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Training Courses


1. Introduction to TensorFlow – 1 day

TensorFlow has become the most popular deep learning framework since Google open sourced it in November 2015. Find out what it can do for your business in this gentle overview of deep neural networks and the TensorFlow framework. This is a high level business focussed introduction to TensorFlow and, as such, there will be no hands on element to this course, although you can expect the odd code snippet to appear in a couple of the slides.


2. Hands-on with TensorFlow – 3 day
Since Google open-sourced their Deep Learning framework TensorFlow, it has become a very popular tool for Data Scientists and Machine Learning Experts. In this course we will explore the various facets of TensorFlow – expect a 50% lab content with this course, both locally on your laptop, and in the cloud.


3. TensorFlow Deep Dive – 5 day 

In this intensive five day hands on TensorFlow course, expect a lot of labs (approximately 80/20 lab/theory). We will explore, through theory and labs, how TensorFlow is being used to classify images, recognise text and speech, and generate images and language. We will explore this using a variety of image, text, speech and video data sets. Labs will be done both locally and in the cloud using both GPU's and TPU's.


4. Data Science Overview – 1 day
A one day overview of the main concepts and technologies underlying the practice of data science – no maths or coding required. We will cover the basics from the business case to what technologies to use and when to use them and how they are being used in businesses today to drive innovation and efficiencies. From Hadoop to Spark from machine learning to AI.

5. Fundamentals of Data Science – 5 day
Consisting of a 50/50 split of concepts and labs, this five day data science course explores all of the main aspects of data science. Going into much more depth than our one day overview course in terms of both theory and practice, topics covered include data collection and cleaning, and data analysis using frameworks such as Spark and TensorFlow. All labs are developed and performed using python.

6. Julia for Data Science – 3 day
In this three day hands on course, we will explore Julia’s capabilities and see why it has fast become such a favoured language for doing data science. It contains a 70% hands on lab element where we will be analyzing real data sets using Julia and its libraries. Several business use cases will be analysed.

7. Spark Overview – 3 day
In this course we will take a look at Apache Spark along with some of the other streaming technologies being used in production, such as Beam and Flink. There will be approximately 40% hands on labs using Spark as we use real data sets to explore both batch and streaming use cases.

8. Spark Deep Dive – 5 day
This course will give a broad overview of the Spark framework as well as diving deep into the various capabilities through hands on lab exercises (approx. 70%). Real data sets will be analysed using the Spark framework. By the end of this course, participants will be able to design and implement a Spark framework to analyse a variety of data sets across distributed clusters.

9. Programming for Data Science (R, Python and Julia) – 5 day
We will look at each of the three main programming languages used in data science and explore how they are being used in practice. This course will consist of a 70/30 mix of practice and theory with about one third of the course being assigned to each language. Labs will be performed using Jupyter notebooks.

10. Practical AI with GPU’s – 2 day
Massive (e.g., petabyte scale) data sets require massively parallel processing in order to do timely analysis. GPU’s are purpose built for this task, and in this practical hands on course, we will learn how to programme them to extract useful information. We will also look at the how TPU's are being used in the Google Cloud Platform. 50/50 practice/theory.

11. Natural Language Processing in Practice – 3 day
In this course we will look at the techniques and analysis of natural language processing (NLP), including speech and text recognition and translation. We will learn how to use some of the libraries and frameworks such as word2vec, TensorFlow and RNN’s to analyse language data streams in practice. 40/60 theory/practice.

12. Haskell for AI – 3 day
If you want to implement AI solutions, it pays to know Haskell, or at least one functional programming language pretty well. In this course we set out to learn the basics of Haskell, especially as applied to programming AI. There will be an approximately 50% lab element as we explore real data sets.

13. Introduction to Quantum Computing – 1 day
In this beginners course, we will take an overview of quantum computing including a little bit of theory (quantum physics without too much maths), hardware, principles, quantum algorithms and how they differ from classical machine learning algorithms, as well as looking at quantum computing services available on the market today (D-wave and cloud). We will also look at some QC use cases.
 
14. Quantum Computing Deep Dive – 3 day
We will look at the theory behind quantum computing as well as the practical aspects of building and actually programming with a quantum computer. There will be a hands on element whereby we will use a quantum computer simulator to programme a selection of quantum algorithms. Familiarity with the Linux command line as well as one programming language such as Python or C++ is required. There will be a roughly 50/50 split theory/hands on.


15. Introduction to Neuromorphic Computing – 2 day

Neuromorphic computing is an emerging computing paradigm, which uses an analog processor for spiking neural networks, much the way the brain does computations. Neuromorphic processors utilise massively parallel computations in their synaptic connections between the artificial neurons and run at very low power (1000x reduction in power consumption over CPU's, for example). Despite sounding esoteric, this technology is seeing commercial application in companies and government organisations today. In this two day overview course, we will examine the technology from theory to practice, survey the past and present research and product landscapes, take a look at the various product offerings available today and the differences between them, and cover some case studies to show how companies are already reaping performance benefits from this exciting new technology. Basic python programming is required.


16. Introduction to Bayesian Statistics – 2 day
Starting off with a comparison between Bayesian and classical statistics, this course introduces the participant to the basics of Bayesian statistics including Bayesian probability and inference. It will cover theory and practice with some hands on labs in python so that the student can get experience with analyzing actual data sets using Bayesian methods.


17. TensorFlow Bootcamp - 12 weeks

12 week bootcamp covering the most popular deep learning framework open sourced from Google.

Week 1 - Basics of deep learning - math, data sets, hardware (CPU, GPU, ASIC, cloud), mathematical foundations of neural networks, deep learning frameworks

Week 2 - Basic Convolutional Neural Networks with TensorFlow

Week 3 - Advanced CNNs

Week 4 - Natural Language Processing and Time Series Data with TensorFlow

Week 5 - Basic Recurrent Neural Networks

​Week 6 - Advanced RNNs and LSTMs

Week 7 - Deep Reinforcement Learning with TensorFlow

Week 8 - Advanced Topics in Deep Learning (including GANs, one-shot learning and transfer learning)

Weeks 9-12 - Capstone Project - Students work with a company on a commercial deep learning project with the view to getting hired upon completion

​Prerequisites: Basic math (linear algebra, calculus and probability), basic programming skills, especially python, comfortable at the command line. Price £10k.

Consulting & SERVICES


Deep Learning Partnership offer a range of consulting and training services. Deep Learning technologies used include computer vision and natural language processing frameworks such as TensorFlow, Keras and MXnet. We have deep experience in growing and managing agile data science and data engineering teams along with building key partner relationships and managing client expectations. Deep Learning Partnership design and implement AI solutions for our Enterprise and startup clients across all business domains. Our training courses below reflect our Consultants' wide range of AI expertise. Contact us about any of these or, if you are a Company, we can arrange bespoke training to suit where you are on your AI journey. Please contact us to find out more at pmorgan@deeplp.com.

Business Consulting

 

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