact Us

1-800-982-536

( Toll Free )

Project

  • Project 1

    PUBG Players Finishing Placement Prediction

    Create a model that predicts players’ finishing placement based on their final stats, on a scale of 1 (first place) to 0 (last place).

  • Project 2

    Lending Club Loan Data Analysis

    Create a model that predicts whether a loan will go into default using the historical data.

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TensorFlow Exam & Certification

Deep Learning Certificate in Sydney

  • Who provides the certification and how long is it valid for?

    Once you complete of the deep learning course in Sydney with tensorflow training successfully, you will be awarded an industry-recognized course completion certificate from Simplilearn has lifelong validity. The deep learning course with tensorflow training in Sydney will help you to fetch a well-paid job.

  • What do I need to do to unlock my Simplilearn certificate?

    To obtain a deep learning course with tensorflow training in Sydney, you will need to:
    Attend one complete batch of this deep learning course with tensorflow training in Sydney.
    Finish and acquire an evaluation for atleast one of the projects mentioned below;. 
    After the completion of the course, you will be given a certification of a deep learning course with tensorflow training in Sydney.

  • What is the fee for the TensorFlow Developer certification exam?

    The deep learning course with tensorflow training in Sydney exam costs $100, which includes one exam attempt. The deep learning course with tensorflow training in Sydney will help you to expand your knowledge in this particular subject. 

  • What is the duration of the TensorFlow Developer certification exam?

    After you start the deep learning course with tensorflow training in Sydney, you have 5 hours to complete and submit it. However, if you do not submit a response within 5 hours, the portal will automatically send your response for the deep learning course with tensorflow training in Sydney after that time.

  • How many attempts do I have to pass the TensorFlow Developer certification exam?

    For the deep learning course with tensorflow training in Sydney, you will have three attempts to pass the deep learning course with tensorflow course in Sydney exam.  For the deep learning course with tensorflow course in Sydney, there are no more attempts you can try.

  • What are the system requirements for taking the TensorFlow Developer certification exam?

    To be able to appear for the deep learning course with tensorflow training in Sydney examination, here are the minimum requirements that you need to have:

    • RAM - 4 GB
    • Disk Space - 2.5 GB and another 1 GB for caches
    • Monitor resolution - 1024 x 768
    • Operating system - officially released 64-bit versions of
    • macOS 10.13 or subsequent upgrades, Microsoft Windows 8 or future versions, or any Linux distribution supporting tools including Gnome, KDE, or Unity DE.
    • These are the minimum requirements for the deep learning course with tensorflow training in Sydney examination

  • What are the benefits of taking the TensorFlow Developer certification exam?

    Here are the benefits of taking the deep learning course with tensorflow training in Sydney:

    • Learn new things about Machine Learning - This deep learning course with tensorflow training in Sydney exam will help you increase your proficiency in Machine Learning.
    • Receive recognition - Once you are certified, it is imperative that you will be recognized by the TensorFlow community.
    • Showcase skills - The deep learning course with tensorflow training in Sydney is internationally recognized and attests to the knowledge that you possess and as an extension also is proof of the skills you’ve practiced and perfected with this course.
    • The deep learning course with tensorflow training in Sydney also helps one to build their passion for this subject.

  • How do I crack the Tensorflow Developer certification exam?

    The best way to pass the deep learning course with tensorflow training in Sydney exam is to take this deep learning course with tensorflow training in Sydney. After completing the deep learning course with tensorflow training in Sydney, you will be able to register and take the TensorFlow developer certification exam. There will be five categories during the exam, and students will complete five models, one for each category. These categories include basic ML models, models from learning data sets, CNNs with real image data sets, NLP text classification with real text data sets, and sequence models with real numerical data sets. This exam can be taken on a system that supports the requirements of PyCharm IDE.

Deep Learning Training Reviews

  • A.Anthony Davis

    A.Anthony Davis

    Kingston

    The Simplilearn Data Scientist Master’s Program is an awesome course! You learn how to solve real-world problems, and the wide variety of projects give you hands-on experience to make you industry-ready. The lecturers are experts and share their knowledge energetically. Thank you for an excellent learning experience.

  • Abhishek Tripathi

    Abhishek Tripathi

    Bangalore

    Good online content for data science. I completed Data Science with R and Python. The instructors have good knowledge on the subject. Self-learning videos help a lot, too. Thanks, Simplilearn.

  • Angiras Modak

    Angiras Modak

    Associate System Engineer at IBM India Pvt. Ltd., Kolkata

    Simplilearn is one of the best online training providers available. The trainer was really great in explaining the concepts to the minute detail and also gave multiple real-world examples. The course content was very informative. I understood the concept of CNN. Overall I really enjoyed the training a lot.

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Why Online Bootcamp

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

Deep Learning Training FAQs

  • What is the salary of a Professional with Deep Learning with Tensorflow in Sydney?

    Machine learning includes deep learning as a subset.A machine learning engineer can expect an average annual base salary of AU$76645. Completing a course of deep learning with TensorFlow training in Sydney will add to your professional skills.

  • What are the major companies hiring for Deep Learning with Tensorflow in Sydney?

    Machine learning engineers can apply for many jobs in the city. Companies like Commonwealth Bank, Studiosity, and Correlate Resources offer machine learning engineers jobs in Sydney. After completing deep learning with TensorFlow training in Sydney, one can apply for related jobs.

  • What are the major industries in Sydney?

    Mining, oil refining, tourism, and finance are some of the major industries in Sydney. Information technology is part of the service sector and a major employer in the city. Individuals who have completed deep learning with TensorFlow training in Sydney can look for relevant job positions in the city.

