This lecture, by DeepMind Research Scientist Felix Hill, first discusses the motivation for modelling language with ANNs: language is highly contextual, typically non-compositional and relies on reconciling many competing sources of information. This section also covers Elman's Finding Structure in Time and simple recurrent networks, the importance of context and transformers. In the second part, he explores unsupervised and representation learning for language from Word2Vec to BERT. Finally, Felix discusses situated language understanding, grounding and embodied language learning.

Download the slides here:
https://storage.googleapis.com..../deepmind-media/UCLx

Find out more about how DeepMind increases access to science here:
https://deepmind.com/about#access_to_science

Speaker Bio:

Felix Hill is a Research Scientist working on grounded language understanding, and has been at DeepMind for almost 4 years. He studied pure maths as an undergrad, then got very interested in linguistics and psychology after reading the PDP books by McClelland and Rumelhart, so started graduate school at the University of Cambridge, and ended up in the NLP group. To satisfy his interest in artificial neural networks, he visited Yoshua Bengio's lab in 2013 and started a series of collaborations with Kyunghyun Cho and Yoshua applying neural nets to text processing. This led to some of the first work on transfer learning with sentence representations (and a neural crossword solver). He also interned at FAIR in NYC with Jason Weston. At DeepMind, he's worked on developing agents that can understand language in the context of interactive 3D worlds, together with problems relating to mathematical and analogical reasoning.

About the lecture series:

The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. Over the past decade, Deep Learning has evolved as the leading artificial intelligence paradigm providing us with the ability to learn complex functions from raw data at unprecedented accuracy and scale. Deep Learning has been applied to problems in object recognition, speech recognition, speech synthesis, forecasting, scientific computing, control and many more. The resulting applications are touching all of our lives in areas such as healthcare and medical research, human-computer interaction, communication, transport, conservation, manufacturing and many other fields of human endeavour. In recognition of this huge impact, the 2019 Turing Award, the highest honour in computing, was awarded to pioneers of Deep Learning.

In this lecture series, research scientists from leading AI research lab, DeepMind, deliver 12 lectures on an exciting selection of topics in Deep Learning, ranging from the fundamentals of training neural networks via advanced ideas around memory, attention, and generative modelling to the important topic of responsible innovation.

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Machine learning using neural networks is a very powerful methodology which has demonstrated utility in many different situations. In this talk I will show how work in the mathematical discipline called topological data analysis can be used to (1) lessen the amount of data needed in order to be able to learn and (2) make the computations more transparent. We will work primarily with image and video data.

This talk was part of the workshop on "Topological Data Analysis - Theory and Applications" supported by the Tutte Institute and Western University: https://math.sci.uwo.ca/~jardine/TDA-2021.html

IBM course
Deep Learning Fundamentals

IBM free certification course
All Quiz Answer
Course Quiz Solve

Course link:

https://courses.cognitiveclass.....ai/courses/course-v

notes: https://github.com/ShapeAI/Pyt....hon-and-Machine-Lear

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In this part, we're going to cover how to actually use your model. We will us our cats vs dogs neural network that we've been perfecting.

Text tutorial and sample code: https://pythonprogramming.net/....using-trained-model-

Dog example: https://pythonprogramming.net/....static/images/machin

Cat Example: https://pythonprogramming.net/....static/images/machin

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** Tensorflow Training: https://www.edureka.co/ai-deep....-learning-with-tenso **
This video on "Deep Learning with Python" will provide you with detailed and comprehensive knowledge of Deep Learning, How it came into emergence. The various subparts of Data Science, how they are related and How Deep Learning is revolutionalizing the world we live in. This Tutorial covers the following topics:

1:49 Introduction To AI, ML, and DL
2:19 What is Deep Learning
7:30 Applications of Deep Learning
13:27 Structure of Perceptron
16:31 Demo: Perceptron from scratch
23:52 What is a Neural Network ?
26:35 Demo: Creating Deep Neural Nets

Deep Learning blog series: https://bit.ly/2xVIMe1
Deep Learning With TensorFlow Playlist: https://goo.gl/cck4hE

PG in Artificial Intelligence and Machine Learning with NIT Warangal : https://www.edureka.co/post-gr....aduate/machine-learn

Post Graduate Certification in Data Science with IIT Guwahati - https://www.edureka.co/post-gr....aduate/data-science-
(450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies)

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How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

- - - - - - - - - - - - - -

About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.


