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Holy Communion by Joseph Prince 121gamers 2 Tampilan • 2 bertahun-tahun yang lalu

Hear more from Joseph Prince:
https://www.tbnplus.com/c/sns/7YMNGNYO

In this powerful sermon, renowned pastor Joseph Prince invites believers to experience miraculous healing through the power of Holy Communion and the blood of Jesus. As you watch this video, you'll discover how partaking in the Holy Communion can unleash divine healing in your body, no matter what health challenges you may be facing.

Joseph Prince emphasizes the importance of trusting in the finished work of Jesus on the cross. By His stripes, we are healed, and through His grace, we can experience renewed strength and vitality. As you listen to this inspiring message, you'll learn how to approach the Holy Communion with faith and expectancy, believing that God has provided a righteous foundation for your healing through the sacrifice of His Son.

Throughout this short sermon clip, Joseph Prince leads viewers in powerful healing prayers that will build your faith and help you receive the complete healing that God has in store for you. Whether you're dealing with a chronic illness, an injury, or simply a need for physical renewal, this video will provide you with the spiritual tools you need to experience God's miraculous touch in your life.

Don't miss this opportunity to dive deeper into the transformative power of Holy Communion. Subscribe to our channel for more life-changing sermons from Joseph Prince, and join a growing community of believers who are experiencing the fullness of God's healing grace in their lives. Watch now and let the healing power of Jesus flow through you!

Key Takeaways:

-Holy Communion is a powerful act of faith that can bring healing.
-Jesus' sacrifice provided a righteous foundation for our healing.
-We can approach God with confidence for our healing needs.

FAQ:

Q: How often should I take Holy Communion for healing?
A: You can take Holy Communion as often as you feel led by the Holy Spirit. Many believers partake daily, weekly, or monthly.

Q: Can I receive healing even if I feel unworthy?
A: Yes! Your healing is based on Jesus' finished work, not your own worthiness.

Q: What should I do if I don't see immediate results?
A: Continue to trust in God's promises and partake in Holy Communion with faith. Healing is a process, and God's timing is perfect.

Q: Can I take Holy Communion on my own, or do I need to be in church?
A: You can take Holy Communion wherever you are, as long as you do so with reverence and faith in Jesus.

Q: How can I learn more about divine healing?
A: Watch more of Joseph Prince's sermons and many other faith-based programs that inspire and inform on www.tbnplus.com.

Stay updated with TBN by:
-Signing up to receive e-newsletters: https://www.tbn.org/newsletters
-Signing up for TBN+ : https://www.tbn.org/apps

#tbn #JosephPrince #DivineHealing #HolyCommunion

Google’s AI Course for Beginners (in 10 minutes)! 121gamers 2 Tampilan • 2 bertahun-tahun yang lalu

Grab my free Workspace Toolkit: https://academy.jeffsu.org/workspace-toolkit?utm_source=youtube&utm_medium=video&utm_campaign=146

Try Gemini yourself: g.co/g1referral/9V6A6K8N

🔍 In this video, we unravel the layers of AI, Machine Learning, Deep Learning, and their applications in tools like #ChatGPT and Google #Bard

We first go through how AI is a broad field of study that encompasses #MachineLearning as a sub-field.

We then break down Machine Learning into supervised and unsupervised models, using real-world examples to illustrate their functions and differences.

We move deeper into Deep Learning: Learn about artificial neural networks and the power of semi-supervised learning in applications like fraud detection in banking.

Then we delve into Generative AI, differentiating it from discriminative models and demonstrating its capabilities in creating new, innovative outputs.

Finally we walk through Large Language Models (LLMs) and uncover the significance of LLMs in AI, their pre-training processes, and their customization for specific industry applications

*TIMESTAMPS*
00:00 Google’s AI Course in 10 Minutes
00:38 What is Artificial Intelligence?
01:27 What is Machine Learning?
03:28 What is Deep Learning?
05:15 What is Generative AI?
07:05 What are Large Language Models?

*RESOURCES I MENTION IN THE VIDEO*
Google’s full course: https://www.cloudskillsboost.g....oogle/course_templat
Grab my free Workspace Toolkit: https://academy.jeffsu.org/workspace-toolkit?utm_source=youtube&utm_medium=video&utm_campaign=146

*MY FAVORITE GEAR*
🎬 My YouTube Gear - https://www.jeffsu.org/yt-gear/
🎒 Everyday Carry - https://www.jeffsu.org/my-edc/

*MY TOP 3 FAVORITE SOFTWARE*
❎ CleanShot X - https://geni.us/cleanshotx
✍️ Skillshare - https://geni.us/skillshare-jeff
📖 Readwise - https://readwise.io/jeffsu/

*BE MY FRIEND:*
📧 Subscribe to my Productivity newsletter - https://www.jeffsu.org/productivity-ping/
📸 Instagram - https://instagram.com/j.sushie
🤝 LinkedIn - https://www.linkedin.com/in/jsu05/

*👨🏻‍💻 WHO AM I:*
I'm Jeff, a tech professional trying to figure life out. What I do end up figuring out, I share!