  • How to become a deep learning engineer in Sydney?

    To become a deep learning or machine learning engineer, one needs to have a master's degree. Many individuals also go for PhDs. You can begin learning deep learning on your own, complete deep learning with TensorFlow training in Sydney, and take up internships for work experience.

  • How to Find Deep Learning with Tensorflow Courses in Sydney?

    If you're looking for deep learning with TensorFlow training in Sydney, this is the course for you. You can opt for offline or online mediums. You will also have to pay attention to the eligibility requirements before registering for any particular course.

  • What is Deep Learning?

    Deep Learning, also known as Deep Neural Learning, is a subset of machine learning, an application of AI, where machines imitate the workings of the human brain and employ artificial neural networks to process the information.

  • What is TensorFlow?

    TensorFlow is an open source library created and released by google for numerical computation and building deep learning models.

  • Why is Deep Learning important?

    Companies are gathering a massive amount of data every day and analyzing them to draw meaningful business insights. Most of that data is in an unstructured format, i.e. in the form of text, image, audio, and video rather than numerical. Deep learning is quite effective for analyzing such types of data and has become vitally important for business decision making. With our Deep Learning Training with TensorFlow & Keras certification, you can learn all the essential deep learning concepts from scratch.

  • Why should I learn Deep Learning?

    In traditional machine learning, most of the applied features need to be identified by a domain expert in order to reduce the complexity of the data. Whereas the biggest advantage of the Deep Learning algorithm is it tries to learn high-level features from data in an incremental manner, which makes the process simpler and popular. Deep Learning techniques outperform other techniques when the data size is large and complex, and also, this technique is behind many high-end innovations.

  • How do I become a Deep Learning Engineer?

    This Deep Learning course with Keras and TensorFlow certification training will give you a complete overview of  Deep Learning concepts, enough to prepare you to excel in your next role as a Deep Learning Engineer. Deep Learning Training will help you become familiar with artificial neural networks, PyTorch, autoencoders, and more. At the end of our best deep learning course online, you will get an industry-recognized course completion certificate from Simplilearn, which will be a testament to your skills with deep learning specialization.

    Unlock New Frontiers: Discover More Tensorflow Courses for Career Growth.

  • What kind of careers can I pursue with a background in Deep Learning?

    With the relevant skills that you gain from our Deep Learning course, you can apply for top job roles like Machine Learning Engineer, Data Scientist, Business Intelligence Developer, NLP Scientist, and more.

  • Why should you take this Deep Learning course?

    Deep learning skills are in high demand and offer professionals a clear edge over others when applying for top related job roles like Machine Learning Engineer, Data Scientist, or NLP Specialist. Requiring a high level of technical understanding, one may not find it easy to learn deep learning through self-study. Taking up this Deep Learning course is a better option where you get the right guidance from industry experts.

  • What is online classroom training?

    All of the TensorFlow training classes are conducted via live online streaming. These classes for the TensorFlow course are interactive sessions that enable you to ask questions and participate in discussions during class time.

  • Who are the instructors and how are they selected?

    All of our highly qualified trainers are Deep Learning and Machine Learning industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

  • How will the labs be conducted?

    Simplilearn provides Integrated labs for all the hands-on execution of projects. The learners will be guided on all aspects, from deploying tools to executing hands-on exercises.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in TensorFlow in your first attempt. They engage students proactively to ensure the Deep Learning Course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.  

  • Is this live training or will I watch pre-recorded videos?

    The TensorFlow certification training is conducted through live streaming. They are interactive sessions that enable you to ask questions and participate in discussions during class time. We do, however, provide recordings of each TensorFlow course session you attend for your future reference. Classes are attended by a global audience to enrich your learning experience.

  • What if I miss a class?

    Simplilearn provides recordings of each class of Deep Learning course so you can review them as needed before the next session. With Flexi-pass, Simplilearn gives you access to all classes for 90 days so that you have the flexibility to choose sessions as per your convenience.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your Deep Learning course online with us.

  • How do I enroll for the Deep Learning course?

    You can enroll for this Deep Learning Training on our website and make an online payment using any of the following options: 

    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal

    Once payment is received you will automatically receive a payment receipt and access information via email.

  • If I need to cancel my enrollment, can I get a refund?

    Yes, you can cancel your enrollment if necessary. We will refund the Deep Learning course price after deducting an administration fee. To learn more, please read our Refund Policy.

  • How can I learn more about this Deep Learning course?

    Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide you with more details.

  • What is the recommended learning path after completing a Deep Learning course?

    You can either enroll in our Artificial Intelligence certification course or if you are looking to get the University certificate, you can enroll in the Post Graduate Program in AI and Machine Learning.

Deep Learning Course (with Keras & TensorFlow) in Sydney

Sydney can be found on the east coast of Australia and is the capital of New South Wales. Sydney ranked eleventh among the best cities in the world for economic opportunity in 2012. The most populous city in the country, Sydney has been welcoming immigrants for years. The gross regional product (GRP) of the city in 2019 was 131,381. The city's economic growth is also due to the IT sector, and courses like deep learning with TensorFlow training in Sydney are popular in the city.

The city's climate is such that the inland area sees intense heat exposure while the temperature remains moderate near the coast.

Sydney is among the fifteen most visited tourist cities around the world. One of Australia's most visited tourist destinations, several tourist attractions can be seen as you complete deep learning with TensorFlow training in Sydney:

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  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

This course has been upgraded to

Post Graduate Program in AI and Machine Learning

  • Program completion certificate from Purdue University and Simplilearn
  • Gain exposure to ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools
  • Live interactive sessions on the latest AI trends, such as ChatGPT, generative AI, prompt engineering, and more