- - - - - - - - - - - - - -

Who should go for this course?

The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'

2. Analytics Managers who are leading a team of analysts

3. Business Analysts who want to understand Deep Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. Professionals who want to captivate and analyze Big Data

6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

- - - - - - - - - - - - - -

Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free).

#shorts #machinelearning #deeplearning #chatgpt #neuralnetwork #transformers

Torsten Hoefler presents an overview of sparsity in deep learning. Check the markers for various parts of the talk.

arXiv: https://arxiv.org/abs/2102.00554

Chapters:
0:00 Introduction to deep learning
7:29 Introduction to hardware scaling and locality
11:25 Overview of model compression and optimization
12:47 Introduction to sparsification
18:17 Overparameterization, SGD dynamics, and Occam's hill and generalization
26:00 Sparse storage formats and representational overheads
31:10 Overview of sparsification techniques - model and ephemeral sparsity
35:13 Sparsification schedules - when to sparsify
41:36 Fully sparse training
47:53 Retraining example
50:35 How to sparsify - picking elements for removal
54:50 Data-free pruning - magnitude
56:52 Data-driven pruning - sensitivity, activity, and correlation
59:42 Training-aware pruning - Taylor expansions of the loss and regularization
1:09:19 Learnable gating functions (approximations)
1:12:49 Structured sparsification
1:15:34 Variational removal methods
1:18:08 Parameter budgets between layers and literature statistics
1:22:10 Re-growing elements in fully-sparse training
1:24:53 Ephemeral sparsity - activations, gradients, dynamic networks
1:33:07 Putting everything together - case studies with CNNs
1:36:39 Parameter efficiency and slack
1:41:03 Compute efficiency and sparse transformers
1:43:41 Acceleration for sparse deep learning
1:50:04 Lottery tickets and sparse subnetworks
1:53:37 Best practices for sparse deep learning
1:56:06 Open research questions and summary

Abstract: The growing energy and performance costs of deep learning have driven the community to reduce the size of neural networks by selectively pruning components. Similarly to their biological counterparts, sparse networks generalize just as well, if not better than, the original dense networks. Sparsity can reduce the memory footprint of regular networks to fit mobile devices, as well as shorten training time for ever growing networks. In this paper, we survey prior work on sparsity in deep learning and provide an extensive tutorial of sparsification for both inference and training. We describe approaches to remove and add elements of neural networks, different training strategies to achieve model sparsity, and mechanisms to exploit sparsity in practice. Our work distills ideas from more than 300 research papers and provides guidance to practitioners who wish to utilize sparsity today, as well as to researchers whose goal is to push the frontier forward. We include the necessary background on mathematical methods in sparsification, describe phenomena such as early structure adaptation, the intricate relations between sparsity and the training process, and show techniques for achieving acceleration on real hardware. We also define a metric of pruned parameter efficiency that could serve as a baseline for comparison of different sparse networks. We close by speculating on how sparsity can improve future workloads and outline major open problems in the field.

🔥 Deep Learning Training - TensorFlow Certification(𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎): https://www.edureka.co/ai-deep....-learning-with-tenso
🔹You can find the code here : https://bit.ly/33WkmSl 🔹
This Edureka video on "𝐂𝐎𝐕𝐈𝐃 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐔𝐬𝐢𝐧𝐠 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 " will provide you with a comprehensive and detailed knowledge of Image classification and how it can be implemented using a Convolutional Neural Network. In this video, you will be working on image processing with Python and also will learn about the convolutional neural network. Finally, we will build an end-to-end model to process and identify the Covid X-Ray images with CNN. Below are the topics covered in this COVID Detection Using Deep Learning video :
00:00:00 Introduction
00:00:47 Why CNN?
00:04:55 What is CNN?
00:07:00 Image Processing Using CNN
00:11:00 CNN For Covid Detection

🔹Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
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#Edureka #EdurekaDeepLearning #COVID19Detection #PneumoniaPrediction #DeepLearningTutorial #EdurekaTraining #Python

---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧---------

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---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬---------

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--------------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐆𝐏 𝐂𝐨𝐮𝐫𝐬𝐞𝐬---------------

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----------------------------
How it Works?
1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!