_PS: Some of the links in this description are affiliate links I get a kickback from and my opinions are my own and may not reflect that of my employer_ 😇

Let's build GPT: from scratch, in code, spelled out. 121gamers 2 Tampilan • 2 bertahun-tahun yang lalu

We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3. We talk about connections to ChatGPT, which has taken the world by storm. We watch GitHub Copilot, itself a GPT, help us write a GPT (meta :D!) . I recommend people watch the earlier makemore videos to get comfortable with the autoregressive language modeling framework and basics of tensors and PyTorch nn, which we take for granted in this video.

Links:
- Google colab for the video: https://colab.research.google.....com/drive/1JMLa53HDu
- GitHub repo for the video: https://github.com/karpathy/ng-video-lecture
- Playlist of the whole Zero to Hero series so far: https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ
- nanoGPT repo: https://github.com/karpathy/nanoGPT
- my website: https://karpathy.ai
- my twitter: https://twitter.com/karpathy
- our Discord channel: https://discord.gg/3zy8kqD9Cp

Supplementary links:
- Attention is All You Need paper: https://arxiv.org/abs/1706.03762
- OpenAI GPT-3 paper: https://arxiv.org/abs/2005.14165
- OpenAI ChatGPT blog post: https://openai.com/blog/chatgpt/
- The GPU I'm training the model on is from Lambda GPU Cloud, I think the best and easiest way to spin up an on-demand GPU instance in the cloud that you can ssh to: https://lambdalabs.com . If you prefer to work in notebooks, I think the easiest path today is Google Colab.

Suggested exercises:
- EX1: The n-dimensional tensor mastery challenge: Combine the `Head` and `MultiHeadAttention` into one class that processes all the heads in parallel, treating the heads as another batch dimension (answer is in nanoGPT).
- EX2: Train the GPT on your own dataset of choice! What other data could be fun to blabber on about? (A fun advanced suggestion if you like: train a GPT to do addition of two numbers, i.e. a+b=c. You may find it helpful to predict the digits of c in reverse order, as the typical addition algorithm (that you're hoping it learns) would proceed right to left too. You may want to modify the data loader to simply serve random problems and skip the generation of train.bin, val.bin. You may want to mask out the loss at the input positions of a+b that just specify the problem using y=-1 in the targets (see CrossEntropyLoss ignore_index). Does your Transformer learn to add? Once you have this, swole doge project: build a calculator clone in GPT, for all of +-*/. Not an easy problem. You may need Chain of Thought traces.)
- EX3: Find a dataset that is very large, so large that you can't see a gap between train and val loss. Pretrain the transformer on this data, then initialize with that model and finetune it on tiny shakespeare with a smaller number of steps and lower learning rate. Can you obtain a lower validation loss by the use of pretraining?
- EX4: Read some transformer papers and implement one additional feature or change that people seem to use. Does it improve the performance of your GPT?

Chapters:
00:00:00 intro: ChatGPT, Transformers, nanoGPT, Shakespeare
baseline language modeling, code setup
00:07:52 reading and exploring the data
00:09:28 tokenization, train/val split
00:14:27 data loader: batches of chunks of data
00:22:11 simplest baseline: bigram language model, loss, generation
00:34:53 training the bigram model
00:38:00 port our code to a script
Building the "self-attention"
00:42:13 version 1: averaging past context with for loops, the weakest form of aggregation
00:47:11 the trick in self-attention: matrix multiply as weighted aggregation
00:51:54 version 2: using matrix multiply
00:54:42 version 3: adding softmax
00:58:26 minor code cleanup
01:00:18 positional encoding
01:02:00 THE CRUX OF THE VIDEO: version 4: self-attention
01:11:38 note 1: attention as communication
01:12:46 note 2: attention has no notion of space, operates over sets
01:13:40 note 3: there is no communication across batch dimension
01:14:14 note 4: encoder blocks vs. decoder blocks
01:15:39 note 5: attention vs. self-attention vs. cross-attention
01:16:56 note 6: "scaled" self-attention. why divide by sqrt(head_size)
Building the Transformer
01:19:11 inserting a single self-attention block to our network
01:21:59 multi-headed self-attention
01:24:25 feedforward layers of transformer block
01:26:48 residual connections
01:32:51 layernorm (and its relationship to our previous batchnorm)
01:37:49 scaling up the model! creating a few variables. adding dropout
Notes on Transformer
01:42:39 encoder vs. decoder vs. both (?) Transformers
01:46:22 super quick walkthrough of nanoGPT, batched multi-headed self-attention
01:48:53 back to ChatGPT, GPT-3, pretraining vs. finetuning, RLHF
01:54:32 conclusions

Corrections:
00:57:00 Oops "tokens from the _future_ cannot communicate", not "past". Sorry! :)
01:20:05 Oops I should be using the head_size for the normalization, not C