----------------------------
About the Course :
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

----------------------------
Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.
For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free)

#pytorch #pytorch3d #3ddeeplearning #deeplearning #machinelearning

In this video, I try the 3D Deep Learning tutorials from Pytorch 3D. Join me and learn a bit about 3D Deep Learning.

✅ Recommended Courses
(Includes Amazon Affiliate Links)
==================================
⭐ Python for Everybody 👉🏼 https://coursera.pxf.io/mgJKke
⭐ Machine Learning Specialization with Andrew Ng 👉🏼 https://imp.i384100.net/KemLOy
⭐ Learn Data Science with Coursera Plus 👉🏼 https://imp.i384100.net/c/3326428/1320998/14726

✅ Recommended Playlists
==================================
#️⃣ Wildlife Detection with the Raspberry PI 👉🏼 https://youtube.com/playlist?l....ist=PL3OV2Akk7XpA_Jh
#️⃣ Tensorflow Style Transfer Tutorial and Examples 👉 https://youtube.com/playlist?l....ist=PL3OV2Akk7XpDmz2
#️⃣ 3D Deep Learning 👉🏼 https://youtube.com/playlist?l....ist=PL3OV2Akk7XpBPBE
#️⃣ Creating a 3D model of a Photo using Python and Numpy 👉🏼 https://youtube.com/playlist?l....ist=PL3OV2Akk7XpDs8u
#️⃣ Python Problems for Beginners 👉🏼 https://youtube.com/playlist?l....ist=PL3OV2Akk7XpC-fs
#️⃣ Python Bot Development For Beginners 👉🏼 https://youtube.com/playlist?l....ist=PL3OV2Akk7XpBwRE
#️⃣ Web Scraping Projects 👉🏼 https://youtube.com/playlist?l....ist=PL3OV2Akk7XpBUHg
#️⃣ Blender Python API 👉🏼 https://youtube.com/playlist?l....ist=PL3OV2Akk7XpBlVn

✅ My Favorite Tech
(Includes Amazon Affiliate Links)
==================================
💻 Ryzen 5950 and RTX 3090 PC for Gaming And ML 👉🏼 https://amzn.to/3Pz5saF
🖥 Gigabyte RTX 3090 Ti 👉🏼 https://amzn.to/3cxKN8l
🖥 Gigabyte RTX 3080 Ti 👉🏼 https://amzn.to/3BeRZjX
🖥 Gigabyte RTX 3070 Ti 👉🏼 https://amzn.to/3z3ZpUC
🖥 Gigabyte RTX 3060 👉🏼 https://amzn.to/3BmEpLk
👨🏼‍💻 M2 Macbook Pro 8GB 👉🏼 https://amzn.to/3OwPKve
👨🏼‍💻 M2 Macbook Air 8GB 👉🏼 https://amzn.to/3PNA7Ri
📟 Raspberry PI 👉 🏼https://amzn.to/3z4wKyP
📟 Raspberry Pi HQ Camera 👉🏼 https://amzn.to/3BirRVo
🖲 Web Camera 👉🏼 https://amzn.to/3v8inIs
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🏢 Standing Desk 👉🏼 https://amzn.to/3Be5ssg
📗 Deep Learning Must Read 👉🏼 https://amzn.to/3OvbCHI

✅ My RTX 3070 and Ryzen 5900 Machine Learning PC Build
(Includes Amazon Affiliate Links)
=======================================================
#️⃣ Watch My First Deep Learning PC Build 👉🏼 https://youtube.com/playlist?l....ist=PL3OV2Akk7XpBQBa
#️⃣ AMD Ryzen 5900X CPU 👉🏼 https://amzn.to/3z6K9Gm
#️⃣ Gigabyte GeForce RTX 3070 EAGLE OC 8GB Graphics Card 👉🏼 https://amzn.to/3BeWTgM
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#️⃣ Corsair iCUE H150i PRO XT RGB Liquid CPU Cooler (360mm) 👉🏼 https://amzn.to/3b3w44w
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#️⃣ PC Case - Phanteks Eclipse P600S 👉🏼 https://amzn.to/3Pw34BB
#️⃣ Memory - Corsair Vengeance RGB Pro 32GB (2x16GB) DDR4 3600- Black 👉🏼 https://amzn.to/3owSSwA
#️⃣ Power Supply - CORSAIR CX750F RGB - 750 Watt, Fully Modular 👉🏼 https://amzn.to/3ophd7E

✅ MY EQUIPMENT FOR YOUTUBE
(Includes Amazon Affiliate Links)
==================================
📸 Sony Vlog Camera ZV1 👉🏼 https://amzn.to/3BinAkO
🎤 Rode Wireless GO 👉🏼 https://amzn.to/3PNA7AQ
🎤 Sennheiser Mke 400 Mic 👉🏼 https://amzn.to/3aYxvBv
💡 Neewer 3 Packs 530 RGB Led Light 👉🏼 https://amzn.to/3J20wZn
🏢 Manfrotto Variable Friction Arm with Bracket, Super Clamp 👉🏼https://amzn.to/3IZILdr

As an Amazon Associate, I earn a small commission from qualifying purchases on the amazon links above. It costs you nothing but helps me in keeping the content coming.

✅ BUSINESS ENQUIRIES

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✅ Timestamps
0:00 Introduction
1:00 Heterogeneous Batching
1:40 3D Operators
3:56 Differential Renderer
4:33 pytorch3d.datasets
5:54 Google Colab: Deform a source mesh to form a target mesh using 3D loss functions
6:52 Loss functions/regularizers: Chamfer distance, edge loss, laplacian smoonthing, normal consistency
8:58 Using Stochastic Gradient Descent for Optimization loop
13:38 Opening the reconstructed mesh in Blender (Dolphin)
16:00 Trying Pytorch3D's visualization tools for Plotly
22:27 Fit a mesh using Pytorch3D's differential renderer

Ever wondered what those monster deep learning models are built from, lots of these.

Oh, and don't forget to connect with me!
LinkedIn: https://bit.ly/324Epgo
Facebook: https://bit.ly/3mB1sZD
GitHub: https://bit.ly/3mDJllD
Patreon: https://bit.ly/2OCn3UW
Join the Discussion on Discord: https://bit.ly/3dQiZsV

Happy coding!
Nick

P.s. Let me know how you go and drop a comment if you need a hand!
#dalle2 #ai

Prof. Geoffrey Ye Li (Imperial College London)

It has been demonstrated recently that deep learning (DL) has great potential to break the bottleneck of conventional communication systems. In this talk, we present our recent work in DL in wireless communications, including physical layer processing and resource allocation.

DL can improve the performance of each individual (traditional) block in a conventional communication system or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL-based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN).

Judicious resource (spectrum, power, etc.) allocation can significantly improve the efficiency of wireless networks. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. Deep learning represents a promising alternative due to its remarkable power to leverage data for problem-solving and can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to reduce the complexity of mixed-integer non-linear programming (MINLP). We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with applications in vehicular networks.

Bio:
Dr. Geoffrey Ye Li is the Chair Professor in Wireless Systems in Department of EEE, Imperial College London. Before joining Imperial College London in 2020, he was a professor with Georgia Institute of Technology, GA, USA, for 20 years and a Principal Technical Staff Member with AT&T (Bell) Labs – Research in New Jersey, USA, for around 5 years. He is currently focusing on machine learning and statistical signal processing for wireless communications. His research topics in the past couple decades include machine learning for wireless signal detection and resource allocation, cognitive radios, cross-layer optimisation for spectrum- and energy-efficient wireless networks, OFDM and MIMO techniques for wireless systems, and blind signal processing.

Dr. Geoffrey Ye Li was awarded IEEE Fellow for his contributions to signal processing for wireless communications in 2005. He won several prestigious awards from IEEE Signal Processing Society (Donald G. Fink Overview Paper Award in 2017), IEEE Vehicular Technology Society (James Evans Avant Garde Award in 2013 and Jack Neubauer Memorial Award in 2014), and IEEE Communications Society (Stephen O. Rice Prize Paper Award in 2013, Award for Advances in Communication in 2017, and Edwin Howard Armstrong Achievement Award in 2019). He also received the 2015 Distinguished ECE Faculty Achievement Award from Georgia Tech. He has been recognised as the Highly-Cited Researcher by Thomson Reuters almost every year.

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Wireless ML Seminars is a series of lectures focused on Machine Learning in the wireless space. Invited speakers for the series are leaders in their fields, hailing from respected research institutions worldwide. The seminar series is curated by Prof. Jeff Andrews and Prof. Hyeji Kim from the Wireless Networking and Communications Group at the University of Texas at Austin.

Find out more here: https://sites.utexas.edu/wirelessmlseminars/

Special Thanks To Ashish Patel And All The Bloggers For the Amazing Contribution
https://www.linkedin.com/in/ashishpatel2604/
https://github.com/ashishpatel....26/500-AI-Machine-le
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Starter In Data Science

1 Complete Machine Learning Playlist:(Top 24 videos)

https://www.youtube.com/playli....st?list=PLZoTAELRMXV

2 Statistics in Machine Learning:(Understand some Concepts With Respect To Data)- Complete Playlist
https://www.youtube.com/playli....st?list=PLZoTAELRMXV

3. Feature Engineering(Complete Playlist)
https://www.youtube.com/playli....st?list=PLZoTAELRMXV

4. Continue The Complete Machine Learning Playlist(24-all the videos)

5. Live Stream Playlist:(Top 10 videos)
https://www.youtube.com/playli....st?list=PLZoTAELRMXV

6. Machine Learning Pipelines

https://www.youtube.com/playli....st?list=PLZoTAELRMXV

7. Complete Deep Learning Playlist:
Tensorflow And Keras-https://www.youtube.com/playli....st?list=PLZoTAELRMXV

Pytorch: https://www.youtube.com/playli....st?list=PLZoTAELRMXV

8. Live Projects Playlist:
https://www.youtube.com/playli....st?list=PLZoTAELRMXV
9. Live stream Playlist: https://www.youtube.com/playli....st?list=PLZoTAELRMXV
10.Docker Playlist: https://www.youtube.com/playli....st?list=PLZoTAELRMXV
11. Mongodb: https://www.youtube.com/playli....st?list=PLZoTAELRMXV
12. Machine Learning Interviews: https://www.youtube.com/playli....st?list=PLZoTAELRMXV

(Data Science Certification: https://www.edureka.co/data-sc....ience-r-programming- )
This Edureka video on "Best Laptops for Machine Learning" will provide you the detail and comprehensive knowledge about the best laptops that you can use for machine learning.

Below is the Link to Laptops

TensorBook: https://lambdalabs.com/deep-le....arning/laptops/tenso
MacBook: https://www.apple.com/shop/buy-mac/macbook-pro
Asus ROG Strix GL702VS: https://www.asus.com/Laptops/ROG-GL702VS/
ASUS ROG Zephyrus S: https://www.asus.com/Laptops/ROG-Zephyrus-S-GX531/
Dell XPS 15 9560: https://www.dell.com/en-in/sho....p/laptops-2-in-1-pcs
Razer Blade 15: https://www.razer.com/gaming-laptops/razer-blade
MSI GS65: https://www.msi.com/Laptop/GS65-Stealth-Thin-8RF
Acer Predator (Helios 300 and Triton 700): https://www.acer.com/ac/en/US/....content/predator-ser

Subscribe to our channel to get video updates. Hit the subscribe button above.

Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm

#Edureka #EdurekaBestlaptop #EdurekaDataScience
#MachineLearning #MachineLearning #DataScience

How it Works?
1. This is a 30-hour Instructor-led Online Course.

2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.

3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate!

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About the Course

Edureka's Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR

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Who should go for this course?

The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for:
Developers aspiring to be a 'Data Scientist'
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Machine Learning (ML) Techniques
Information Architects who want to gain expertise in Predictive Analytics
'R' professionals who wish to work Big Data
Analysts wanting to understand Data Science methodologies

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Why learn Data Science?
Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modelling, statistics and analytics. To take complete benefit of these opportunities, you need a structured training with an updated curriculum as per current industry requirements and best practices.
Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes.
Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges.
.

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If you are looking for live online training, write back to us at sales@edureka.in or call us at US: + 18338555775 (Toll-Free) or India: +91 9606058406 for more information

Welcome to a tutorial where we'll be discussing Convolutional Neural Networks (Convnets and CNNs), using one to classify dogs and cats with the dataset we built in the previous tutorial.

Text tutorials and sample code: https://pythonprogramming.net/....convolutional-neural

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